Next Article in Journal
Generating High-Quality Panorama by View Synthesis Based on Optical Flow Estimation
Previous Article in Journal
Pose Determination of the Disc Cutter Holder of Shield Machine Based on Monocular Vision
 
 
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Review

Smart Electrically Assisted Bicycles as Health Monitoring Systems: A Review

by
Eli Gabriel Avina-Bravo
1,*,
Johan Cassirame
2,3,4,
Christophe Escriba
1,
Pascal Acco
1,
Jean-Yves Fourniols
1 and
Georges Soto-Romero
1
1
Laboratory for Analysis and Architecture of Systems (LAAS), University of Toulouse, F-31077 Toulouse, France
2
EA4660, Culture, Sport, Health and Society Department and Exercise Performance, University of Bourgogne-France Comté, 25000 Besançon, France
3
EA7507, Laboratoire Performance Santé Métrologie Société, 51100 Reims, France
4
Société Mtraining, R&D Division, 25480 Ecole Valentin, France
*
Author to whom correspondence should be addressed.
Sensors 2022, 22(2), 468; https://doi.org/10.3390/s22020468
Submission received: 18 November 2021 / Revised: 24 December 2021 / Accepted: 5 January 2022 / Published: 8 January 2022
(This article belongs to the Section Internet of Things)

Abstract

:
This paper aims to provide a review of the electrically assisted bicycles (also known as e-bikes) used for recovery of the rider’s physical and physiological information, monitoring of their health state, and adjusting the “medical” assistance accordingly. E-bikes have proven to be an excellent way to do physical activity while commuting, thus improving the user’s health and reducing air pollutant emissions. Such devices can also be seen as the first step to help unhealthy sedentary people to start exercising with reduced strain. Based on this analysis, the need to have e-bikes with artificial intelligence (AI) systems that recover and processe a large amount of data is discussed in depth. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines were used to complete the relevant papers’ search and selection in this systematic review.

1. Introduction

In recent years, electrically assisted bikes have become more popular as a way to achieve active mobility or for recreation. In the United States, sales increased by 145% from 2019 to 2020, surpassing regular bikes with a 65% growth rate [1]. In Europe, the market increases by 23% each year, and it is estimated that by 2030 there will be twice as many e-bikes as cars [2]. It is well known that e-bikes are an excellent way to engage in physical activity, allowing users to travel on roads that might be too demanding (with challenging up-hill segments). They also reduce the user’s commuting time and carbon footprint [3,4]. Even more now, with the COVID-19 pandemic, people avoid using public transportation (because of social distancing) for their travels [5].
In the literature many articles evaluate the potential of electric bikes as an effective active mobility tool that can aid many to meet their physical activity guidelines. Bourne et al. [6] compared several studies showing the e-bike health potential by analyzing physical activity outcomes between regular bikes and walking. Besides, they made several comparative effort tests using no electric assistance, low and high assistance, and traveling on different types of roads (e.g., flat, hilly routes). As a result, even though the amount of exercise performed was smaller than amounts performed on a regular bike, it is still a better alternative to cars and public transportation; the lower perception effort could incentivize more people to use e-bikes as their daily means of transportation.
The World Health Organization (WHO) recommends a minimum of 150 min of moderate-intensity physical activity or 75 min of vigorous-intensity, which leads to numerous benefits and prevents chronic diseases [7].
Chronic diseases are major causes of death in the world (see Figure 1); WHO [8] has reported that seven out of these ten causes of death are from chronic (non-communicable) diseases, and are responsible for 44% of deaths. All chronic diseases together are responsible for 74% of deaths globally in 2019. Smoking, inadequate nutrition, lack of physical activity, and alcohol are the leading causes of cardiovascular diseases, cancer, chronic respiratory disease, and diabetes [9,10].
Booth et al. [12] examined 35 chronic diseases and their relationship to physical inactivity; they clarify the usage of physical activity, prevention of disease being one, and it is primarily used as a means to avoid physical inactivity, which is the direct cause of certain diseases. Physical activity is used as a treatment principally to diminish the side effects of pathologies. Pedersen and Saltin [13] recompiled the impact of physical activity as a therapy in 26 chronic diseases, in which for each disease they detailed the background, evidence-based physical training, possible mechanism, type of training and contraindications. Lee et al. [14] concluded that over the years the life expectancy of the physically active would increase by 0.68 years. Meanwhile, if inactive individuals started performing an activity, they would gain from 1.3 to 3.7 years (in the United States). This is also noticeable in Asia, where active people live 2.6 to 4.2 years longer than inactive ones.
Such health facts denote the importance of having intelligent electric bikes that could supervise their riders by collecting physiological, mechanical, and environmental data. This data helps with making some optimal decisions, such as establishing adequate electrical assistance, battery management, and sending information to peers (e.g., research team, medical staff, and coaches). Considering the above-mentioned, this review compares the existing systems that monitor the user’s physiological signals and analyzes the data in order to control the bike. In addition, this study discusses the impacts on health that the existing systems could have on users with reduced mobility or pain difficulties (chronic diseases, knee and ankle procedures, sedentary habits).

2. Materials and Methods

2.1. Search Procedure

This review was done under the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, Liberati et al. [15]. The following databases were consulted in March 2021: Web of Science, PubMed, IEEE, Science Direct, Google Scholar, and HAL (France open archives), ending the search task in June 2021. The keywords used to consult each database, including their synonyms and combinations, were: e-bike, electrically assisted bike, electrically assisted bicycle, health, and monitoring systems. Thus, additional filters focused this search of multi-sensing e-bikes and their application within the medical world (e.g., in Web of Science Categories: ENGINEERING ELECTRICALELECTRONIC OR MEDICINE GENERAL INTERNAL; on Science Direct’s publications titles: Medical Engineering & Physics or Procedia Engineering).

2.2. Inclusion Criteria

Some articles addressed the fundamental aspects of an electric bike (see Figure 2), such as motor control (i.e., field-oriented [16,17], fuzzy logic [18,19,20], firefly algorithm [21], particle swarm optimization [22], model predictive [23], reinforcement learning [24], and others [25,26,27,28,29,30,31]) using different inputs, such as torque (human and machine), power and speed to control the bike’s motor. Studies on battery management system (BMS) and energy recovery [18,23,31,32,33,34], explored methods of supervising and charging the batteries used by the bikes (State of Health and State of Charges) and also explored the possibility of recovering energy by braking and in downhill situations. These systems need to work alongside the motor controllers to extend the battery at disposal and extend the rides. These aforementioned research fields were not included in this review because of the lack of a focus on sport and health. Furthermore, transportation papers and comparisons between regular bikes and e-bikes were also excluded, since they evaluate the e-bike’s potential benefits [35,36,37,38], while lacking the electronics and control integration. Some papers proposed communication methods using Internet of Things (IoT) protocols such as LoRa and XBee, as seen on Ambika bhuvaneswari and Muthumari [39] and Gharghan et al. [40]. The former presented an algorithm to reduce the energy consumption in a WSN (Wireless Sensor Network). Depending on the bike’s speed change, the algorithm augments or diminishes the data transmission. These articles have fundamental importance since they can reduce communication energy consumption, but they were considered “off topic“ for this review. Meanwhile, eco-mobility, air quality (measuring the fine particles in the air while commuting) and road optimization (choosing the best path depending on several factor such as distance, elevation, air quality, time, remaining battery, among others) papers were taken into consideration only if the proposed systems took into account the rider’s health in addition to the reduction in carbon footprint elements.

3. Results

The search provided 199 articles which were narrowed down to 114 after the elimination of duplicates. Eighty-six articles did not meet the inclusion criteria after titles, abstracts, and full-text readings. Thirty-four articles were not kept because they focused only on the electronic control units or battery management systems. Fifty-seven papers only examined asset the health and transportation aspects, and were, therefore, also excluded. The remaining 23 articles were considered.

3.1. Sensors

When discussing the sensors typically used on a bike, Global Positioning System (GPS), cadence in revolutions per minute [RPM], speed in kilometers per hour or mile per hour [Km/h, Mi/h], power in watts [W], and heart rate in beats per minute [BPM], are the first to be considered. BPM has been of the highest importance for health-focused studies because it is the primary indicator of exercise intensity [41,42,43] and is used to monitor diseases [44,45,46]. The other above-mentioned sensors are used as performance indicators, commonly used by professional athletes and amateurs to assist them in achieving their fitness or competitive goals. Additional sensors can be (and are) added to increase the amounts of environmental and physiological data, complement algorithms, or give more information to the user about its state and surroundings. Table 1 shows the sensors that are used the most often in the literature. As expected, heart rate is essential in this field, and almost every paper uses it to supervise the tester’s condition and adjust the algorithms in response. The selected papers used different technologies to measure the heart rate, such as Electrocardiogram (ECG), pulse oximeter (which also measures oxygen saturation), pulse sensor, chest belt, and self-made sensors.
Barrios et al. [70] compared a standard ECG with off-the-shelf wrist and armbands, concluding that wristbands were the most biased due to external interference. Etiwy et al. [71] showed that the chest belt monitor was in the strongest agreement with the ECG, significantly more so than the wrist band, whose accuracy varied depending on the type of exercise. Both articles have inferred that ECG is the preferred heart rate measuring method and should be used if possible. However, the downside of ECG is that it is not practical for outdoor use (patient comfort), which is where most of the subjects would perform tests (in real-life scenarios). The second and third most commonly used sensors measure power and cadence (or speed, since they can be deduced from each other). They are used in almost every motor control system since they retrieve the cyclist’s power or torque produced and their rate. This information correlated that the heart rate can be used to estimate the state of fatigue of the cyclist [52,53,57,65,68] as well as the road difficulty to assist the user in the best manner and reduce strain during their travels. GPS and accelerometer data are mainly used for road classification as slope recognition (flat, uphill, and downhill) and road segmentation [72,73], however they can be used in other applications (e.g., fall detection [74,75,76]).

3.2. Architectures

When it comes to the architecture of a smart electrical assisted bike, a computer is needed to recover and process the sensors’ data. Throughout the literature, systems may use one or several computers. Additional computers are used to complement the functionalities that one computer lacks or to increase the processing stages and augment computational power. Typically, the computers used on these systems are smartphones, servers, personal computers, and of course, microcontrollers. From Table 2, it can be inferred that microcontrollers are essential since they allow for the faster integration of sensors unable to communicate over the air, using protocols like BLE, WIFI, ZigBee, and Sigfox, to name a few. Microcontrollers also directly control and monitor the battery and the motors.
Nowadays, most people own a smartphone which can be exploited as a human machine interface (HMI). With these devices, users can parameter the algorithm and have visual updates of their physical activity in real-time [53,55,57,58,77]. It is possible to process the information and aid the controller in providing a better response, or transmit the collected data to a server or personal computer (PC) for further analysis [59,63,64,65,66,67]. Tablets could perform these functionalities but are limited by their size, which can be a nuisance on a bike. Moreover, some tablets don’t have an integrated GSM communication, or require the user to pay a subscription fee in order to use it.
Uploading data in real-time to a server is mainly used to retrieve, process, and analyze data, providing feedback to users [53,59,60,64,65,69] and allowing the health professionals to potentially monitor their patients remotely and adjust the algorithm (i.e., surveillance thresholds, training workload).
A significant majority of the studies included a smartphone as an HMI, which is essential for a sportive individual who wants to keep track of their data and post-activity recapitulation, or for a regular user who wants to travel from point A to point B. An interesting point is the potential of the HMI to personalize the bike, so that it responds accordingly to each rider, allowing them to introduce their profile (physiological characteristics) and desired training levels (e.g., time, distance, power). With an internet connection, the data can be reported on a corresponding website (server) so that healthcare professionals, coaches and individuals can visualize and follow activities, and adjust the workload.

3.3. Study Characteristics

Fifteen studies, reported in Table 3, have assessed their systems on 246 people. Higashi et al. [47], Ueno et al. [49] and De La Iglesia et al. [65] included in their studies a range of people between the ages of 20 to 30. Kiryu and Minagawa [52], De La Iglesia et al. [59] and Rey-Barth et al. [69] extended their studies to people in their sixties. On average, the studies included at least two testers, with the exception of [47,52,65,69] which included more than 9 subjects and [59] which included almost 190. The on-field tests were mostly comprised of flat and uphill segments, which had to be repeated multiple times while respecting specific test conditions such as keeping the same speed and/or cadence at all times. De La Iglesia et al. [59,65], Rey-Barth et al. [69] were the only ones that reported the duration of their studies, demonstrating the long-term impacts of these systems.

4. Discussion

As seen in Table 1, it is noticeable which sensors are used with electric bikes to transform them into health monitoring systems. Fundamentally, we can separate these sensors into three categories: physiological, neuromuscular, and environmental sensors.
Physiological sensors are more relevant for this purpose because they can investigate the physiological status of people during the ride and potentially estimate fatigue induced by exercise intensity and duration [78,79,80,81]. From a technical point of view, many sensors, that provide information related to the physiological values of the rider are available on the market [82]. Nevertheless, it is essential to consider the measurement process itself in order to maximize the user’s comfort level during the riding experience without disturbances that might lead to the loss of the electric assistance’s benefit. Secondly, it’s imperative to select values with the necessary background knowledge during the exercise to build an appropriate algorithm to model data or control loops to drive electric assistance.
The presented review pointed out that only heart rate (22/24 studies) and blood oxygen saturation (5/24 studies) were used for this purpose.
Heart rate was predominantly used for this application because of a large number of studies linking this parameter with health status and sport science experience legacy [78,79,80,83,84,85]. From a physiological aspect, heart rate is primarily used for its relationship with oxygen consumption (VO2), which is widely accepted as the gold standard to set exercise intensity. Based on Fick’s principle [86], cardiac output can be related to VO2, neglecting systolic ejection volume as demonstrated by Zhou et al. [87] with an elite athlete, trained, and untrained subject.
Based on that, several studies proposed a control model-based heart rate measurement using a preliminary test in a laboratory in order to get a better understanding of individual physiological states, using the heart rate and power outputs produced by the rider [57,68,88]; or used more basic methods such as the sliding mode controller [56].
The authors did not systematically describe the heart rate measurement process with sensor typology and location during the reviewed studies. Meyer et al. [88] used a chest strap in their study (Tickr; Wahoo Fitness, LLC, Atlanta, United States) to monitor heart rate during exercise, and we imagine that other studies used the same process because the chest belt is widely considered as the gold standard to measure heart rate accurately in numerous situations [71,89]. Recently, many new technical possibilities to monitor heart rate in non-invasive ways have appeared. They provide the most comfortable experience for people and have the potential for discrete integration in HMI [90]. Regarding e-bikes, few of those new possibilities are technically viable for implementation in a real ecological situation. In the last decade, many commercial heart rate monitors have implemented photoplethysmography technology as an alternative to traditional electrocardiography requiring chest belts, which may lead to discomfort or technical constraint for women, older, or obese people. This technique calculates heart rate by measuring micro blood flow oscillations in the skin. The working principle is based on the reflection of light passing through the skin [91,92]. Even if the technical aspect is promising, the actual accuracy of the commercial and experimental sensors is not sufficient for use with electrical assistance for healthy individuals or people with illnesses [71,89,93,94]. In his review for automotive sensors, Arakawa [90] describes the instrumentation potential of a steering wheel with bioelectrical measurement sensors that used the hands in order to detect a heartbeat. A similar system could be transposed on to the handlebar to eliminate third-party accessories, and measure the heart rate directly on a bike. Such sensors can be connected directly to an HMI, and an appropriate algorithm can be implemented to calculate the heart rate analogically, for example with other fitness equipment.
A consideration of heart rate measurement techniques is crucial for the future and possible implementation of heart rate sensors with more complex calculations as heart rate variability helps to estimate exercise intensity [95,96,97] for which accuracy is essential [98]. In addition, this potential implementation may lead to gaining an estimation of other different physiological variables such as breathing frequency [99], VO2 [100], and EPOC (Excess Post-Exercise Oxygen Consumption) [101]. Having this data will eventually result in more complex and dynamic models to control electrical assistance.
This review also points out that blood oxygen saturation may be used to control the stress caused by exercise [53,54] and also by environmental factors such as pollution [66]. Blood oxygen saturation is commonly used in the medical field to supervise patients in intensive care units and during the cardiac or pulmonary rehabilitation process [102]. This parameter provides more a profound insight into the individual’s physiological reaction during exercise. Nevertheless, blood oxygen saturation is obtained by an optical measurement that is more complicated to obtain than the heart rate’s. Traditionally, this optical measurement is performed at the finger but can also be measured at the ear, tympanic, or at several muscular locations [103]. This measurement is significantly affected by light and requires a light shield at the measurement location such as a finger hood or a large ear clip. For these reasons, it is not very easy to use these measurements in ecological tests, but depending on their evolution, these technologies could become an essential alternative for such measurements. Moreover, traditional finger measurement is affected by muscular contraction of the forearms and fingers when gripping the handlebar, controlling the bike trajectory, managing the braking system, and changing gear. As for the heart rate measurement, recent technical solutions have appeared and allow for the measurement of this variable with fewer constraints [93,104]. Several commercial wrist-worn products measure pulse oximetry using photoplethysmography while maintaining an affordable cost. Few researchers have investigated the accuracy of such devices for dynamic cycling, but recent studies from Schiefer et al. [105] and Hermand et al. [106] invite a careful consideration of these devices, especially when the pulse oximetry variable is of interest.
Today, it seems clear that heart rate remains the most practical solution for ecological utilization, and that more advanced devices would help get better algorithms for research purposes, but are not transferable in an actual application. As a gold standard, VO2 may be more used in the study of validation or proof of concept to get a clearer understanding of the physiological considerations and avoid heart rate shortcuts that may miss critical body reactions during specific field conditions. Gas exchange measurement with VO2 and VCO2 is widely used in the medical and sport science fields. This exam is accepted as the best indicator for determining the capacity of the heart, lungs, vessels, and muscles to effectively use oxygen during exercise ([107], pp. 1–3). This data could be collected from initial trials where subjects are given instructions on how to use the bike and its systems, how often it should be used, and on which path (if it is a controlled study). This would adequately customize the system to establish a rider’s intensity zone and evaluate if the subject should remain in that zone or change. Nagata et al. [51] and Sweeney et al. [66] measured VO2 and used it to supervise its evolution during a test, concluding that their system can prevent the user from crossing the second ventilatory threshold.
The actual state of the art for physiological assessments requires an oronasal face mask and gas exchange measurement system, which can be stationary and used in the laboratory, or be portable to evaluate people in ambulatory tests (actual field situations). It is of interest to maximize the utilization of VO2 measurement during such studies in order to upgrade the algorithms and increase the quality of metabolic predictions [108,109,110].
Regarding physiological measurements, we note that near infrared spectrometry is now widely used to grade physiological status [111] and may become more accurate and sensitive than heart rate in an ecological situations [112,113]. Today, these sensors are not ready to be integrated in the dynamic control of e-bikes, but may be good candidates in future works and add more relevant physiological information on leg muscles. This may give new insight on a potential control loop for electrical assistance based on local physiological markers on legs that could potentially exclude heart rate fluctuations related to side factors such as stress, temperature, caffeine, or others.
From general knowledge of the human body, it is well known that each person has their individual capabilities in both force and speed [114,115], leading to natural preferences during movements [116,117]. Moreover, the literature is well documented about the impact of strength limitations in cycling exercise within several populations, especially among those with diseases [118,119]. Based on this data, there may be a potential benefit for algorithms that adjust electrical assistance based on individual physiological and muscular (biomechanical) capacities in order to maintain people in their comfort zone.
In the same direction, the usage of electromyography (EMG) should be considered to assess the rider’s muscle fatigue during different terrain tests, i.e., assess the amount of time needed before fatigue is apparent on a flat, uphill, and mixed road, as seen in Kiryu and Minagawa [52]. Naturally, testing all the road combinations would take an enormous amount of time. However, this could help predict when and where the rider would need more assistance; helping to conserve the battery until it is needed. Furthermore, such tests can provide insights on potential muscular improvements for people who are in leg rehabilitation [120,121,122] ([123], p. 785).
A correlation must be determined using other physiological inputs (i.e., heart rate, cadence, and power) and muscular fatigue since it is not possible to have an EMG on the subject at all times when engaged in an outdoor session. However, this fatigue can be estimated using other inputs, as has been accomplished in works such as Corno et al. [57] and Meyer et al. [68]. Alternatively, even if this parameter can obtain relevant information to adjust the algorithm’s individualization accurately, it is essential to remember that surface muscular activation is not directly related to the force production ([124], p. 7). Moreover, the hardware setup and the experience of technicians need to be top tier to carefully place EMG sensors and manage the data. This typology of measurement can be possible in the laboratory. So far, few studies in the literature suggest that such devices can accurately and reliably measure the force or exercise induced fatigue [125,126].
One side effect of using an electric bike is that, while reducing the carbon footprint, it causes the cyclist to breathe polluted air, and this is noticeable in big cities ([127,128,129,130]) Sweeney et al. [66] and Venkatanarayanan et al. [67] proposed interesting ideas to control the respiration rate and measure air quality, respectively. Both are used to reduce the inhalation of traffic pollution, which has proven to have a greater negative impact on cyclists than people using other methods of transportation [131]. This is especially important to consider when the rider has respiratory pathologies such as asthma or even lung cancer. Caggiani et al. [132] addressed this issue by proposing an algorithm that can optimize the route depending on the user’s desires (i.e., travel time, safety, air pollution). Implementing this on an electrically assisted bike could help to protect the cyclist even further when commuting or doing exercise, and at the same time, collect data which can be used by local authorities and environmental groups to manage the air quality in cities and their surroundings, as proposed in works by Elen et al. [133], Liu et al. [134], Aguiari et al. [135], and Shen et al. [136].
The arrival of the 5G mobile networks will allow new technologies to surface, referring to the “5G triangle”, smart electrical bikes, in general, could fit in the mMTC (massive Machine Type Communication) use case. In a city with connected vehicles and city infrastructure, it is plausible to have bikes connected to that network as well, predicting the best route (considering traffic, bike lanes availability, air quality), and alerting other vehicles nearby, thus preventing more accidents. Meanwhile, environmental sensors can be recovered from the bike to be sent to a central hub, where this data could help improve weather prediction, air quality, and traffic reports. Health data recovered could be sent immediately, allowing health professionals to supervise their patients while doing rehabilitation, alerting them when it is necessary. Models used to predict health population could receive a boost by recollecting these on-field data, by analyzing the body’s response during an activity, providing new insights into how people with and without diseases perform, providing opportunities to prevent diseases by comparing these cases, and acting accordingly to improve the population’s health [137,138].
Good ergonomics are crucial to avoid injuries while using a bike. Bini et al. [139,140], Swart and Holliday [141] reviewed several works for saddle optimization intending to reduce injuries, concluding that a knee angle between 25° and 35° using the Holmes et al. [142] method is the most often used to reduce injuries. Nevertheless, further research is required to develop a thorough method that considers different saddle and handlebar positions, workload (watts, speed, cadence), and aerodynamics (sports performance). These new methods need to be tested in the field and on a static bike; position sensors can be placed on electric bikes and cyclists [143]. The information obtained can be compared in the field using different bike configurations so that pedaling techniques and riding styles can be identified and trained, as seen in Korff et al. [144] and Lucchetti et al. [145].

5. Conclusions

Electrically assisted bikes applied to the sport and health sector is a growing research field (exponentially growing in publications and number of citation) with direct and real applications that could change the perception of active mobility, and encourage many to start exercising progressively within their displacements, especially now that we are in a worldwide pandemic and people are reluctant to use public transportation.
Telemonitoring could be possible with every use; doctors and physicians could prescribe usage of the e-bike for the prevention of diseases like heart disease, strokes, diabetes, cancers, dementia, depression, and anxiety [13,146]. Using a web server or an app, health care professionals could connect to it and supervise patients at risk, following their activity and possibly having active communication with them, such as sending warning messages, increasing electric assistance, and changing the medical prescription (e.g., heart rate surveillance thresholds).
Test protocols for these kinds of devices are not yet properly established, which explains the discrepancies in the literature on the monitoring of physiological information and determining its purpose (i.e., pathology monitoring, physical therapy, active mobility, sports). Close collaboration with medical staff is imperative, so that the devices are validated both technological and clinically.
Nowadays, microcontrollers can integrate machine and deep learning libraries, al-lowing for more complex algorithms that can adapt depending on who is using the bike, and learn from the physiological and environmental data to better adjust electric assistance, battery management, and travel optimization. Further research is needed to conclude if such an intelligent system could have a relevant impact on the sportive and medical areas as well as providing an eco-friendly method of transportation.

Author Contributions

Conceptualization, E.G.A.-B. and G.S.-R.; methodology, E.G.A.-B., C.E., P.A. and G.S.-R.; validation, E.G.A.-B., C.E., J.C., P.A., J.-Y.F. and G.S.-R.; investigation, E.G.A.-B.; data curation, E.G.A.-B. and G.S.-R.; writing—original draft preparation, E.G.A.-B.; writing—review and editing, E.G.A.-B., C.E., J.C., P.A., J.-Y.F. and G.S.-R.; supervision, G.S.-R.; All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by TnP Consultants Company (Paris, France), grant CIFRE 2020/0279.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Acknowledgments

The authors would like to express appreciation for the support of TnP Consultants Company (Paris, France), which manages the Ph.D. grant CIFRE 2020/0279.

Conflicts of Interest

The authors declare no conflict of interest.

Abbreviations

The following abbreviations are used in this manuscript:
BMIBody Mass Index
BPMBeat Per Minute
RPMRevolution Per Minute
ECGElectrocardiogram
GPSGlobal Positioning System
HMIHuman Machine Interface
MDPIMultidisciplinary Digital Publishing Institute
SOFState of Fatigue
PCPersonal Computer

References

  1. Glusac, E. Farther, Faster and No Sweat: Bike-Sharing and the E-Bike Boom. New York Times. 2 March 2021. Available online: https://www.nytimes.com/2021/03/02/travel/ebikes-bike-sharing-us.html (accessed on 27 September 2021).
  2. Reid, C. E-Bike Sales to Grow from 3.7 Million to 17 Million Per Year by 2030, Forecast Industry Experts. Available online: https://www.forbes.com/sites/carltonreid/2020/12/02/e-bike-sales-to-grow-from-37-million-to-17-million-per-year-by-2030-forecast-industry-experts/ (accessed on 27 September 2021).
  3. Heinen, E.; van Wee, B.; Maat, K. Commuting by Bicycle: An Overview of the Literature. Transp. Rev. 2010, 30, 59–96. [Google Scholar] [CrossRef]
  4. Fishman, E.; Cherry, C. E-bikes in the Mainstream: Reviewing a Decade of Research. Transp. Rev. 2016, 36, 72–91. [Google Scholar] [CrossRef]
  5. De Vos, J. The effect of COVID-19 and subsequent social distancing on travel behavior. Transp. Res. Interdiscip. Perspect. 2020, 5, 100121. [Google Scholar] [CrossRef]
  6. Bourne, J.E.; Sauchelli, S.; Perry, R.; Page, A.; Leary, S.; England, C.; Cooper, A.R. Health benefits of electrically-assisted cycling: A systematic review. Int. J. Behav. Nutr. Phys. Act. 2018, 15, 116. [Google Scholar] [CrossRef]
  7. World Health Organization. Physical Activity. Available online: https://www.who.int/news-room/fact-sheets/detail/physical-activity (accessed on 28 September 2021).
  8. World Health Organization. Non Communicable Diseases. Available online: https://www.who.int/news-room/fact-sheets/detail/noncommunicable-diseases (accessed on 24 September 2021).
  9. Australian Government Department of Health Managing Chronic Conditions. Available online: https://www.health.gov.au/health-topics/chronic-conditions/managing-chronic-conditions (accessed on 24 September 2021).
  10. Centers for Disease Control and Prevention About Chronic Diseases | CDC. Available online: https://www.cdc.gov/chronicdisease/about/index.htm (accessed on 24 September 2021).
  11. World Health Organization. The Top 10 Causes of Death. Available online: Used under a Creative Commons Attribution-ShareAlike License: CC BY-NC-SA 3.0 IGO. Available online: https://www.who.int/news-room/fact-sheets/detail/the-top-10-causes-of-death (accessed on 24 September 2021).
  12. Booth, F.W.; Roberts, C.K.; Laye, M.J. Lack of exercise is a major cause of chronic diseases. Compr. Physiol. 2012, 2, 1143–1211. [Google Scholar] [CrossRef] [Green Version]
  13. Pedersen, B.K.; Saltin, B. Exercise as medicine-evidence for prescribing exercise as therapy in 26 different chronic diseases. Scand. J. Med. Sci. Sports 2015, 25, 1–72. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  14. Lee, I.-M.; Shiroma, E.J.; Lobelo, F.; Puska, P.; Blair, S.N.; Katzmarzyk, P.T. Effect of physical inactivity on major non-communicable diseases worldwide: An analysis of burden of disease and life expectancy. Lancet 2012, 380, 219–229. [Google Scholar] [CrossRef] [Green Version]
  15. Liberati, A.; Altman, D.G.; Tetzlaff, J.; Mulrow, C.; Gotzsche, P.C.; Ioannidis, J.P.A.; Clarke, M.; Devereaux, P.J.; Kleijnen, J.; Moher, D. The PRISMA statement for reporting systematic reviews and meta-analyses of studies that evaluate healthcare interventions: Explanation and elaboration. BMJ 2009, 339, b2700. [Google Scholar] [CrossRef] [Green Version]
  16. Mohammadi-Abdar, H.; Ridgel, A.L.; Discenzo, F.M.; Loparo, K.A. Design and Development of a Smart Exercise Bike for Motor Rehabilitation in Individuals With Parkinson’s Disease. IEEE/ASME Trans. Mechatron. 2016, 21, 1650–1658. [Google Scholar] [CrossRef] [Green Version]
  17. Iosub, A.; Kirscher, J.; Rafaila, M.; Buzo, A.; Pelz, G.; Goras, L. Simulation-based approach to application fitness for an E-Bike. In Proceedings of the 2016 IEEE Sensors Applications Symposium (SAS), Catania, Italy, 20–22 April 2016. [Google Scholar]
  18. Giliberto, M.; Arena, F.; Pau, G. A fuzzy-based Solution for Optimized Management of Energy Consumption in e-bikes. J. Wirel. Mob. Netw. Ubiquitous Comput. Dependable Appl. 2019, 10, 45–64. [Google Scholar] [CrossRef]
  19. Lee, J.-S.; Jiang, J.-W.; Sun, Y.-H. Design and simulation of control systems for electric-assist bikes. In Proceedings of the 2016 IEEE 11th Conference on Industrial Electronics and Applications (ICIEA), Hefei, China, 5–7 June 2016; pp. 1736–1740. [Google Scholar]
  20. Guarisco, M.; Gao, F.; Paire, D. Autonomy and User Experience Enhancement Control of an Electrically Assisted Bicycle With Dual-Wheel Drive. IEEE Trans. Ind. Appl. 2017, 53, 1476–1484. [Google Scholar] [CrossRef]
  21. Anshory, I.; Hadidjaja, D.; Jakaria, R.B. Bldc Motor: Modeling And Optimization Speed Control Using Firefly Algorithm. Dinamik 2020, 25, 51–58. [Google Scholar] [CrossRef]
  22. Anshory, I.; Robandi, I. Monitoring and optimization of speed settings for Brushless Direct Current (BLDC) using Particle Swarm Optimization (PSO). In Proceedings of the 2016 IEEE Region 10 Symposium (TENSYMP), Bali, Indonesia, 9–11 May 2016; pp. 243–248. [Google Scholar]
  23. Bergmann, L.; Leonhardt, S.; Greven, D.; Misgeld, B.J.E. Optimal assistive control of a pedal-electric drive unit. Control. Eng. Pract. 2021, 110, 104765. [Google Scholar] [CrossRef]
  24. Hsu, R.C.; Liu, C.-T.; Lee, W.-M.; Chen, C.-H. A Reinforcement Learning Based Power Assisted Method with Comfort of Riding for Light Electric Vehicle. In Proceedings of the 2010 IEEE 71st Vehicular Technology Conference, Taipei, Taiwan, 16–19 May 2010; IEEE: Taipei, Taiwan, 2010; pp. 1–5. [Google Scholar]
  25. Abhilash, D.S.H.; Wani, I.; JoSeptemberh, K.; Jha, R.; Haneesh, K.M. Power Efficient e-Bike with Terrain Adaptive Intelligence. In Proceedings of the 2019 International Conference on Communication and Electronics Systems (ICCES), Coimbatore, India, 17–19 July 2019; pp. 1148–1153. [Google Scholar]
  26. Chen, P.-C.; Chuang, H.-S.; Hsiao, C.-C.; Chang, S.-B. The velocity control with disturbance estimation for the E-Bike systems. In Proceedings of the 2011 International Conference on System Science and Engineering, Macau, China, 8–10 June 2011; pp. 171–176. [Google Scholar]
  27. Dumitrache, F.; Carp, M.C.; Pana, G. E-bike electronic control unit. In Proceedings of the 2016 IEEE 22nd International Symposium for Design and Technology in Electronic Packaging (SIITME), Oradea, Romania, 20–23 October 2016; pp. 248–251. [Google Scholar]
  28. Hatano, R.; Namikawa, D.; Minagawa, R.; Iwase, M. Experimental verification of effectiveness on driving force assist control based on repetitive control for electrically-assisted bicycles. In Proceedings of the 2014 IEEE 13th International Workshop on Advanced Motion Control (AMC), Yokohama, Japan, 14–16 March 2014; pp. 237–241. [Google Scholar]
  29. Kai, Z.; Dejun, Y. A control approach adaptive to load and road slope for electric power assisted bicycle. In Proceedings of the 2017 36th Chinese Control Conference (CCC), Dalian, China, 26–28 July 2017; pp. 3414–3418. [Google Scholar]
  30. Chen, P.; Lin, H.; Chang, S.; Huang, Y. The torque control of human power assisted electric bikes. In Proceedings of the 2010 International Conference on System Science and Engineering, Taipei, Taiwan, 1–3 July 2010; pp. 373–378. [Google Scholar]
  31. Zaghari, B.; Stuikys, A.; Weddell, A.S.; Beeby, S. Efficient Energy Conversion in Electrically Assisted Bicycles Using a Switched Reluctance Machine Under Torque Control. IEEE Access 2020, 8, 202401–202411. [Google Scholar] [CrossRef]
  32. Ceuca, E.; Pocan, I.; Pop, O. Electromagnetic compatibility analysis of the assembly e-bike—Power electronic converter with recovery function. In Proceedings of the 2016 39th International Spring Seminar on Electronics Technology (ISSE), Pilsen, Czech Republic, 18–22 May 2016; pp. 398–401. [Google Scholar]
  33. Sreehari, M.D.; Manikandan, K.; Pranjith, K.; Adithya, V.; Ram, M.B.J.; Indu, V.; Dharmana, M.M. Design and Analysis of E-Bike with Electrical Regeneration and Self-Balancing Assist. In Proceedings of the 2020 4th International Conference on Trends in Electronics and Informatics (ICOEI)(48184), Tirunelveli, India, 15–17 June 2020; pp. 119–124. [Google Scholar]
  34. Maier, O.; Krause, M.; Krauth, S.; Langer, N.; Pascher, P.; Wrede, J. Potential benefit of regenerative braking on electric bicycles. In Proceedings of the 2016 IEEE International Conference on Advanced Intelligent Mechatronics (AIM), Banff, AB, Canada, 12–15 July 2016; pp. 1417–1423. [Google Scholar]
  35. Castro, A.; Gaupp-Berghausen, M.; Dons, E.; Standaert, A.; Laeremans, M.; Clark, A.; Anaya-Boig, E.; Cole-Hunter, T.; Avila-Palencia, I.; Rojas-Rueda, D.; et al. Physical activity of electric bicycle users compared to conventional bicycle users and non-cyclists: Insights based on health and transport data from an online survey in seven European cities. Transp. Res. Interdiscip. Perspect. 2019, 1, 100017. [Google Scholar] [CrossRef]
  36. Alessio, H.M.; Reiman, T.; Kemper, B.; von Carlowitz, W.; Bailer, A.J.; Timmerman, K.L. Metabolic and Cardiovascular Responses to a Simulated Commute on an E-Bike. Transl. J. ACSM 2021, 6, e000155. [Google Scholar] [CrossRef]
  37. Langford, B.C.; Cherry, C.R.; Bassett, D.R.; Fitzhugh, E.C.; Dhakal, N. Comparing physical activity of pedal-assist electric bikes with walking and conventional bicycles. J. Transp. Health 2017, 6, 463–473. [Google Scholar] [CrossRef]
  38. Hoj, T.H.; Bramwell, J.J.; Lister, C.; Grant, E.; Crookston, B.T.; Hall, C.; West, J.H. Increasing Active Transportation Through E-Bike Use: Pilot Study Comparing the Health Benefits, Attitudes, and Beliefs Surrounding E-Bikes and Conventional Bikes. JMIR Public Health Surveill. 2018, 4, e10461. [Google Scholar] [CrossRef]
  39. Ambika Bhuvaneswari, C.; Muthumari, M. Design and realization of radio communication using LoRa & XBee module for an e-Bike. In Proceedings of the 2018 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC), Madurai, India, 13–15 December 2018; IEEE: Madurai, India, 2018; pp. 1–4. [Google Scholar]
  40. Gharghan, S.; Nordin, R.; Ismail, M. Energy-Efficient ZigBee-Based Wireless Sensor Network for Track Bicycle Performance Monitoring. Sensors 2014, 14, 15573–15592. [Google Scholar] [CrossRef]
  41. Ekelund, U.; Poortvliet, E.; Yngve, A.; Hurtig-Wennlöv, A.; Nilsson, A.; Sjöström, M. Heart rate as an indicator of the intensity of physical activity in human adolescents. Eur. J. Appl. Physiol. 2001, 85, 244–249. [Google Scholar] [CrossRef]
  42. Jeukendrup, A.; Diemen, A.V. Heart rate monitoring during training and competition in cyclists. J. Sports Sci. 1998, 16, 91–99. [Google Scholar] [CrossRef]
  43. Grant, C.C.; Murray, C.; Janse van Rensburg, D.C.; Fletcher, L. A comparison between heart rate and heart rate variability as indicators of cardiac health and fitness. Front. Physiol. 2013, 4, 337. [Google Scholar] [CrossRef] [Green Version]
  44. Oliva, F.; Sormani, P.; Contri, R.; Campana, C.; Carubelli, V.; Cirò, A.; Morandi, F.; Di Tano, G.; Mortara, A.; Senni, M.; et al. Heart rate as a prognostic marker and therapeutic target in acute and chronic heart failure. Int. J. Cardiol. 2018, 253, 97–104. [Google Scholar] [CrossRef]
  45. Kaur, B.; Durek, J.J.; O’Kane, B.L.; Tran, N.; Moses, S.; Luthra, M.; Ikonomidou, V.N. Heart rate variability (HRV): An indicator of stress. In Proceedings SPIE 9118, Independent Component Analyses, Compressive Sampling, Wavelets, Neural Net, Biosystems, and Nanoengineering XII; 91180V; SPIE: Baltimore, MD, USA, 2014. [Google Scholar] [CrossRef]
  46. ChuDuc, H.; NguyenPhan, K.; NguyenViet, D. A Review of Heart Rate Variability and its Applications. APCBEE Procedia 2013, 7, 80–85. [Google Scholar] [CrossRef] [Green Version]
  47. Higashi, D.; Imai, T.; Ueno, A.; Miyashita, O. A Wearable capacitive heart-rate monitor for controlling electrically assisted bicycle. In Proceedings of the 2009 International Conference on Electrical Machines and Systems, Tokyo, Japan, 15–18 November 2009; pp. 1–6. [Google Scholar]
  48. Walker, W.; Praveen Aroul, A.L.; Bhatia, D. Mobile health monitoring systems. In Proceedings of the 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Minneapolis, MN, USA, 3–6 September 2009; pp. 5199–5202. [Google Scholar]
  49. Ueno, A.; Miyashita, O.; Yoshida, T.; Yamamoto, J.; Lataire, P. A wearable ECG-HR detector and its application to automatic assist-mode selection of an electrically assisted bicycle. In Proceedings of the 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Boston, MA, USA, 30 August–3 September 2011; pp. 6903–6906. [Google Scholar]
  50. Lee, C.-N.; Chan, L.; Yang, C.-Y.; Lee, G.-Y.; Ciou, C.-R. The design and implementation of the E-BIKE physiological monitoring prototype system for cyclists. In Proceedings of the IEEE iWEM2011, Taipei, Taiwan, 8–10 August 2011; pp. 161–165. [Google Scholar]
  51. Nagata, T.; Okada, S.; Makikawa, M. Electric motor assisted bicycle as an aerobic exercise machine. In Proceedings of the 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, San Diego, CA, USA, 28 August–1 September 2012; pp. 1933–1935. [Google Scholar]
  52. Kiryu, T.; Minagawa, H. Feasibility study on a perceived fatigue prediction dependent power control for an electrically assisted bicycle. In Proceedings of the 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Osaka, Japan, 3–7 July 2013; pp. 2088–2091. [Google Scholar]
  53. Theobalt, A.; Feodoroff, B.; Werth, D.; Loos, P. A Personalized and Context-Aware Mobile Assistance System for Cardiovascular Prevention and Rehabilitation. In Ambient Assisted Living; Wichert, R., Klausing, H., Eds.; Advanced Technologies and Societal Change; Springer: Berlin/Heidelberg, Germany, 2014; pp. 161–173. ISBN 978-3-642-37987-1. [Google Scholar]
  54. Blumenstein, T.; Zeitlmann, H.; Alves-Pinto, A.; Turova, V.; Lampe, R. Optimization of electric bicycle for youths with disabilities. SpringerPlus 2014, 3, 646. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  55. Afonso, J.; Rodrigues, F.; Pedrosa, D.; Afonso, J. Automatic Control of Cycling Effort Using Electric Bicycles and Mobile Devices. In World Congress on Engineering, WCE 2015; IAENG: Hong Kong, Chnia, 2015; ISBN 978-988-19253-4-3. [Google Scholar]
  56. Corno, M.; Giani, P.; Tanelli, M.; Savaresi, S.M. Human-in-the-Loop Bicycle Control via Active Heart Rate Regulation. IEEE Trans. Contr. Syst. Technol. 2015, 23, 1029–1040. [Google Scholar] [CrossRef]
  57. Corno, M.; Berretta, D.; Spagnol, P.; Savaresi, S.M. Design, Control, and Validation of a Charge-Sustaining Parallel Hybrid Bicycle. IEEE Trans. Contr. Syst. Technol. 2016, 24, 817–829. [Google Scholar] [CrossRef]
  58. Nath, S.; Sinha, S.; Gladence, L.M.; BevishJinila, Y.; Rajalakshmi, V. Health analysis of bicycle rider and security of bicycle using IoT. In Proceedings of the 2017 International Conference on Communication and Signal Processing (ICCSP), Chennai, India, 6–8 April 2017; pp. 0802–0806. [Google Scholar]
  59. De La Iglesia, D.; Villarrubia, G.; De Paz, J.; Bajo, J. Multi-Sensor Information Fusion for Optimizing Electric Bicycle Routes Using a Swarm Intelligence Algorithm. Sensors 2017, 17, 2501. [Google Scholar] [CrossRef] [Green Version]
  60. Makarova, I.; Shubenkova, K.; Pashkevich, A.; Boyko, A. Smart-Bike as One of the Ways to Ensure Sustainable Mobility in Smart Cities. In Sensor Systems and Software; Magno, M., Ferrero, F., Bilas, V., Eds.; Springer International Publishing: Cham, Switzerland, 2017; Volume 205, pp. 187–198. ISBN 978-3-319-61562-2. [Google Scholar]
  61. Chandraprabha Dissanayake, V.S.; Kannangara, R.E.; Bigumjith Dias, M.U.; Kulasekera, A.L.; Jayaweera, N. An Intelligent Control System for an Electrically Power Assisted Cycle (EPAC). Am. J. Eng. Appl. Sci. 2018, 11, 986–995. [Google Scholar] [CrossRef] [Green Version]
  62. Siong Kwong, W.; Ubin, A.; Mohd Jenu, M.Z. Cyclist Monitoring System using NI myRIO-1900. MATEC Web Conf. 2018, 150, 01006. [Google Scholar] [CrossRef] [Green Version]
  63. Makarova, I.; Buyvol, P.; Shubenkova, K.; Tsybunov, E.; Boyko, A. Development of Intelligent Smart Bicycle Control System. In Proceedings of the 2018 IEEE East-West Design & Test Symposium (EWDTS), Kazan, Russia, 14–17 September 2018; pp. 1–8. [Google Scholar]
  64. Mayr, P.; Falgenhauer, M.; Modre-Osprian, R.; Hofmann, P.; Traninger, H.; Rath, M.; Schreier, G. HEALTHeBIKES-Smart E-Bike Prototype for Controlled Exercise in Telerehabilitation Programs. Stud. Health Technol. Inf. 2018, 248, 307–313. [Google Scholar]
  65. De La Iglesia, D.; De Paz, J.; Villarrubia González, G.; Barriuso, A.; Bajo, J.; Corchado, J. Increasing the Intensity over Time of an Electric-Assist Bike Based on the User and Route: The Bike Becomes the Gym. Sensors 2018, 18, 220. [Google Scholar] [CrossRef] [Green Version]
  66. Sweeney, S.; Ordonez-Hurtado, R.; Pilla, F.; Russo, G.; Timoney, D.; Shorten, R. A Context-Aware E-Bike System to Reduce Pollution Inhalation While Cycling. IEEE Trans. Intell. Transport. Syst. 2019, 20, 704–715. [Google Scholar] [CrossRef] [Green Version]
  67. Venkatanarayanan, A.; Vijayavel, A.; Rajagopal, A.; Nagaradjane, P. Design of sensor system for air pollution and human vital monitoring for connected cyclists. IET Commun. 2019, 13, 3181–3186. [Google Scholar] [CrossRef]
  68. Meyer, D.; Korber, M.; Senner, V.; Tomizuka, M. Regulating the Heart Rate of Human–Electric Hybrid Vehicle Riders Under Energy Consumption Constraints Using an Optimal Control Approach. IEEE Trans. Contr. Syst. Technol. 2019, 27, 2125–2138. [Google Scholar] [CrossRef]
  69. Rey-Barth, S.; Eychenne, C.; Rolland, C.; Pinsault, N.; Bosson, J. Use of a smart electrically assisted bicycle (VELIS) in the health field -Proof of concept. Med. Eng. Phys. 2020, 81, 125–129. [Google Scholar] [CrossRef] [PubMed]
  70. Barrios, L.; Oldrati, P.; Santini, S.; Lutterotti, A. Evaluating the accuracy of heart rate sensors based on photoplethysmography for in-the-wild analysis. In Proceedings of the Proceedings of the 13th EAI International Conference on Pervasive Computing Technologies for Healthcare, Trento, Italy, 20–23 May 2019; ACM: Trento, Italy, 2019; pp. 251–261. [Google Scholar]
  71. Etiwy, M.; Akhrass, Z.; Gillinov, L.; Alashi, A.; Wang, R.; Blackburn, G.; Gillinov, S.M.; Phelan, D.; Gillinov, A.M.; Houghtaling, P.L.; et al. Accuracy of wearable heart rate monitors in cardiac rehabilitation. Cardiovasc. Diagn. 2019, 9, 262–271. [Google Scholar] [CrossRef]
  72. Zang, K.; Shen, J.; Huang, H.; Wan, M.; Shi, J. Assessing and Mapping of Road Surface Roughness based on GPS and Accelerometer Sensors on Bicycle-Mounted Smartphones. Sensors 2018, 18, 914. [Google Scholar] [CrossRef] [Green Version]
  73. Ferster, C.; Nelson, T.; Laberee, K.; Winters, M. Mapping bicycling exposure and safety risk using Strava Metro. Appl. Geogr. 2021, 127, 102388. [Google Scholar] [CrossRef]
  74. Iqbal, A.; Iqbal, A.; Ali, H.; Ehatisham-ul-Haq, M.; Arsalan, A.; Raheel, A. Fall Detection of Riders using Inertial Sensors: A Smart Helmet. In Proceedings of the 2021 International Bhurban Conference on Applied Sciences and Technologies (IBCAST), Islamabad, Pakistan, 12–16 January 2021; pp. 620–624. [Google Scholar]
  75. Islam, M.M.; Ridwan, A.E.M.; Mary, M.M.; Siam, M.F.; Mumu, S.A.; Rana, S. Design and Implementation of a Smart Bike Accident Detection System. In Proceedings of the 2020 IEEE Region 10 Symposium (TENSYMP), Dhaka, Bangladesh, 5–7 June 2020; pp. 386–389. [Google Scholar]
  76. Murgano, E.; Caponetto, R.; Pappalardo, G.; Cafiso, S.D.; Severino, A. A Novel Acceleration Signal Processing Procedure for Cycling Safety Assessment. Sensors 2021, 21, 4183. [Google Scholar] [CrossRef] [PubMed]
  77. Behrendt, F.; Kiefer, C. Smart e-bike monitoring system: Real-time open source and open hardware GPS assistance and sensor data for electrically-assisted bicycles. IET Intell. Transp. Syst. 2016, 10, 79–88. [Google Scholar] [CrossRef] [Green Version]
  78. Bellenger, C.R.; Thomson, R.L.; Howe, P.R.C.; Karavirta, L.; Buckley, J.D. Monitoring athletic training status using the maximal rate of heart rate increase. J. Sci. Med. Sport 2016, 19, 590–595. [Google Scholar] [CrossRef] [Green Version]
  79. Cheng, T.M.; Savkin, A.V.; Celler, B.G.; Su, S.W. Lu Wang Nonlinear Modeling and Control of Human Heart Rate Response During Exercise With Various Work Load Intensities. IEEE Trans. Biomed. Eng. 2008, 55, 2499–2508. [Google Scholar] [CrossRef] [PubMed]
  80. Achten, J.; Jeukendrup, A.E. Heart Rate Monitoring: Applications and Limitations. Sports Med. 2003, 33, 517–538. [Google Scholar] [CrossRef] [PubMed]
  81. Dalleck, L.C.; Kravitz, L. Relationship Between %Heart Rate Reserve And %VO2 Reserve During Elliptical Crosstrainer Exercise. J. Sports Sci. Med. 2006, 5, 662–671. [Google Scholar] [PubMed]
  82. Naresh, V.; Lee, N. A Review on Biosensors and Recent Development of Nanostructured Materials-Enabled Biosensors. Sensors 2021, 21, 1109. [Google Scholar] [CrossRef]
  83. Camarda, S.R.D.A.; Tebexreni, A.S.; Páfaro, C.N.; Sasai, F.B.; Tambeiro, V.L.; Juliano, Y.; Barros Neto, T.L.D. Comparison of maximal heart rate using the prediction equations proposed by Karvonen and Tanaka. Arq. Bras. Cardiol. 2008, 91, 311–314. [Google Scholar] [CrossRef] [Green Version]
  84. Karvonen, J.; Vuorimaa, T. Heart rate and exercise intensity during sports activities. Practical application. Sports Med. 1988, 5, 303–311. [Google Scholar] [CrossRef]
  85. Kawada, T.; Sunagawa, G.; Takaki, H.; Shishido, T.; Miyano, H.; Miyashita, H.; Sato, T.; Sugimachi, M.; Sunagawa, K. Development of a servo-controller of heart rate using a treadmill. Jpn. Circ. J. 1999, 63, 945–950. [Google Scholar] [CrossRef] [Green Version]
  86. Fagard, R.; Conway, J. Measurement of cardiac output: Fick principle using catheterization. Eur. Heart J. 1990, 11, 1–5. [Google Scholar] [CrossRef] [PubMed]
  87. Zhou, B.; Conlee, R.K.; Jensen, R.; Fellingham, G.W.; George, J.D.; Fisher, A.G. Stroke volume does not plateau during graded exercise in elite male distance runners. Med. Sci. Sports Exerc. 2001, 33, 1849–1854. [Google Scholar] [CrossRef] [PubMed]
  88. Meyer, D.; Senner, V. Evaluating a heart rate regulation system for human–electric hybrid vehicles. Proc. Inst. Mech. Eng. Part P J. Sports Eng. Technol. 2018, 232, 102–111. [Google Scholar] [CrossRef]
  89. Pasadyn, S.R.; Soudan, M.; Gillinov, M.; Houghtaling, P.; Phelan, D.; Gillinov, N.; Bittel, B.; Desai, M.Y. Accuracy of commercially available heart rate monitors in athletes: A prospective study. Cardiovasc. Diagn. Ther. 2019, 9, 379–385. [Google Scholar] [CrossRef]
  90. Arakawa, T. A Review of Heartbeat Detection Systems for Automotive Applications. Sensors 2021, 21, 6112. [Google Scholar] [CrossRef] [PubMed]
  91. Challoner, A.V.; Ramsay, C.A. A photoelectric plethysmograph for the measurement of cutaneous blood flow. Phys. Med. Biol. 1974, 19, 317–328. [Google Scholar] [CrossRef] [PubMed]
  92. Kamal, A.A.; Harness, J.B.; Irving, G.; Mearns, A.J. Skin photoplethysmography—A review. Comput. Methods Programs Biomed. 1989, 28, 257–269. [Google Scholar] [CrossRef]
  93. Hermand, E.; Cassirame, J.; Ennequin, G.; Hue, O. Validation of a Photoplethysmographic Heart Rate Monitor: Polar OH1. Int. J. Sports Med. 2019, 40, 462–467. [Google Scholar] [CrossRef] [PubMed]
  94. Temko, A. Accurate Heart Rate Monitoring During Physical Exercises Using PPG. IEEE Trans. Biomed. Eng. 2017, 64, 2016–2024. [Google Scholar] [CrossRef] [Green Version]
  95. Rogers, B.; Giles, D.; Draper, N.; Hoos, O.; Gronwald, T. A New Detection Method Defining the Aerobic Threshold for Endurance Exercise and Training Prescription Based on Fractal Correlation Properties of Heart Rate Variability. Front. Physiol. 2021, 11, 1806. [Google Scholar] [CrossRef] [PubMed]
  96. Cottin, F.; Leprêtre, P.-M.; Lopes, P.; Papelier, Y.; Médigue, C.; Billat, V. Assessment of ventilatory thresholds from heart rate variability in well-trained subjects during cycling. Int. J. Sports Med. 2006, 27, 959–967. [Google Scholar] [CrossRef] [PubMed]
  97. Nascimento, E.M.F.; Salvador, P.C.D.N.; Antunes, D.; Possamai, L.T.; Ventura, T.; Guglielmo, L.G.A.; Denadai, B.S.; de Lucas, R.D. Heart rate variability kinetics during different intensity domains of cycling exercise in healthy subjects. Eur. J. Sport Sci. 2021, 1–9. [Google Scholar] [CrossRef]
  98. Cassirame, J.; Chevrolat, S.; Mourot, L. Effects of R-R time series accuracy on heart rate variability indexes. Mov. Sport Sci.-Sci. Mot. 2019, 106, 27–35. [Google Scholar] [CrossRef] [Green Version]
  99. Sierra-Alonso, E.F.; Septemberúlveda-Cano, L.M.; Bailón-Luesma, R.; Laguna, P.; Castellanos-Dominguez, G. Estimating respiratory frequency from HRV during treadmill exercise testing. In Proceedings of the Computing in Cardiology 2013, Zaragoza, Spain, 22–25 September 2013; pp. 121–124. [Google Scholar]
  100. Smolander, J.; Juuti, T.; Kinnunen, M.-L.; Laine, K.; Louhevaara, V.; Männikkö, K.; Rusko, H. A new heart rate variability-based method for the estimation of oxygen consumption without individual laboratory calibration: Application example on postal workers. Appl. Erg. 2008, 39, 325–331. [Google Scholar] [CrossRef]
  101. Rusko, H.; Pulkkinen, A.; Saalasti, S.; Hynynen, E.; Kihu, J. Pre-prediction of epoc: A tool for monitoring fatigue accumulation during exercise? Med. Sci. Sports Exerc.-Med. Sci. Sport Exerc. 2003, 35, S183. [Google Scholar] [CrossRef] [Green Version]
  102. Miles, M.; Rodrigues, A.; Tajali, S.; Xiong, Y.; Orchanian-Cheff, A.; Reid, W.D.; Rozenberg, D. Muscle and cerebral oxygenation during cycling in chronic obstructive pulmonary disease: A scoping review. Chron Respir. Dis. 2021, 18, 1479973121993494. [Google Scholar] [CrossRef]
  103. Bowes, W.A.; Corke, B.C.; Hulka, J. Pulse oximetry: A review of the theory, accuracy, and clinical applications. Obs. Gynecol. 1989, 74, 541–546. [Google Scholar]
  104. Al-Naji, A.; Khalid, G.A.; Mahdi, J.F.; Chahl, J. Non-Contact SpO2 Prediction System Based on a Digital Camera. Appl. Sci. 2021, 11, 4255. [Google Scholar] [CrossRef]
  105. Schiefer, L.M.; Treff, G.; Treff, F.; Schmidt, P.; Schäfer, L.; Niebauer, J.; Swenson, K.E.; Swenson, E.R.; Berger, M.M.; Sareban, M. Validity of Peripheral Oxygen Saturation Measurements with the Garmin Fēnix® 5X Plus Wearable Device at 4559 m. Sensors 2021, 21, 6363. [Google Scholar] [CrossRef]
  106. Hermand, E.; Coll, C.; Richalet, J.-P.; Lhuissier, F.J. Accuracy and Reliability of Pulse O2 Saturation Measured by a Wrist-worn Oximeter. Int. J. Sports Med. 2021, 42, 1268–1273. [Google Scholar] [CrossRef]
  107. Bundy, M.; Leaver, A. Training and conditioning. In A Guide to Sports and Injury Management; Elsevier: Amsterdam, The Netherlands, 2010; pp. 1–9. ISBN 978-0-443-06813-3. [Google Scholar]
  108. Hedge, E.; Au, J.; Hughson, R. Wearable Sensor Technology and Machine Learning for Prediction of Oxygen Uptake and Extraction of Kinetics during Moderate and Heavy Intensity Exercise by Eric Thomas Hedge. Available online: https://www.semanticscholar.org/paper/Wearable-Sensor-Technology-and-Machine-Learning-for-Hedge-Au/979affa63843c3b06d34a6d59de22f3b46133c9e (accessed on 15 November 2021).
  109. Zignoli, A.; Fornasiero, A.; Ragni, M.; Pellegrini, B.; Schena, F.; Biral, F.; Laursen, P.B. Estimating an individual’s oxygen uptake during cycling exercise with a recurrent neural network trained from easy-to-obtain inputs: A pilot study. PLoS ONE 2020, 15, e0229466. [Google Scholar] [CrossRef] [Green Version]
  110. Alzamer, H.; Abuhmed, T.; Hamad, K. A Short Review on the Machine Learning-Guided Oxygen Uptake Prediction for Sport Science Applications. Electronics 2021, 10, 1956. [Google Scholar] [CrossRef]
  111. Barstow, T.J. Understanding near infrared spectroscopy and its application to skeletal muscle research. J. Appl. Physiol. 2019, 126, 1360–1376. [Google Scholar] [CrossRef]
  112. Stöggl, T.; Born, D.-P. Near Infrared Spectroscopy for Muscle Specific Analysis of Intensity and Fatigue during Cross-Country Skiing Competition—A Case Report. Sensors 2021, 21, 2535. [Google Scholar] [CrossRef]
  113. Born, D.-P.; Stöggl, T.; Swarén, M.; Björklund, G. Near-Infrared Spectroscopy: More Accurate Than Heart Rate for Monitoring Intensity in Running in Hilly Terrain. Int. J. Sports Physiol. Perform. 2017, 12, 440–447. [Google Scholar] [CrossRef]
  114. Jaafar, H.; Attiogbé, E.; Rouis, M.; Vandewalle, H.; Driss, T. Reliability of Force-Velocity Tests in Cycling and Cranking Exercises in Men and Women. BioMed. Res. Int. 2015, 2015, e954780. [Google Scholar] [CrossRef] [Green Version]
  115. Dorel, S.; Couturier, A.; Lacour, J.-R.; Vandewalle, H.; Hautier, C.; Hug, F. Force-velocity relationship in cycling revisited: Benefit of two-dimensional pedal forces analysis. Med. Sci. Sports Exerc. 2010, 42, 1174–1183. [Google Scholar] [CrossRef]
  116. Vercruyssen, F.; Brisswalter, J. Which factors determine the freely chosen cadence during submaximal cycling? J. Sci. Med. Sport 2010, 13, 225–231. [Google Scholar] [CrossRef] [PubMed]
  117. Abbiss, C.; Peiffer, J.; Laursen, P. Optimal cadence selection during cycling. ECU Publ. 2009, 10, 1–15. [Google Scholar]
  118. Suzuki, K.; Omiya, K.; Yamada, S.; Kobayashi, T.; Suzuki, N.; Osada, N.; Miyake, F. Relations between strength and endurance of leg skeletal muscle and cardiopulmonary exercise testing parameters in patients with chronic heart failure. J. Cardiol. 2004, 43, 59–68. [Google Scholar]
  119. Farooqi, M.A.M.; Killian, K.; Satia, I. The impact of muscle strength on exercise capacity and symptoms. ERJ Open Res. 2020, 6, 00089–02020. [Google Scholar] [CrossRef]
  120. McLeod, W.D.; Blackburn, T.A. Biomechanics of knee rehabilitation with cycling. Am. J. Sports Med. 1980, 8, 175–180. [Google Scholar] [CrossRef] [PubMed]
  121. Johnston, T.E. Biomechanical Considerations for Cycling Interventions in Rehabilitation. Phys. Ther. 2007, 87, 1243–1252. [Google Scholar] [CrossRef] [Green Version]
  122. Olivier, N.; Weissland, T.; Legrand, R.; Berthoin, S.; Rogez, J.; Thevenon, A.; Prieur, F. The effect of a one-leg cycling aerobic training program during the rehabilitation period in soccer players with anterior cruciate ligament reconstruction. Clin. J. Sport Med. 2010, 20, 28–33. [Google Scholar] [CrossRef] [PubMed]
  123. Kisner, C.; Colby, L.A. Therapeutic Exercise: Foundations and Techniques, 6th ed.; F.A. Davis: Philadelphia, PA, USA, 2012; ISBN 978-0-8036-2574-7. [Google Scholar]
  124. Perry, J.; Bekey, G.A. EMG-force relationships in skeletal muscle. Crit. Rev. Biomed. Eng. 1981, 7, 1–22. [Google Scholar]
  125. Bengs, D.; Jeglinsky, I.; Surakka, J.; Hellsten, T.; Ring, J.; Kettunen, J. Reliability of Measuring Lower-Limb-Muscle Electromyography Activity Ratio in Activities of Daily Living With Electrodes Embedded in the Clothing. J. Sport Rehabil. 2017, 26, 1–12. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  126. Colyer, S.L.; McGuigan, P.M. Textile Electrodes Embedded in Clothing: A Practical Alternative to Traditional Surface Electromyography when Assessing Muscle Excitation during Functional Movements. J. Sports Sci. Med. 2018, 17, 101–109. [Google Scholar] [PubMed]
  127. Hernández, M.A.; Ramírez, O.; Benavides, J.A.; Franco, J.F. Urban cycling and air quality: Characterizing cyclist exposure to particulate-related pollution. Urban Clim. 2021, 36, 100767. [Google Scholar] [CrossRef]
  128. Carreras, H.; Ehrnsperger, L.; Klemm, O.; Paas, B. Cyclists’ exposure to air pollution: In situ evaluation with a cargo bike platform. Environ. Monit. Assess. 2020, 192, 470. [Google Scholar] [CrossRef]
  129. Farrell, W. Investigating the Effects of the Urban Environment on Cyclist Exposure to Near-Roadway Air Pollution. Master’ Thesis, McGill University, Montreal, QC, Canada, 2014. [Google Scholar]
  130. Berghmans, P.; Bleux, N.; Panis, L.I.; Mishra, V.K.; Torfs, R.; Van Poppel, M. Exposure assessment of a cyclist to PM10 and ultrafine particles. Sci. Total Environ. 2009, 407, 1286–1298. [Google Scholar] [CrossRef]
  131. Int Panis, L.; de Geus, B.; Vandenbulcke, G.; Willems, H.; Degraeuwe, B.; Bleux, N.; Mishra, V.; Thomas, I.; Meeusen, R. Exposure to particulate matter in traffic: A comparison of cyclists and car passengers. Atmos. Environ. 2010, 44, 2263–2270. [Google Scholar] [CrossRef]
  132. Caggiani, L.; Camporeale, R.; Ottomanelli, M. A real time multi-objective cyclists route choice model for a bike-sharing mobile application. In Proceedings of the 2017 5th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS), Naples, Italy, 26–28 June 2017; pp. 645–650. [Google Scholar]
  133. Elen, B.; Peters, J.; Poppel, M.; Bleux, N.; Theunis, J.; Reggente, M.; Standaert, A. The Aeroflex: A Bicycle for Mobile Air Quality Measurements. Sensors 2012, 13, 221–240. [Google Scholar] [CrossRef]
  134. Liu, X.; Li, B.; Jiang, A.; Qi, S.; Xiang, C.; Xu, N. A bicycle-borne sensor for monitoring air pollution near roadways. In Proceedings of the 2015 IEEE International Conference on Consumer Electronics-Taiwan, Taipei, Taiwan, 6–8 June 2015; pp. 166–167. [Google Scholar]
  135. Aguiari, D.; Delnevo, G.; Monti, L.; Ghini, V.; Mirri, S.; Salomoni, P.; Pau, G.; Im, M.; Tse, R.; Ekpanyapong, M.; et al. Canarin II: Designing a smart e-bike eco-system. In Proceedings of the 2018 15th IEEE Annual Consumer Communications & Networking Conference (CCNC), Las Vegas, NV, USA, 12–15 January 2018; pp. 1–6. [Google Scholar]
  136. Shen, S.; Lv, C.; Xu, X.; Liu, X. A Bicycle-Borne Sensor Node for Monitoring Air Pollution Based on NB-IoT. In Machine Learning and Intelligent Communications; Zhai, X.B., Chen, B., Zhu, K., Eds.; Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering; Springer International Publishing: Cham, Switzerland, 2019; Volume 294, pp. 325–332. ISBN 978-3-030-32387-5. [Google Scholar]
  137. Hossain, E.; Khan, A.; Moni, M.A.; Uddin, S. Use of Electronic Health Data for Disease Prediction: A Comprehensive Literature Review. IEEE/ACM Trans. Comput. Biol. Bioinf. 2021, 18, 745–758. [Google Scholar] [CrossRef]
  138. Morgenstern, J.D.; Buajitti, E.; O’Neill, M.; Piggott, T.; Goel, V.; Fridman, D.; Kornas, K.; Rosella, L.C. Predicting population health with machine learning: A scoping review. BMJ Open 2020, 10, e037860. [Google Scholar] [CrossRef]
  139. Bini, R.R.; Hume, P.A.; Croft, J.; Kilding, A. Optimizing Bicycle Configuration and Cyclists’ Body Position to Prevent Overuse Injury Using Biomechanical Approaches. In Biomechanics of Cycling; Bini, R.R., Carpes, F.P., Eds.; Springer International Publishing: Cham, Switzerland, 2014; pp. 71–83. ISBN 978-3-319-05538-1. [Google Scholar]
  140. Bini, R.; Hume, P.A.; Croft, J.L. Effects of Bicycle Saddle Height on Knee Injury Risk and Cycling Performance. Sports Med. 2011, 41, 463–476. [Google Scholar] [CrossRef] [PubMed]
  141. Swart, J.; Holliday, W. Cycling Biomechanics Optimization—The (R) Evolution of Bicycle Fitting. Curr. Sports Med. Rep. 2019, 18, 490–496. [Google Scholar] [CrossRef]
  142. Holmes, J.C.; Pruitt, A.L.; Whalen, N.J. Lower extremity overuse in bicycling. Clin. Sports Med. 1994, 13, 187–205. [Google Scholar] [CrossRef]
  143. Maio, A.; Afonso, J. Wireless Cycling Posture Monitoring Based on Smartphones and Bluetooth Low Energy. In World Congress on Engineering; World Congress on Engineering, WCE 2015; IAENG: Hong Kong, Chnia, 2015; ISBN 978-988-19253-4-3. [Google Scholar]
  144. Korff, T.; Romer, L.M.; Mayhew, I.; Martin, J.C. Effect of Pedaling Technique on Mechanical Effectiveness and Efficiency in Cyclists. Med. Sci. Sports Exerc. 2007, 39, 991–995. [Google Scholar] [CrossRef] [Green Version]
  145. Lucchetti, A.; Corno, M.; Savaresi, S.M. Cyclist’s Riding Style Assessment via Inertial Measurement. IFAC-PapersOnLine 2017, 50, 994–999. [Google Scholar] [CrossRef]
  146. Ruegsegger, G.N.; Booth, F.W. Health Benefits of Exercise. Cold Spring Harb. Perspect. Med. 2018, 8, a029694. [Google Scholar] [CrossRef] [Green Version]
Figure 1. Top 10 causes of death [11].
Figure 1. Top 10 causes of death [11].
Sensors 22 00468 g001
Figure 2. Electrically assisted bikes and associated research fields.
Figure 2. Electrically assisted bikes and associated research fields.
Sensors 22 00468 g002
Table 1. Most common sensors used across the literature.
Table 1. Most common sensors used across the literature.
SensorsOxygen SaturationHeart RatePower/Force/TorqueCadenceSpeedGyroscopeAccelero-MeterGPSWeather
Articles
A Wearable Capacitive Heart-Rate Monitor for Controlling Elec-trically Assisted Bicycle (2009) [47]NoYesNoNoNoNoNoNoNo
Mobile Health Monitoring Systems (2009) [48]YesYesNoNoYesNoYesNoTempera-ture
A wearable ECG-HR detector and its application to automatic assist-mode selection of an electrically as-sisted bicycle (2011) [49]NoYesNoNoNoNoNoNoNo
The design and implementation of the E-BIKE physiological monitoring pro-totype system for cyclists [50]NoYesNoNoNoNoNoYesTempera-ture
Electric Motor Assisted Bicycle as an Aerobic Ex-ercise Machine (2012) [51]Yes (VO2)YesNoYesNoNoNoNoNo
Feasibility Study on a Perceived Fatigue Prediction Dependent Power Control for an Electrically Assisted Bicycle (2013) [52]*Inferred from each stroke using the electromyo-gramNoYes*Yes*Yes*YesNoNoNoNo
A Personalized and Context-Aware Mobile Assistance Sys-tem for Cardio-vascular Preven-tion and Rehabili-tation (2014) [53]YesYesYesYesYesNoYesYesNo
Optimization of Electric Bicycle for Youths with Disabilities (2014) [54]YesYesYesYesYesNoNoNoNo
Automatic Con-trol of Cycling Ef-fort Using Elec-tric Bicycles and Mobile Devices (2015) [55]NoNoYesYesNoNoNoNoYes
Human-in-the-Loop Bicycle Control via Ac-tive Heart Rate Regulation (2015) [56]NoYesYesYesNoNoNoNoNo
Design, Control, and Validation of aCharge-Sustain-ing Parallel Hy-brid Bicycle (2016) [57]NoYesYesYesNoNoNoNoNo
Health Analysis ofBicycle Rider andSecurity of Bicy-cle Using IoT (2017) [58]NoYesNoNoYesNoYesNoNo
Multi-Sensor InformationFusion for Opti-mizing Electric Bicycle Routes Using a Swarm Intelligence Algo-rithm (2017) [59]NoYesNoYesYesNoNoYesNo
Smart-Bike as One of the Ways to Ensure Sustainable Mobility in Smart Cities (2017) [60]NoYesNoNoYesYesYesYesYes
An Intelligent Control System for an Electrically Power Assisted Cycle (EPAC) (2018) [61]NoYesNoYesYesNoYesNoNo
Cyclist Monitoring System Using NI myRIO-1900 (2018) [62]NoYesNoNoYesNoYesYesNo
Development of Intelligent Smart Bicycle Control System (2018) [63]NoYesNoNoYesYesYesYesYes
HEALTHeBIKES–Smart E-Bike Prototype for Controlled Exercise in Tele rehabilitation Programs (2018) [64]NoYesYesYesYesNoNoNoNo
Increasing the Intensity over Time of an Electric-Assist Bike Basedon the User and Route: The Bike Becomes the Gym (2018) [65]NoYesNoYesYesNoNoYesNo
A context-aware e-bike system to reduce pollution inhalation while cycling (2019) [66]YesYesYesNoYesNoNoNoYes (air quality)
Design of sensor system for air pollution and human vital monitoring for connected cyclists (2019) [67]NoYesNoNoNoNoYesNoNo
Regulating the Heart Rate of Human–Electric Hybrid Vehicle Riders Under Energy ConsumptionConstraints Using an Optimal Control Approach (2019) [68]NoYesYesYesNoNoNoNoNo
Use of a smart electrically assisted bicycle (VELIS) in the health field Proof of concept (2020) [69]NoYesYesYesYesNoNoYesNo
* They used an electromyogram to evaluate the fatigue on the leg muscles, and from that measure, they also estimate the values of torque, cadence and speed.
Table 2. Architectures used across the literature.
Table 2. Architectures used across the literature.
ArchitecturesPCServerMicro-ControllerSmartphone
Articles
A Wearable Capacitive Heart-Rate Monitor for Controlling Electrically Assisted Bicycle(2009) [47]NoNoYesNo
Mobile Health Monitoring Systems (2009) [48]YesYesYesNo
A wearable ECG-HR detector and its application to automatic assist-mode selection of an electrically assisted bicycle (2011) [49]YesNoNoNo
The design and implementation of the E-BIKE physiological monitoring prototype system for cyclists (2011) [50]NoNoYesNo
Electric Motor Assisted Bicycle as an Aerobic Exercise Machine (2012) [51]NoNoYesNo
A Personalized and Context-Aware Mobile Assistance System for Cardiovascular Prevention and Rehabilitation (2014) [53]YesYesYesYes
Optimization of Electric Bicycle for Youths with Disabilities (2014) [54]YesNoYesNo
Automatic Control of Cycling Effort Using Electric Bicycles and Mobile Devices (2015) [55]NoNoYesYes
Human-in-the-Loop Bicycle Control via Active Heart Rate Regulation (2015) [56]NoNoYesNo
Design, Control, and Validation of aCharge-Sustaining Parallel Hybrid Bicycle (2016) [57]NoNoYesYes
Health Analysis ofBicycle Rider andSecurity of Bicycle Using IoT (2017) [58]NoNoYesYes
Multi-Sensor InformationFusion for Optimizing Electric Bicycle Routes Using a Swarm Intelligence Algorithm (2017) [59]NoYesYesYes
Smart-Bike as One of the Ways to Ensure Sustainable Mobility in Smart Cities (2017) [60]NoYesYesNo
An Intelligent Control System for an Electrically Power Assisted Cycle (EPAC) (2018) [61]NoNoYesNo
Cyclist Monitoring System Using NI myRIO-1900 (2018) [62]YesNoYesNo
Development of Intelligent Smart Bicycle Control System (2018) [63]NoNoYesYes
HEALTHeBIKES–Smart E-Bike Prototype for Controlled Exercise in Tele rehabilitation Programs (2018) [64]NoYesYesYes
Increasing the Intensity over Time of an Electric-Assist Bike Basedon the User and Route: The Bike Becomes the Gym (2018) [65]NoYesYesYes
A context-aware e-bike system to reduce pollution inhalation while cycling (2019) [66]NoNoYesYes
Design of sensor system for air pollution and human vital monitoring for connected cyclists(2019) [67]NoNoYesYes
Regulating the Heart Rate of Human–Electric Hybrid Vehicle Riders Under Energy ConsumptionConstraints Using an Optimal Control Approach (2019) [68]NoNoYesNo
Table 3. Study characteristics across the literature.
Table 3. Study characteristics across the literature.
CharacteristicsSubjects (Sex)Mean AgeMean WeighMean HeightActivityConditionDuration
Articles
A Wearable Capacitive
Heart-Rate Monitor for
Controlling Electrically
Assisted Bicycle (2009) [47]
4 (N/S)21.860.31701.14 Km on flat road and 365 m of 4% slope2 min rest prior to the activity.
Constant speed of 10 Km/h
A wearable ECG-HR detector and its application to automatic assist mode selection of an electrically assisted bicycle (2011) [49]1 (M)22 4 loops–224 m flat and 106 m slope–3 sets: one without and with assistance, and the proposed controlConstant speed of 12 Km/h
Electric Motor Assisted Bicycle as an Aerobic Exercise Machine (2012) [51]5 (N/S) 4.6 Km mixed road–3 sets: assistance control at 65 RPM, 70 RPM and regular assistance
Feasibility Study on aPerceived Fatigue Prediction Dependent Power Control for an Electrically Assisted Bicycle (2013) [52]17 (12M, 5F)8M (23.8 ± 2.3) 4M (61.3 ± 8.1) 5F (44.2 ± 6.3) 2.1 Km with 600 m of uphill–20 min rest intervalsConstant cadence of 60 RPM
Optimization of Electric Bicycle for Youths with Disabilities (2014) [54]1 (N/S) 2 sets: with assistance and without (on a home trainer)
Automatic Control of Cycling Effort Using Electric Bicycles and Mobile Devices (2015) [55]1 (N/S) Home trainer resistance minimum value for 30 s. From 30 to 50 s resistance increased to maximum for 70 s. After resistance was gradually decreased–2 sets: with assistance and withoutConstant cadence
Human-in-the-Loop Bicycle Control via Active Heart Rate Regulation (2015) [56]2(N/S) 2 sets: Traditional mode and HR control mode
Design, Control, and Validation of a Charge-Sustaining Parallel Hybrid Bicycle (2016) [57]
First study1 (N/S) 1.1 Km with an average speed of 20 Km/h–2 sets: without assistance and with the algorithm
Second study2 (N/S) 20 min of route with ± 5% slopes–2 sets: without assistance and with the algorithm
Multi-Sensor Information Fusion for Optimizing Electric Bicycle Routes Using a Swarm Intelligence Algorithm (2017) [59]187 (N/S)Between 17 and 58 3.82 Km route (home to work) 11 Months
Cyclist Monitoring System Using NI myRIO 1900 (2018) [62]1 (N/S) Standard procedure
HEALTH eBIKES–Smart E-Bike Prototype for Controlled Exercise in Tele rehabilitation Programs (2018) [64]2 (N/S) 7 Rides on different terrains
Increasing the Intensity over Time of an Electric Assist Bike Based on the User and Route: The Bike Becomes the Gym (2018) [65]9 (6M, 3F)30.9 ± 4.7 4 Months
A context-aware e-bike system to reduce pollution inhalation while cycling (2019) [66]1 (N/S) 5 Km mostly flat route–2 lapsConstant speed of 20 Km/h
Regulating the Heart Rate of Human–Electric Hybrid Vehicle Riders Under Energy Consumption Constraints Using an Optimal Control Approach (2019) [68] Validation through simulation
Use of a smart electrically assisted bicycle (VELIS) in the health field Proof of concept (2020) [69]12 (7M, 5F)Between 29 and 66 14 Km with 350 positive elevation (6 Km flat, 2.5 Km uphill and 5.5 Km downhill)–4 sessionsConstant cadence of 55, 65 and 75 RPM depending on the dayOne week
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Avina-Bravo, E.G.; Cassirame, J.; Escriba, C.; Acco, P.; Fourniols, J.-Y.; Soto-Romero, G. Smart Electrically Assisted Bicycles as Health Monitoring Systems: A Review. Sensors 2022, 22, 468. https://doi.org/10.3390/s22020468

AMA Style

Avina-Bravo EG, Cassirame J, Escriba C, Acco P, Fourniols J-Y, Soto-Romero G. Smart Electrically Assisted Bicycles as Health Monitoring Systems: A Review. Sensors. 2022; 22(2):468. https://doi.org/10.3390/s22020468

Chicago/Turabian Style

Avina-Bravo, Eli Gabriel, Johan Cassirame, Christophe Escriba, Pascal Acco, Jean-Yves Fourniols, and Georges Soto-Romero. 2022. "Smart Electrically Assisted Bicycles as Health Monitoring Systems: A Review" Sensors 22, no. 2: 468. https://doi.org/10.3390/s22020468

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop