3.1. Important Factors
Sensors are the core of wearable technology. Without sensors, there is no use for wearables. Consumers are desiring monitoring systems that give out specific data. These data come from sensors and get processed for the intended user. Figure 4
gives a forecast in the importance of accelerometers, gyroscopes, and the impact of inertial sensors (combined), in their share of the market size. It is projected to be “$
2.86 Bn by 2025” [12
]. IDTech complemented this research claim in stating that the type of sensory components that will lead revenue for wearable tech (forecasted 2022) show the importance of IMU and optical sensors [13
]. This gives an idea of which future phase sensors may need more research. MEMS are very suitable for wearables due to their sizes, as designers prioritize being minimal in weight and power consumption (consequently reducing costs whilst increasing ergonomics). Table 1
shows the sensors that are present in wearable technology for different types of industries. This complements the data from Figure 4
, where accelerometers and gyroscopes are heavily present in multiple wearables.
All wearable tech listed in Table 1
has Bluetooth. Data from Vandrico show what each industry possesses in wearable tech and its hardware.
Alongside microcontrollers, there are some essentials for wearable technology to work—how data are communicated (wireless data transfer), storage, and battery [54
]. Storage in wearables is dependent on the operating system. The nature of feedback mediums such as smart watches, phones, tablets, or personal computers (PCs) depends on its application. With the use of Cloud storage, wearable firms can send the data from users’ wearables onto their servers for it to be processed (an ethical concern) [17
]. With consumer wearables, most applications give feedback on handhelds, meaning there can be a possibility of having the data stored on the phone itself (internal memory/memory card). The time taken to sync the data can vary, meaning there needs to be a base where the storage is kept. This is what can differ between storing on the device itself, e.g., flash, or on servers. Wearable technology manufacturers benefit from using a smart phone, as it possesses electronic components that are useful for wearables, such as Wi-Fi [58
Wireless communication is an essential part of wearables. It is regarded as the wireless sensor network and has different topologies (e.g., mesh, star, etc.) [59
]. These work with sensor nodes, which have low maintenance, and monitor the environmental conditions to determine how data transfer would occur [39
]. This component is fundamental for consumer ergonomics. It also allows the data storage to be defined regarding where the communication should transmit and receive. Radio frequency is commonly used for all essential communication methods. Table 2
shows the different wireless technologies for wearables. Actual quantitative specifications vary for different versions of the same wireless tech.
Different versions are available for the same wireless tech where parameters are different. The cost refers exclusively to the tag and not the processes involved with development [60
]. The bit rates are average values, as they fluctuate depending on application. Design consideration for choosing certain wireless communication methods is dependent on application as well, such as the size or how proof it is in certain conditions (waterproof, shatterproof, UV resistant, etc.) [61
]. Bluetooth low energy (BLE) is used greatly amongst the consumer sport manufacturers due to designer’s requiring low cost and power consumption with good reliability [62
]. Being low in energy consumption will help the sustainability of the network [64
]. For wearable tech, it can work in a Piconet, where one master device controls multiple slave devices.
Ideally, the range, the bit rate, and the power consumption are supposed to be proportionate. The specific values can only be known via testing. A 6-Mbits/s Wi-Fi data rate may work around 70 m but can also be 54-Mbits/s for 10 m, and these differ for indoor and outdoor conditions [65
]. Most smart watches will work with outdoor standard data sets. The downstream and upstream values also affect wearables, when designing [60
]. If a PC is considered as a base station, then the wearable cannot be portable [7
]. By having a smart phone paired with it, Wi-Fi can enable the phone to send these data to potential servers. The ranges listed above for Wi-Fi have a variety of versions, each fit for its intended purpose. Ideally, a wearable for the sports sector will not have either of these due to costs and maintenance. Cellular is another type of wireless sensor network, that has very high costs, power consumptions, range, bandwidth, and physical size. Cellular is heavily used in industry and is excluded in the review in Table 2
because it is not a feasible option. However, both Wi-Fi and cellular can be found in lifestyle (smart watches) and industry (google smart glasses) wearables.
Instances arise where manufacturers would choose more than one wireless technology, typically in lifestyle applications. This is because of the diverse use, such as smart watches that sends activity data to a base (BLE) as well as making payments (NFC). Lower bandwidth is preferred for wearables, as most do not need to transfer substantial amounts of data. NFC can also be preferred more in medical wearable sensors. The power consumptions are very dependent on the type of wearable and it’s use in industry [52
]. ZigBee has been trialed for gait monitoring experiments and rehabilitation. This was to test its potential in the medical industry with the creation of its mesh networks and in-built security measures, making it a choice as a potential wireless communication method [59
The source of power comes from batteries. Evolving battery technologies has helped wearables become the desired consumer items they are today, such as sensors being self-powered [67
]. Whilst designing, it is crucial that the battery can be recharged with minimal changes to the wearable itself. This is because it should not need to be unassembled and must have a way to remove the battery without taking the wearable apart too much (modular designs) [68
]. The importance in the size of wearables is distinguished by what the designer wants. There is a trade-off between operating time and data quality, which hinders the power source as well as the sensors used. This means that the size of the battery will affect the size of the sensor used. The battery consumption usage can be split into three. The first is the idle state, which can range from 0 to 25% of consumption. Sensing can also range from a similar range. Communication can use up to 50%, however some wearables may send the data whilst sensing, which would make the total (combined) consumption larger [69
]. Figure 5
shows the plot taken from Maxim Integrated about battery consumption in wearables. Common types of wearable batteries are alkaline, Nickel metal hybrids, and lithium ion (polymer versions as well), with the latter being the more popular option in wearables. With flexible thin film, energy harvesting is possible due to its high energy density, which can be perceived as another important benefit of Li-polymer batteries (pouch cell) for consumer wearable devices [70
illustrates an example of how Fitbit and Viper PODS differ in use. Viper PODS are used for certain periods of the day, whereas Fitbit is used throughout the day. This means that the power consumption states for both these wearables differ. It is important that the designer defines how much power consumption rate the device’s battery will consume. Fitbit monitoring too much can confuse the user if it starts producing data that they do not understand the benefits of. Viper PODS only work during certain hours, which may limit their capacity to judge certain parameters, such as recovery period. When designing wearable tech for the sports industry, it is important to know what the training regime can be for different types of users. This can identify which components are needed. Table 3
shows an example of “a day in the life of…”, a term used to detail a system’s stages throughout the day. Figure 6
displays the example set from Table 3
in how the battery consumption rates change during the day for both wearables. The projections are based on theoretical consumption rates (Figure 5
) without specific values to show an example of the difference between Fitbit (lifestyle wearable) and Viper POD (sport specific wearable). The actual percentage of battery consumption during the three states (communication, idle, and sensing) may be completely different. This example is just used to show the difference in the function of two wearables regarding battery used.
There has been research into battery-less wearables [70
]. This is for weight reduction (as batteries tend to be heavy) and for better energy conservation (sustainable). A form of harvesting energy, such as using potentially lost energy, can be useful. As with the piezoelectric effect or solar, which use photovoltaic cells (convert photons into energy), there are potential advances in this sector that follow energy harvesting methods [71
]. Using kinetic energy (body movement) to charge the wearable is useful, if not obvious, due to the nature of wearable technology applications. These are still hindered by material advancements and how they can be integrated into wearable tech due to size. University of Southampton used piezoelectric energy harvesting methods on the insole of shoes. Charging occurs via kinetic movements. The sustainability of the piezo elements is a liability due to the ease of damage. Solar energies can be used for health purposes as well, such as sunburn time. With the advancement of wireless charging, this can also be used for design considerations where the components can be imbedded into equipment (easy maintenance).
Having sensors only send and receive data periodically (or in bulk) can help power consumption. This will mean it has to be stored somewhere [17
]. Wearable technology integrated with smart phones has an advantage in that it can use the phone’s storage capability. Data need to be kept somewhere safe. Lifestyle wearables depend on continuous readings, and there may be a need to use every data that the wearable are monitoring [28
]. This may be something consumers desire. With firms using Cloud services to store consumer data, which has live encryption monitoring, storage priority may not be a concern [68
]. However, when ethical issues arise in data privacy, this may make consumers uncomfortable regarding how their personal data are accessible.
shows an example of how a wearable that has a chip with IMU sensors, can track data and communicate via wireless technology. The option to have it on servers or phones can be dependent on the user, and the feedback is fed into where the designer planned for.
3.4. Monitored Data
In sport, there is always a subjective opinion needed. This is because the stats that are monitored by sensors are physical [74
]. Zepp uses a camera to truly define some of their performance measuring capabilities. Filtering can give the users a representation of their performance [73
]. How can sensors measure performance stats? This can be done via data processing, where filters (e.g., Kalman) can convert the physical data in performance terms. Other reliability concerns are whether real time data feedback is as accurate as post game processed data. A study from Victoria University Australia investigated how real time GPS data compared against post game data. There were more errors present in real time tracking, which means that there are still opportunities for electronic improvements for live time accurate monitoring [44
Individual influence in sport varies depending on whether or not it is a team sport. Therefore, there is a complexion in defining some attributes by sensors and whether it can really help team play. Data monitoring shows key skills that can be tracked, but for a sports coach, they will always prioritize the collective team data [74
]. Data scientists may always need to be viewing and analyzing the sensory data and linking them to key performance attributes. This may require multiple sensors working together [31
]. Performance stats are more technical, and if advancements are made to allow data sync between teammates in training, only then can a collective team progression be made. Even if this is successful, having a subjective opinion is always an important factor (e.g., player may run more distance than usual yet lose a game). Thus, in what context is an individual judged based on team performance, and whether they themselves are improving individually to help the team efficiently or tactically; are questions that can only be subjective [74
Accuracy of wearables and transparency of the data published are fundamental elements. Producing calculations with precisely monitored body movements and quantifying them in a way that consumers understand is a smart procedure, but how well the sensors measure these body movements is another concern entirely. It is still considered that the wearables are not accurate in producing training data [75
]. It has been perceived that sensor readings are moderately accurate to actual movements. Accuracy is higher when doing exercises of low to moderate intensities or when doing consistent movements, such as jogging [75
]. This is also the case when the sensors are exclusively measuring one attribute [50
]. The accuracy differs more when doing sport related activities where players not only experience high intensity but they are constantly changing agility, which leads to sensors not producing accurate readings [75
]. Readings that users see are not just the quantities that are measured by the sensors but what the program is told to do with these data. The conversions, algorithms, and data process of the monitored quantity are all equally responsible for the accuracy of sensor readings.
Tests have been done on fitness wearables where the accuracy of the data was perceived to be consistent amongst the different types [76
]. This validated that the wearables were monitoring accurately but they differed from each other in that they were affected by various activity states [31
]. Examples of variety are when some of the researched wearables produce very accurate readings for step counting, and some give a larger range for heart rates [50
]. Inaccuracy is judged against one wearable reading. Thus, the difference made by other wearable readings indicates that the sensors used are not consistent enough, giving the range of error. This directly links to what the data conversions are giving, e.g., algorithms [75
]. When intense activities do occur, the processing of raw data needs to improve in accuracy to give precise meaningful feedback. Because fitness wearables now advertise as tracking many factors, such as energy expenditure, this can complicate the programming side, which results in more inconsistent accurate readings [31
]. Even if sleep monitoring is accurate, there can be occurrences where the processing of the data is varied due to it tracking so many different activities. Therefore, the wearables must be smart enough to detect the changes of activity states [31
]. Accuracy of sport wearables tend to differ in that they are measured via camera tracking. This method gives a better subjective analysis and makes it easier to quantify biomechanical movement [76
Design for behavior change is still crucial, even if wearables are considered disruptive tech [77
]. There are still people who do not want to wear wearables [80
]. Even though technology has become more affordable—smart phones being very dominant in consumer portable belongings—most smart watches are advertised to do what phones do. The question is whether it is a worthy replacement, which is very individual based. Smart phones are a heavy investment. Would it really be possible to replace that with a wrist worn device that functions as a phone or acts as an accessory to it [80
]? Due to ergonomic advantages of smart phones, the sustainability of wearables will be compared to them. There can be usability issues that hamper the success of wearables, but wrist worn devices have their own ergonomic advantages [6
]. People may also not want to invest in fitness wearables because smart phones already have applications (apps) that also do what wearables do [80
]. The Nike Run app is an example where the smart phone’s sensors (accelerometer, gyroscope, etc.) are slotted into a plastic sleeve, and when the user exercises, it can process the data monitored into physical stats in relation to fitness. Pairing this with Under Armour’s My Fitness Pal app, which takes both food consumption and exercises stats, nullifies the reason to invest in a separate fitness wearable. How users adapt to the experience, forecasts the sustainability of the wearable [77
User experience and interface plays a role in the perception of monitored data [81
]. The user will only judge these results in comparison to their physical activity. If the wearable outputs raw data without making it user friendly, then the perception of accuracy will be questionable. This ergonomic consideration is what accuracy of wearables is judged upon. Where the user sends and receives data is a sustainability factor. Users can share their quantified achievements to relevant people. This can be a motivational reason or a reason to educate themselves more by viewing other’s feedback on progress [83
]. Social interactivity increases immersion and learning experiences.
Size and skin contact can be a concern depending on the individual. Aesthetics can be an obstacle to replace traditional watches, which are viewed as jewelry [80
]. Older generations may not want them due to their perception on technology advancement [6
]. Fitness enthusiasts may feel they do not need them due to their success from traditional methods. However, there are reasons why consumers would want this technology in their lives in a very self-obsessed era. Thriving on relating success to quantified data is where wearable technology has become disruptive and where trends have spiked since the evolution of social media [3
3.5. Injury Tracking
With increasingly human-centered designs, the use of wearable technology allows industry to work more efficiently, thus funds can be invested effectively. Elderly and disabled will need extensive human factors research, as this is where technology can really help ease a way of living [84
]. Remote monitoring can be a solution, as advancements in biosensors have led to this contribution [73
]. In the medical field, instead of having a patient book an appointment in advance for a hospital, sensors that allow a recording of the patient’s heart rate (e.g., printed PCB on t-shirt) can allow the doctor to be notified if there are any abnormal activities [21
]. Figure 8
shows an example block diagram of how wearable technology can be used to monitor health for the elderly and disabled. Known as the wireless body area network, this benefits the user and the doctor, saving time and giving a more transparent form of communication and analysis [59
]. Advancements in biosensors have led to this contribution.
The environment is a crucial factor in how smart wearables in the medical sector play a role. In rural areas, the chance of seeing a physician is less than that in an urban area. To have good access to health care, one must travel further if they reside in rural areas. Having a monitoring system that can simplify elements relating to a user’s health condition can benefit them psychologically as well as physically [24
]. There is a greater transparency between the condition, the user, and the doctor. However, the user may not prefer this way, as they may not be accustomed to such maintenance of technology and might prefer regular reassurance from an actual doctor [4
Wearable technology in sport is not just about tracking performance. Health monitoring systems that are applied in the medical industry use the same sensors that can be used in sport, allowing wearable technology research to be very compatible [9
]. Wearable technology used to assess spine movement is relatively considered as a medical experiment, but its value in sport is just as important [7
]. The same sensors can give both the player and their doctor a greater interaction using technology to monitor live time health status. This also educates the player on where they are making it easier to become injury prone. Harbin University’s research into how a multifunctional single sensor is used for bioengineering applications such as gait monitoring and gestures is useful in sport. This is due to the sensors having multifunctional capability whilst reducing complexity [74
American football is known to have sensors embedded into their helmets, which monitors the status of head injuries (concussion) [11
]. Due to the sport’s nature (frequent head to head tackles), there is a need to monitor how the forces are being dissipated and dispersed throughout the helmet [84
]. This measurement can give an idea of how much energy is being felt on the inside of the dampening material (inner foam pads). Material modifications can have potential biomechanical effects, such as how much shock absorption it allows, and pressure distribution [14
]. Smart clothing is also an influence in this sector, however, there can be an argument that the protection or monitoring unit may not be aesthetically pleasing or comfortable, which discourages the user from wearing it [85
Wearable tech clothing has been researched in improving a baseball pitcher’s biomechanics. This research benefits the performance of the pitcher whilst trying to reduce the chances of potential injuries. Compression shirts are known to be used to detect arm movement and technique [25
]. This same method can be used to track the diverse factors of pitching styles, which can relate to performance and injury factors. Sensors are placed in the lower back and arms with conductive threads to give power. Producing data of how pitching consistencies are performed and how injuries can be prevented helps conditioning of the techniques [86
]. From Table 6
, Zepp golf and baseball editions allow monitoring of the player’s swing (biomechanical features) to improve their stance. This is not just to optimize attributes related to performance (timing, strength, speed, etc.) but also to educate how their movements should be done to minimize chances of injury. North American baseball also has a Health and Injury Tracking System, that displays injury surveillance without wearable tech, but exclusively with observations [88
]. Trends are easily noticed this way by collating data on injuries, sessions, body part, position, history, lost time, recovery time, medical clearance, and diagnosis to generate reports for team physiotherapists and doctors to advise coaching staff in training routines. This is a way of using observational methods for monitoring injury patterns.
There are two types of injury classifications in sport—accidental and overuse. Accidental injuries are sudden, ones that players may not see coming. Accidental injuries have been linked to being traceable from an observational point of view. When a player makes a mistake, they are more prone to rash decisions to compensate, which means they can cause these accidental injuries. Another possibility could be using trackers that monitor sleep patterns; if they are irregular, then it can be linked to making bad decisions based on mental fatigue [58
]. These are examples of predicting accidents due to human errors by a player [89
]. When an injury occurs, the treatment process can be compiled with medicine, physiotherapy, and adequate rest. This will give an idea of when to undertake gradual training before returning to fitness [90
Overuse injuries can be a result of repetitive actions with or without correct form. It can be dependent on the strains and loads, which are applied to certain parts of the body [92
]. Minor overuse injuries can heal on their own or with minimum treatment. Major overuse injuries will need extensive care. The intensity at which a player performs can determine how severe an injury can be. A minor injury also has the possibility to become a major injury if the player has not treated it properly. Overuse injuries can affect the bones, muscles, joints, tendons, or ligaments [93
Overuse injuries can be prevented with correct form of movement whilst training, which enables the body to familiarize itself with the correct motions needed [94
]. Warming up is renowned as a traditional form of exercise before intense training begins [92
]. This allows the muscles to be flexible, strong, and healthy, giving greater blood supply. In terms of cardiovascular strength, low intensity cardio allows the heart rate to gradually increase, which sets up the body to be in a good position to react to drastic changes, such as sudden increase in intensities. These ranges of motion can be done with adequate loads on the tendons and ligaments. Loosening the muscles is needed to give users enough degree of freedom in their movements, passing the strength to resist potential movements that cause joint pain or muscle damage [95
With advances in the medical as well as the fitness sector in wearable technology, there is a good combination for the sports industry to work with in injury tracking and performance. Figure 9
(data adapted from National Health Service UK) shows sport injuries patients in 2012 who were admitted to the Accident and Emergency Department (A&E). A higher number of attendances in sport injuries were 10–19 year old males. This can be classified as youths. The next highest attendees were 20–29 year olds. This means that younger generations may find it beneficial to use smart wearables to reduce the frequency or severity of injuries. These urgent care scenarios are not defined in accidental or overuse, thus a comparison between the two cannot be made.
A study conducted by the University of Birmingham and Southampton FC researched how workload relates to injury in youth level football players [96
]. The research revolved around acute chronic ratio (acute workload ratio divided by the chronic work load ratio). This calculation is mostly used to decide when the player can return to their last best-known fitness level. It is used to predict the recovery time of the player and to avoid risks.
Acute work load is the force experienced by the player in the most recent week of training. Chronic work load is the average force experienced by the player during a 4-week time period prior to the present week (rolling averages). This method is used for different sports, with the characteristics being forwarded to different situations. Gradual increases in loads and intensities must be taken with precaution to reduce the chance of overuse injury [96
]. Mechanical loads are defined by the sensors as the cumulative index of effort based on acceleration [97
]. This format is trying to build a resistance to the loadings at an adaptable pace. If the acute loadings spike abnormally, then they are likely to cause injuries. It is important to gather data of players before they train to know their different states and history of injuries [90
If a wearable device has been preprogrammed to measure these loads, then certain conditions need to be applied. This is where human input is very important, e.g., if a player has not been putting in as much effort during the first two weeks for psychological reasons and then manages to increase their efforts, the tracking device could show a chance of injury because there were very minimal readings considering rolling averages. This hinders the accuracy of the wearable in feedback, even when the sensors are working perfectly. The player could have been accelerating at a normal rate during times where the device may not have been worn. The choices that a player makes can make a wearable’s judgment less reliable. It requires precise and sensitive monitoring throughout the day to fully define chronic load injuries. Essentially, the acute to chronic ratio helps player conditioning and prevents injury whilst allowing players to perform efficiently to an extent [96
The acute to chronic workload ratio is regarded as a better predictor of injury than either acute and chronic alone. Hulin et al. conducted a study into rugby league players, finding that higher chronic workload players are more resistant to injury in moderate ratio conditions [98
]. They are more prone to injury when the acute to chronic ratio is very high. These can be causes of those sudden spikes in abnormal motions during training. It can be noted that sensors have shown enough characteristics to predict elements that can identify high risk movements [96
]. Bath university worked on Rugby Union players and how they have a decreasing rate of thresholds, i.e., the maximum load that a player can exert before injury, as the season progresses due to fatigue [99
]. Therefore, conditioning during preseason is necessary, as players can increase their threshold limits. Combining multiple key elements will help physiotherapists know how a player can recover. The important question designers will face is how this information can be relayed back to an amateur athlete who solely relies on wearable technology feedback [97
shows some potential examples of which biomechanical factors in sports that lead to injury can be monitored by sensors. Consistency is the key to how a gradual work load increases safely. This method of defining injury via quantifying workloads means that if a player is accelerating at a higher level continuously for over two weeks, the player is regarded as having a greater chance of injury. This higher level can only be determined by how the wearable defines the user’s average acceleration. This reading must be crucial if the wearable can measure the user’s change in average levels. This could then distinguish if the player is slowly improving their work load without risking themselves to injury. Table 8
also restates how important IMU sensors can be. They are heavily used in sports wearables for performance, but this table shows how these sensors potentially show signs of injury monitoring. This is where data processing is a complex feature, as it needs to be able to derive these parameters and distinguish the difference in what it measures. Even when analyzing running, there is a need to know the biomechanical features to determine how to increase performance or reduce injury. This is where the advancement of both accelerometers and gyroscopes together is useful [42
]. For some examples, this can be simple, such as a gyroscope being used to determine the angular rotation of the hip, which can immediately show if it is too excessive. Calculated and programmed values can be used in acute chronic ratio based on monitoring rolling averages that lead to a potential injury.
Presently, it is considered that if the ratio is large (greater than one), it means that the acute workload is greater in the current training week [96
]. If a player experiences an overuse injury, would there be an option to implement this data in wearable tech? How features in equipment design can be something desirable for the consumer for tracking such parameters is a question for the near future [100
]. There are numerous factors to determine how frequent overuse injuries occur for a player [92
]. Sensors can monitor total distance covered, but observations can show external factors when wearable technology is not worn. The longer the player runs, the sorer their muscles will be; due to endurance, there is a higher chance of injury based on observation. This would link to how injury prone the observed player could be. Multiple factors must be combined to determine overuse injury, such as length of high intense loads and the momentum, which is linked to the mass and the speed of the player. These can be used to forecast the length of time a player has been running at higher intensity and the more frequent loads per step they experience, consequently giving a better understanding of where injuries could occur. It should not be mistaken for gradual improvements. GPS trackers on wearable technology are known to monitor load values such as the Viper POD [96
]. Monitoring how the average peak impact of each step on both feet can show where the user may be more prone to injury (left or right side) [102
]. It helps users maintain correct form and improve efficiency. Therefore, the programming of wearable technology (microcontroller data processing) is vital [103
Data protection is an ethical concern. There are security measures deployed to prevent consumer data from being accessible by other parties. This is an important parameter that designers and manufacturers of wearables will have to consider if they are to release it to the consumer market. This is important not only to protect the consumer’s identity and data relating to their personal wellbeing but also to protect the programming of the system. If hacks do occur on a manufacturer’s system, then it is very easy to alter the algorithms, which can completely disrupt the data processing elements of the wearable (reading out wrong data to confuse or worry the user). Therefore, balancing the budget is critical in terms of how many layers of cryptography the designer wants. Psychology plays a big role in sports performance, and momentum is perceived as the consistency of good form, but how can these terminologies be monitored and implemented into wearable tech? These terminologies are heavily used in observational stand points, and such progression is still to be made.