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Wearable Sensors and Mobile Apps in Human Health Monitoring

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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Biomedical Sensors".

Deadline for manuscript submissions: closed (28 August 2023) | Viewed by 15452

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Dr. Marco Altini

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Guest Editor

Founder of HRV4Training, Guest Lecturer Department of Human Movement Sciences, Vrije Universiteit Amsterdam, De Boelelaan 1105, 1081 HV Amsterdam, The Netherlands
Interests: wearable sensors; heartrate variability analysis; digital health; sport science
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Wearable sensors and mobile apps allow for non-invasive and continuous measurement of health biomarkers, enabling users and researchers to better understand human physiology at a much larger scale than normally possible in laboratory studies. In this Special Issue, you are invited to submit contributions describing the development and validation of technologies and methods to monitor human health using wearable sensors and smartphone apps. Contributions ranging from technology development and validation, and algorithm development and validation to large-scale analysis of user-generated data in the context of measuring and analyzing different aspects of human health such as cardiovascular health, metabolic health, sleep, physical activity, stress, chronic disease, etc. are encouraged.

Dr. Marco Altini
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (10 papers)

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Research

14 pages, 1268 KiB

Open AccessArticle

Validation of the Activ8 Activity Monitor for Monitoring Postures, Motions, Transfers, and Steps of Hospitalized Patients

by Marlissa L. Becker, Henri L. P. Hurkmans, Jan A. N. Verhaar and Johannes B. J. Bussmann

Sensors 2024, 24(1), 180; https://doi.org/10.3390/s24010180 - 28 Dec 2023

Cited by 1 | Viewed by 674

Abstract

Sedentary behaviors and low physical activity among hospitalized patients have detrimental effects on health and recovery. Wearable activity monitors are a promising tool to promote mobilization and physical activity. However, existing devices have limitations in terms of their outcomes and validity. The Activ8 device was optimized for the hospital setting. This study assessed the concurrent validity of the modified Activ8. Hospital patients performed an activity protocol that included basic (e.g., walking) and functional activities (e.g., room activities), with video recordings serving as the criterion method. The assessed outcomes were time spent walking, standing, upright, sedentary, and newly added elements of steps and transfers. Absolute and relative time differences were calculated, and Wilcoxon and Bland–Altman analyses were conducted. Overall, the observed relative time differences were lower than 2.9% for the basic protocol and 9.6% for the functional protocol. Statistically significant differences were detected in specific categories, including basic standing (p < 0.05), upright time (p < 0.01), and sedentary time (p < 0.01), but they did not exceed the predetermined 10% acceptable threshold. The modified Activ8 device is a valid tool for assessing body postures, motions, steps, and transfer counts in hospitalized patients. This study highlights the potential of wearable activity monitors to accurately monitor and promote PA among hospital patients. Full article

(This article belongs to the Special Issue Wearable Sensors and Mobile Apps in Human Health Monitoring)

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14 pages, 358 KiB

Open AccessArticle

Deep Learning in the Recognition of Activities of Daily Living Using Smartwatch Data

by Ariany F. Cavalcante, Victor H. de L. Kunst, Thiago de M. Chaves, Júlia D. T. de Souza, Isabela M. Ribeiro, Jonysberg P. Quintino, Fabio Q. B. da Silva, André L. M. Santos, Veronica Teichrieb and Alana Elza F. da Gama

Sensors 2023, 23(17), 7493; https://doi.org/10.3390/s23177493 - 29 Aug 2023

Cited by 1 | Viewed by 1427

Abstract

The recognition of human activities (HAR) using wearable device data, such as smartwatches, has gained significant attention in the field of computer science due to its potential to provide insights into individuals’ daily activities. This article aims to conduct a comparative study of deep learning techniques for recognizing activities of daily living (ADL). A mapping of HAR techniques was performed, and three techniques were selected for evaluation, along with a dataset. Experiments were conducted using the selected techniques to assess their performance in ADL recognition, employing standardized evaluation metrics, such as accuracy, precision, recall, and F1-score. Among the evaluated techniques, the DeepConvLSTM architecture, consisting of recurrent convolutional layers and a single LSTM layer, achieved the most promising results. These findings suggest that software applications utilizing this architecture can assist smartwatch users in understanding their movement routines more quickly and accurately. Full article

(This article belongs to the Special Issue Wearable Sensors and Mobile Apps in Human Health Monitoring)

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15 pages, 4818 KiB

Open AccessArticle

An Optoelectronics-Based Compressive Force Sensor with Scalable Sensitivity

by Zachary Pennel, Michael McGeehan and Keat Ghee Ong

Sensors 2023, 23(14), 6513; https://doi.org/10.3390/s23146513 - 19 Jul 2023

Cited by 1 | Viewed by 860

Abstract

There is an increasing need to accurately measure compressive force for biomedical and industrial applications. However, this need has not been fully addressed, as many sensors are bulky, have high power requirements, and/or are susceptible to electromagnetic interference. This paper presents an optoelectronics-based force sensor that can overcome the limitations of many sensors in the market. The sensor uses a light emitting diode (LED) to transmit visible broad-spectrum light into a photoresistor through an optically clear spacer on top of an elastomeric medium. In the absence of an external force, the light path is mostly blocked by the opaque elastomeric medium. Under a compressive force, the clear spacer compresses the elastomer, moving itself into the light path, and thus increasing the overall light transmission. The amount of light received by the photoresistor is used to quantify compressive force based on elastomer displacement/compression and a priori knowledge of elastomer stiffness. This sensing scheme was tested under eight different configurations: two different sized sensors with four types of elastomers per size (20A neoprene, 30A neoprene, 50A neoprene, and 75A styrene–butadiene rubber (SBR)). All configurations measured force with R² > 0.97, RMSE < 1.9 N, and sensitivity values ranging from 17 to 485 N/V. This sensing scheme provides a low-cost, low-power method for accurate force sensing with a wide force range. Full article

(This article belongs to the Special Issue Wearable Sensors and Mobile Apps in Human Health Monitoring)

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18 pages, 3428 KiB

Open AccessArticle

Human Activity Recognition Using Hybrid Coronavirus Disease Optimization Algorithm for Internet of Medical Things

by Asmaa M. Khalid, Doaa Sami Khafaga, Eman Abdullah Aldakheel and Khalid M. Hosny

Sensors 2023, 23(13), 5862; https://doi.org/10.3390/s23135862 - 24 Jun 2023

Cited by 3 | Viewed by 1066

Abstract

Background: In our current digital world, smartphones are no longer limited to communication but are used in various real-world applications. In the healthcare industry, smartphones have sensors that can record data about our daily activities. Such data can be used for many healthcare purposes, such as elderly healthcare services, early disease diagnoses, and archiving patient data for further use. However, the data collected from the various sensors involve high dimensional features, which are not equally helpful in human activity recognition (HAR). Methods: This paper proposes an algorithm for selecting the most relevant subset of features that will contribute efficiently to the HAR process. The proposed method is based on a hybrid version of the recent Coronavirus Disease Optimization Algorithm (COVIDOA) with Simulated Annealing (SA). SA algorithm is merged with COVIDOA to improve its performance and help escape the local optima problem. Results: The UCI-HAR dataset from the UCI machine learning repository assesses the proposed algorithm’s performance. A comparison is conducted with seven well-known feature selection algorithms, including the Arithmetic Optimization Algorithm (AOA), Gray Wolf Optimizer (GWO), Whale Optimization Algorithm (WOA), Reptile Search Algorithm (RSA), Zebra Optimization Algorithm (ZOA), Gradient-Based Optimizer (GBO), Seagull Optimization Algorithm (SOA), and Coyote Optimization Algorithm (COA) regarding fitness, STD, accuracy, size of selected subset, and processing time. Conclusions: The results proved that the proposed approach outperforms state-of-the-art HAR techniques, achieving an average performance of 97.82% in accuracy and a reduction ratio in feature selection of 52.7%. Full article

(This article belongs to the Special Issue Wearable Sensors and Mobile Apps in Human Health Monitoring)

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18 pages, 1652 KiB

Open AccessArticle

A Study on the Influence of Sensors in Frequency and Time Domains on Context Recognition

by Pedro de Souza, Diógenes Silva, Isabella de Andrade, Júlia Dias, João Paulo Lima, Veronica Teichrieb, Jonysberg P. Quintino, Fabio Q. B. da Silva and Andre L. M. Santos

Sensors 2023, 23(12), 5756; https://doi.org/10.3390/s23125756 - 20 Jun 2023

Viewed by 1156

Abstract

Adaptive AI for context and activity recognition remains a relatively unexplored field due to difficulty in collecting sufficient information to develop supervised models. Additionally, building a dataset for human context activities “in the wild” demands time and human resources, which explains the lack of public datasets available. Some of the available datasets for activity recognition were collected using wearable sensors, since they are less invasive than images and precisely capture a user’s movements in time series. However, frequency series contain more information about sensors’ signals. In this paper, we investigate the use of feature engineering to improve the performance of a Deep Learning model. Thus, we propose using Fast Fourier Transform algorithms to extract features from frequency series instead of time series. We evaluated our approach on the ExtraSensory and WISDM datasets. The results show that using Fast Fourier Transform algorithms to extract features performed better than using statistics measures to extract features from temporal series. Additionally, we examined the impact of individual sensors on identifying specific labels and proved that incorporating more sensors enhances the model’s effectiveness. On the ExtraSensory dataset, the use of frequency features outperformed that of time-domain features by 8.9 p.p., 0.2 p.p., 39.5 p.p., and 0.4 p.p. in Standing, Sitting, Lying Down, and Walking activities, respectively, and on the WISDM dataset, the model performance improved by 1.7 p.p., just by using feature engineering. Full article

(This article belongs to the Special Issue Wearable Sensors and Mobile Apps in Human Health Monitoring)

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12 pages, 1338 KiB

Open AccessArticle

Accelerometer-Assessed Physical Activity in People with Type 2 Diabetes: Accounting for Sleep when Determining Associations with Markers of Health

by Alex V. Rowlands, Vincent T. van Hees, Nathan P. Dawkins, Benjamin D. Maylor, Tatiana Plekhanova, Joseph Henson, Charlotte L. Edwardson, Emer M. Brady, Andrew P. Hall, Melanie J. Davies and Thomas Yates

Sensors 2023, 23(12), 5382; https://doi.org/10.3390/s23125382 - 07 Jun 2023

Cited by 1 | Viewed by 1220

Abstract

High physical activity levels during wake are beneficial for health, while high movement levels during sleep are detrimental to health. Our aim was to compare the associations of accelerometer-assessed physical activity and sleep disruption with adiposity and fitness using standardized and individualized wake and sleep windows. People (N = 609) with type 2 diabetes wore an accelerometer for up to 8 days. Waist circumference, body fat percentage, Short Physical Performance Battery (SPPB) test score, sit-to-stands, and resting heart rate were assessed. Physical activity was assessed via the average acceleration and intensity distribution (intensity gradient) over standardized (most active 16 continuous hours (M16h)) and individualized wake windows. Sleep disruption was assessed via the average acceleration over standardized (least active 8 continuous hours (L8h)) and individualized sleep windows. Average acceleration and intensity distribution during the wake window were beneficially associated with adiposity and fitness, while average acceleration during the sleep window was detrimentally associated with adiposity and fitness. Point estimates for the associations were slightly stronger for the standardized than for individualized wake/sleep windows. In conclusion, standardized wake and sleep windows may have stronger associations with health due to capturing variations in sleep durations across individuals, while individualized windows represent a purer measure of wake/sleep behaviors. Full article

(This article belongs to the Special Issue Wearable Sensors and Mobile Apps in Human Health Monitoring)

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11 pages, 2033 KiB

Open AccessCommunication

Activity-Based Prospective Memory in ADHD during Motor Sleep Inertia

by Miranda Occhionero, Lorenzo Tonetti, Andreas Conca, Sara Giovagnoli, Giancarlo Giupponi, Marina Zoppello and Vincenzo Natale

Sensors 2023, 23(11), 5181; https://doi.org/10.3390/s23115181 - 30 May 2023

Viewed by 1275

Abstract

Prospective memory (PM) is essential in everyday life because it concerns the ability to remember to perform an intended action in the future. Individuals diagnosed with attention deficit hyperactivity disorder (ADHD) often show poor performance in PM. Because age can be confounding, we decided to test PM in ADHD patients (children and adults) and healthy controls (children and adults). We examined 22 children (four females; mean age = 8.77 ± 1.77) and 35 adults (14 females; mean age = 37.29 ± 12.23) with ADHD, in addition to 92 children (57 females; mean age = 10.13 ± 0.42) and 95 adults (57 females; mean age = 27.93 ± 14.35) as healthy controls. Each participant originally wore an actigraph around the non-dominant wrist and was requested to push the event-marker at get-up time. To assess the efficiency of PM performance, we calculated the time elapsing between the end of sleep in the morning and the pushing of the event-marker button. The results showed lower PM performance in ADHD participants, regardless of age. However, the differences between ADHD and control groups were more evident in the children group. Our data seem to confirm that PM efficiency is compromised in individuals diagnosed with ADHD regardless of age, and agree with the idea of considering the PM deficit as a neuropsychological marker of ADHD. Full article

(This article belongs to the Special Issue Wearable Sensors and Mobile Apps in Human Health Monitoring)

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14 pages, 1709 KiB

Open AccessArticle

Validation of an Algorithm for Measurement of Sedentary Behaviour in Community-Dwelling Older Adults

by Khalid Abdul Jabbar, Javad Sarvestan, Rana Zia Ur Rehman, Sue Lord, Ngaire Kerse, Ruth Teh and Silvia Del Din

Sensors 2023, 23(10), 4605; https://doi.org/10.3390/s23104605 - 09 May 2023

Cited by 1 | Viewed by 2270

Abstract

Accurate measurement of sedentary behaviour in older adults is informative and relevant. Yet, activities such as sitting are not accurately distinguished from non-sedentary activities (e.g., upright activities), especially in real-world conditions. This study examines the accuracy of a novel algorithm to identify sitting, lying, and upright activities in community-dwelling older people in real-world conditions. Eighteen older adults wore a single triaxial accelerometer with an onboard triaxial gyroscope on their lower back and performed a range of scripted and non-scripted activities in their homes/retirement villages whilst being videoed. A novel algorithm was developed to identify sitting, lying, and upright activities. The algorithm’s sensitivity, specificity, positive predictive value, and negative predictive value for identifying scripted sitting activities ranged from 76.9% to 94.8%. For scripted lying activities: 70.4% to 95.7%. For scripted upright activities: 75.9% to 93.1%. For non-scripted sitting activities: 92.3% to 99.5%. No non-scripted lying activities were captured. For non-scripted upright activities: 94.3% to 99.5%. The algorithm could, at worst, overestimate or underestimate sedentary behaviour bouts by ±40 s, which is within a 5% error for sedentary behaviour bouts. These results indicate good to excellent agreement for the novel algorithm, providing a valid measure of sedentary behaviour in community-dwelling older adults. Full article

(This article belongs to the Special Issue Wearable Sensors and Mobile Apps in Human Health Monitoring)

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18 pages, 1452 KiB

Open AccessArticle

Healthy Ageing: A Decision-Support Algorithm for the Patient-Specific Assignment of ICT Devices and Services

by Agnese Brunzini, Manila Caragiuli, Chiara Massera and Marco Mandolini

Sensors 2023, 23(4), 1836; https://doi.org/10.3390/s23041836 - 07 Feb 2023

Cited by 2 | Viewed by 1372

Abstract

In response to rapid population ageing, digital technology represents the greatest resource in supporting the implementation of active and healthy ageing principles at clinical and service levels. However, digital information platforms that deliver coordinated health and social care services for older people to cover their needs comprehensively and adequately are still not widespread. The present work is part of a project that focuses on creating a new personalised healthcare and social assistance model to enhance older people’s quality of life. This model aims to prevent acute events to favour the elderly staying healthy in their own home while reducing hospitalisations. In this context, the prompt identification of criticalities and vulnerabilities through ICT devices and services is crucial. According to the human-centred care vision, this paper proposes a decision-support algorithm for the automatic and patient-specific assignment of tailored sets of devices and local services based on adults’ health and social needs. This decision-support tool, which uses a tree-like model, contains conditional control statements. Using sequences of binary divisions drives the assignation of products and services to each user. Based on many predictive factors of frailty, the algorithm aims to be efficient and time-effective. This goal is achieved by adequately combining specific features, thresholds, and constraints related to the ICT devices and patients’ characteristics. The validation was carried out on 50 participants. To test the algorithm, its output was compared to clinicians’ decisions during the multidimensional evaluation. The algorithm reported a high sensitivity (96% for fall monitoring and 93% for cardiac tracking) and a lower specificity (60% for fall monitoring and 27% for cardiac monitoring). Results highlight the preventive and protective behaviour of the algorithm. Full article

(This article belongs to the Special Issue Wearable Sensors and Mobile Apps in Human Health Monitoring)

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18 pages, 1003 KiB

Open AccessArticle

Wearable-Measured Sleep and Resting Heart Rate Variability as an Outcome of and Predictor for Subjective Stress Measures: A Multiple N-of-1 Observational Study

by Herman J. de Vries, Helena J. M. Pennings, Cees P. van der Schans, Robbert Sanderman, Hilbrand K. E. Oldenhuis and Wim Kamphuis

Sensors 2023, 23(1), 332; https://doi.org/10.3390/s23010332 - 28 Dec 2022

Cited by 2 | Viewed by 2733

Abstract

The effects of stress may be alleviated when its impact or a decreased stress-resilience are detected early. This study explores whether wearable-measured sleep and resting HRV in police officers can be predicted by stress-related Ecological Momentary Assessment (EMA) measures in preceding days and predict stress-related EMA outcomes in subsequent days. Eight police officers used an Oura ring to collect daily Total Sleep Time (TST) and resting Heart Rate Variability (HRV) and an EMA app for measuring demands, stress, mental exhaustion, and vigor during 15–55 weeks. Vector Autoregression (VAR) models were created and complemented by Granger causation tests and Impulse Response Function visualizations. Demands negatively predicted TST and HRV in one participant. TST negatively predicted demands, stress, and mental exhaustion in two, three, and five participants, respectively, and positively predicted vigor in five participants. HRV negatively predicted demands in two participants, and stress and mental exhaustion in one participant. Changes in HRV lasted longer than those in TST. Bidirectional associations of TST and resting HRV with stress-related outcomes were observed at a weak-to-moderate strength, but not consistently across participants. TST and resting HRV are more consistent predictors of stress-resilience in upcoming days than indicators of stress-related measures in prior days. Full article

(This article belongs to the Special Issue Wearable Sensors and Mobile Apps in Human Health Monitoring)

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