Submitted Successfully!
To reward your contribution, here is a gift for you: A free trial for our video production service.
Thank you for your contribution! You can also upload a video entry or images related to this topic.
Version Summary Created by Modification Content Size Created at Operation
1 -- 4115 2024-02-23 15:05:35 |
2 Reference format revised. -100 word(s) 4015 2024-02-26 01:23:11 |

Video Upload Options

Do you have a full video?


Are you sure to Delete?
If you have any further questions, please contact Encyclopedia Editorial Office.
Yang, K.; Mcerlain-Naylor, S.A.; Isaia, B.; Callaway, A.; Beeby, S. E-Textiles for Sports and Fitness Sensing. Encyclopedia. Available online: (accessed on 18 April 2024).
Yang K, Mcerlain-Naylor SA, Isaia B, Callaway A, Beeby S. E-Textiles for Sports and Fitness Sensing. Encyclopedia. Available at: Accessed April 18, 2024.
Yang, Kai, Stuart A. Mcerlain-Naylor, Beckie Isaia, Andrew Callaway, Steve Beeby. "E-Textiles for Sports and Fitness Sensing" Encyclopedia, (accessed April 18, 2024).
Yang, K., Mcerlain-Naylor, S.A., Isaia, B., Callaway, A., & Beeby, S. (2024, February 23). E-Textiles for Sports and Fitness Sensing. In Encyclopedia.
Yang, Kai, et al. "E-Textiles for Sports and Fitness Sensing." Encyclopedia. Web. 23 February, 2024.
E-Textiles for Sports and Fitness Sensing

E-textiles have emerged as a fast-growing area in wearable technology for sports and fitness due to the soft and comfortable nature of textile materials and the capability for smart functionality to be integrated into familiar sports clothing.

e-textiles wearable technology sensors sports fitness performance IMU ECG

1. Introduction

Wearable technologies are now accepted and widely used in multiple sports and fitness activities across all levels of performance, from recreational to elite, in individual and team sports, and including non-disabled and disabled athletes alike [1]. Wearables can be used to monitor a wide variety of biosignals (e.g., heart rate and muscle excitation) and can also track performance (e.g., distance and speed) and the technique (e.g., joint angles) used to produce that performance. The most common types of existing wearables are typically wrist-worn smartwatches, chest straps, devices mounted on or in the footwear, or, more recently, those located within sports clothing [2]. Analysis of subsequent data can be used to gauge improvements in fitness, help mitigate injury risk [3][4], inform recovery [5], monitor technique [6], or, at a consumer level, simply provide motivation [7]. The typical sensors used to date are inertial measurement units (IMUs comprising accelerometers, gyroscopes, and magnetometers), Global Positioning Systems (GPS), and heart rate (electrocardiography, ECG) and muscle excitation (electromyography, EMG) sensors.
Electronics textiles (e-textiles or smart fabrics) are advanced textiles that include electronic functionality ranging from conductive tracks to sensing/actuating, communications, and microprocessing [8]. The global market for e-textiles is projected to reach around $15 billion by 2028 [9]. E-textiles are a platform technology for wearables that are highly relevant to sports and fitness applications. The motivation for incorporating sensing and electronic functionality into textiles for sports and fitness applications is evidenced by the growing number of clothing-based wearable devices aimed at this sector. Examples include STATSports Apex Athlete series [10] and Catapult One [11], both of which are GPS-based tracking devices also incorporating IMUs, where the electronics containing sensing, communication, and processing capabilities are implemented in a conventional rigid form with the modules located in a pocket on the garment. GPS-enabled smart watches or footwear could provide similar GPS data; however, their use is not permitted in most sports where physical contact between participants is possible, but this is dependent on the changing rules of each sport.
Textiles provide a comfortable, ubiquitous platform that individuals are entirely familiar with. Considerable effort has gone into engineering technical textiles for sportswear [12], where the market is dominated by household brands such as Nike, Adidas, and Puma. E-textiles technology offers the ability to further enhance sportswear functionality by invisibly integrating sensors, microprocessors, and communications into garments [13][14]. This approach can potentially improve compliance with technology amongst users, develop the ecological validity of the data where sensing can happen in the natural sporting environment, and collect data on more important metrics about populations remotely to develop sensing algorithms and interventions. However, at present, the level of integration of the electronic functionality within the garment is typically limited to separate modules that fit into a pocket located on the clothing, as illustrated by the STATSports and Catapult cases. A few other examples do include additional functionality in the textile. Prevayl’s Smartwear incorporates conductive textile electrodes connected to the electronic unit with a 512 Hz sampling frequency to detect the ECG signal and display the information on a smartphone (Figure 1a) [15]. The product is used for both amateur and professional athletes. Equivital LifeMonitor with built-in multi-sensors can monitor ECG, respiration, tri-axis accelerometry, and temperature (Figure 1b). The product is mainly used in training (e.g., military) and health and safety monitoring in harsh working environments (e.g., fire-fighting), but it can also be used in professional sports monitoring (e.g., car races, bike races) [16]. Despite the advances in e-textiles, it is not straightforward to achieve the required reliable and robust electronic and sensing capability in textiles in a manner that has minimal effect on the properties of the fabric. Most existing e-textile technology does not yet deliver practical solutions that replicate the levels of sensing, processing, and communication functionality achieved with the separate, rigid, discrete modules located in a pocket within the garment.
Figure 1. (a) Prevayl ECG vest [15], reproduced with permission from Prevayl; (b) Equivital multi-parameter monitoring system [16], reproduced with permission from Equivital.

2. Technology and Sensing in Sports and Fitness

2.1. Current State and Future Considerations of Technology in Sports Science

Technology has always played an important role in sports science; without it, much of the scientific foundations within its sub-disciples would not have been possible. Historically, sports science was often restricted to a laboratory-based setting, in subject discipline isolation, but the rapid development of technology (sensors, processing, and communications) has allowed the movement from the laboratory to training and competition venues and environments. Calls to enhance the interdisciplinary nature of sports science work [17][18][19][20][21] have slowly been realised in research [22][23][24][25][26], but the call is ever-present. This multidisciplinary approach has happened more rapidly in practice in tandem with the professionalism of multidisciplinary teams [27] involving coaches, strength and conditioning specialists, medical doctors, rehabilitation therapists, physiologists, psychologists, nutritionists, and many others. This has likely happened with the commercial development of technology (e.g., cameras, heart rate monitoring, mobile force platforms), allowing the capture of a variety of data to measure athletic performance and facilitate the design of interventions to enhance performance, recover from injury, and monitor wellness, sleep, and diet.
Athletes wear smartwatches, fitness trackers, heart rate monitors, and other sensors to track their performance, monitor their health, and analyse their training. Previously outlawed in many competitions, there is a slow but welcome allowance to wear sensing technology in competitions, although sport-dependent. These devices can provide real-time feedback metrics such as heart rate, calories burned, and distance covered, helping athletes optimise their workouts and avoid overtraining. They can also help coaches track the progress of athletes they work with and make informed decisions about training and competition.
As wearable devices become more advanced, for example, with the development of communication to transfer more information more rapidly and with the development of mathematical processing from inertial sensors, they will provide ever more detailed and accurate data, giving athletes and coaches a deeper understanding of physical and tactical performance, and training loads. Analytics of this data will continue to improve, allowing analysts to identify more complex patterns and trends and make more accurate predictions about athlete performance and injury risk. However, many these processing capacities will lie with large companies interrogating ever-growing data sets.

2.2. Technology for Training Load Monitoring in Sport

A common application of technology within sports is for training load monitoring. Whether physical activity is performed for health and/or social benefits, sporting performance, injury risk reduction, and/or post-injury rehabilitation, a primary concern is the prescription and monitoring of an appropriate ‘dose’ of activity. This is often described as the ‘training load’, representing the demands of the particular activity for the particular individual in the particular context that it was performed [28]. At a simplistic level of understanding, there is hypothesised to exist a ‘sweet region’ of ideal training load, below which prior adaptations may be lost (i.e., disuse) and above which the negative consequences (e.g., tissue damage) of activity may exceed any beneficial adaptations (i.e., overuse) [29][30].
Training load is often divided into ‘external’ and ‘internal’ load. External load refers to the activity and work completed (e.g., distance travelled, mass lifted, number of repetitions), whereas internal load refers to the effects of that activity on biological systems [31][32][33]. In this context, ‘load’ may not necessarily refer to the mechanical force experienced but rather a description of the demands of the activity, just as in familiar terms such as ‘workload’, ‘cognitive load’, or ‘viral load’ [34][35]. Each of these categories can be further subdivided into physiological or biomechanical loads.
External biomechanical load may refer to ground reaction forces or centre of mass accelerations, whereas internal biomechanical load may refer to joint contact forces or muscle-tendon forces [34]. These alternate loads can, therefore, be prescribed, monitored, and altered somewhat independently of each other to achieve the overall aims of a training block or individual session, perhaps while also addressing secondary aims relating to injury risk management [36].
Perhaps the most well-known and commonly used technologies for monitoring training load in sports are heart rate monitors and GPS or Global Navigation Satellite Systems (GNSS) technologies. GPS is one sub-section of GNSS, with modern GNSS accessing a greater number of satellites and, therefore, potentially greater precision and reliability [37][38]. Both are typically monitored at the torso, heart rate via a chest strap, and GNSS via a unit positioned between the scapulae in a manufacturer-provided elastic harness. Heart rate data are used to calculate metrics such as peak or mean heart rate or time spent within heart rate zones generally corresponding to the use of different physiological energy pathways (e.g., aerobic/anaerobic energy systems). GNSS, on the other hand, can be used to calculate metrics, such as total distance covered, number of sprints, top speed, work:rest ratios, and the time spent in different speed zones.
One area of growing focus is the need and current relative inability to measure biomechanical training loads outside of a laboratory [35], especially internal biomechanical training loads. That is, the forces experienced by specific tissues within the body (e.g., muscle, bone, tendon, ligament). This is particularly relevant when seeking to associate training load measures with injury likelihood or inform progressive overloading of the tissue during rehabilitation or injury risk reduction programs. GNSS can describe the activity performed (e.g., a 10 km run at a certain speed), and heart rate can indicate the cardiovascular demands of the activity, but neither can describe the effects of the exercise on the musculoskeletal system. 
IMUs positioned elsewhere on the body offer additional measurement opportunities. IMUs on specific body segments can be used not only to quantify technique but also in conjunction with machine learning or other algorithms used to detect certain activities [39][40][41][42] and perhaps quantify the intensity of these movements. Greater insight can perhaps be gained when data from wearable technologies are used alongside existing models of tissue stress response or as inputs to neuromusculoskeletal models. For example, models of bone remodelling [43][44] have been used to quantify a tibial ‘Bone Stimulus’ metric from tibial accelerometry via IMeasureU’s IMU Step system. While this has shown reliable results in running and soccer-related tasks [45][46], studies have demonstrated an inability of tibial accelerometry to represent loads on the tibia bone [47][48]. Such bone forces are more dependent upon muscle forces than any shockwave resulting from the ground reaction force [47].
There are a number of important considerations when choosing or developing wearable technology for sports, particularly for biomechanical loads. These include hardware mass, dimensions, fixation, sensor range and sampling frequency, and calibration routines [49]. If the technology is intended to be used for injury-related applications, then it should additionally build upon established causal relationships, be applicable without any laboratory-based inputs, and be informed by specific guidelines such as individual- or population-based normative boundaries, thresholds, or trends [50]

3. Commercialised Wearable Technologies for Sports

3.1. Non-Textile/Clothing-Based Wearables

Wrist-based wearables are the most popular form of wearable technology for amateur athletes and have been used in numerous studies [51]. There are too many commercial smartwatches available to list here, but examples such as the Apple Watch and Fitbit Charge series provide typical functionality associated with such devices. This includes heart rate monitoring, general activity tracking (steps, distance, energy expenditure, floors climbed, built-in GPS) and recognition and tracking of particular sports/activities (e.g., walking, running, cycling, swimming). A comprehensive list of devices and their applications, together with a summary of performance evaluation, has been presented by Cosoli et al. [52][53]. Most smartwatches incorporate optical heart rate measurement by photoplethysmography (PPG). This approach can be subject to motion artefacts, and the quality of the fit and location on the wrist can also affect measurements, especially when active [54]. Error rates are higher when swimming, where arm movements and the water can also affect the PPG-based sensors [55]. Where arm movement is a key aspect of a particular sport (e.g., baseball pitching or tennis serving), the IMUs within smart watches can obtain sufficient kinematic data to provide real-time feedback on arm-related technique, allowing the player to improve their performance, leading to an improvement in ball speed and the pronation movement in serve [56]. The wearable WHOOP wristband is a wristband specifically focused on fitness and health that tracks an individual’s training, recovery, and sleep [57]. It uses PPG sensors to monitor heart rate, heart rate variability, and sleep, and it uses this data to estimate training magnitude whilst exercising and the rate of recovery. Other specialised wrist-based sensors have been developed for particular sports.
Another commonly used and well-established class of wearable devices for monitoring users when undertaking physical activity is chest straps with heart rate detection. These detect the electrical signal associated with a heartbeat through the skin, and hence, the electrodes located on the strap must be in contact with the skin. The same technique is used for clinical ECG measurements, although chest straps are limited to 2 electrodes, whereas clinical systems can use over 20. Early research into their efficacy was very positive, demonstrating that consumer sports chest straps achieve 99% accuracy when compared to clinical ECG equipment [58]
Wearable inertial sensors can be located on the body using pockets in clothing or by simply strapping the sensor onto, for example, a limb. These started with pedometers recording step count/frequency for daily ambulatory monitoring. The limitation of these is that only total steps for the duration of the recording are shown, and no temporal information is provided for the calculation of the rate of change of steps (i.e., were they running or walking). As technology has progressed, inertial sensor-based devices have helped address this limitation, allowing many sports to obtain various spatiotemporal parameters [59].

3.2. Clothing and Textile-Based Wearables

IMUs located within clothing have been developed for a range of specific sports. For example, sensors placed along the spine can be used to identify swimming stroke and phase [60], commercialised with the Incus Nova wearable [61]. This sits in a pocket between the shoulder blades and can also provide data when running. This highlights the broad applicability of IMUs, where the same hardware can provide data for each activity type, subsequently requiring the appropriate analysis. The STATSports Apex Athlete series [10] and Catapult One [11] GPS devices are designed to monitor an athlete’s performance in field sports where parameters such as the distance covered, number of sprints, time in different speed zones, and top speed are monitored. These garment-based wearables allow the electronics to be placed on the upper back between the scapulae, where they are safe and relatively unobtrusive. The STATSports Apex, for example, has been compared favourably with radar-based tracking technologies [62] and has been used to gain insight into the performance of football players in different age groups, the results of which could inform training programs [63]. The Nadi X, developed by startup company Wearable X [64], include accelerometers and haptic feedback in the form of vibrations that are designed to assist with obtaining and maintaining yoga positions. The rigid device mechanically mounts to compression-fitting yoga pants behind the knee.
E-textiles do not have to be engineered into full garments. For example, smart sensing sleeves that cover the forearm or full arm have been developed. The Komodo AIO Smart Sleeve is a general-purpose wearable device that incorporates a PPG sensor, IMU, battery and Bluetooth Low Energy hardware in a typical rigid module that snaps onto the inside of the sleeve and sits in contact with the skin [65]. The sleeve is available in both short (lower arm only) or long (full arm length) versions, and in active mode, the device monitors steps, distance, heart rate, sleep, and activity intensity. In health mode, the sleeve has an additional wired electrode that connects to the module and is designed to be placed on the chest. This provides one lead, two electrodes, and ECG data when stationary and cannot be used when training. The level of functionality within the textile is low, with the sleeve simply incorporating magnetic clip-on connectors and a single embedded wire to the top connector. Motus has developed a sleeve for monitoring throwing sports such as baseball, American football and cricket, but the functionality is again provided by a rigid module that sits within pockets on a sleeve or band [66]. Myontec offers a suite of garments, including compression shirts, shorts, waist belts and arm and lower-leg sleeves with IMU sensors and EMG electrodes [67]. The shorts provide similar functionality to the Stive system, with EMG sensing electrodes monitoring the quadriceps, hamstrings, and gluteals. This has been used to longitudinally analyse neuromuscular responses to training [68]. The leg sleeve monitors the tibialis, gastrocnemius and soleus muscles, whilst the belt targets the multifidus and erector spinae muscles in the back. The same module housing the IMU, and other EMG monitoring electronics has been used within each garment using a bespoke snap-on rigid connector. 

4. E-Textiles for Sports and Fitness

4.1. The Motivation for E-Textiles Research on Sports and Fitness

Textile implementations of sensors and electronics have a large range of potential applications in monitoring progress and performance, reducing injury risk and motivating regular exercise and training. To advance the current commercial products and enable the wide adoption of e-textile technologies in sports and fitness, research has been conducted in the areas of new materials and sensor components, manufacturing methods, system integration and user-centred design. The aims of these studies include improving sensing functions (e.g., accuracy, reliability), user comfort (e.g., flexibility, softness), durability (e.g., washing, wearing) and design (e.g., aesthetic appearance, ease to put on and take off). Other research has related to the charging/power, data acquisition/processing/transmission and the interaction with other technologies (e.g., Internet of Things (IoT), Artificial Intelligence (AI)).
The benefits offered by e-textile implementation depend upon the application and the solution, but there are several common advantages:
  • Textile/clothing-based solutions provide a comfortable and familiar platform to users. E-textiles would enable unobtrusive and ubiquitous deployment sensors in clothing. Textile-based soft products are safe in contact sports;
  • Textiles are versatile materials that can be designed to change their properties to fit the application needs through the optimal combination of textile materials and structures;
  • The integration in clothing will improve compliance (users might forget to use conventional technology, but they always remember to get dressed). Ease of use and increased compliance can provide more data to better inform training and reduce the risk of overload;
  • The unobtrusive and seamless integration of miniaturised or flexible sensors is less likely to influence the parameters being monitored. This allows close contact between the sensors and the skin, reducing measurement errors caused by the displacement of the rigid/large sensors relative to the underlying anatomy;
  • Multiple sensors can be incorporated into a single platform (e.g., an item of clothing) in multiple body positions rather than requiring users to wear several separate devices;
  • Integration of sensing within garments enables sensors to be located at the optimal location on the body and to measure a much wider range of signals than possible with, for example, a smartwatch;
  • Information or alerts can be provided through the textile, providing real-time feedback to the user in a single platform.

4.2. Research on E-Textiles for Sports and Fitness Using Different Sensing Technologies

IMUs, biopotential electrodes and tactile/pressure interfaces are the most commonly used technologies in sports [69]. Main applications include motion measurement, vital signs monitoring and interactive applications. The level of sensor-textile integration varies from simply attaching the sensor to the wearer using a textile (e.g., elastic strap) to integrating the sensor components (e.g., IMU chip, sensing yarn/ink) to form an integral part of the wearable e-textile.

4.3. IMUs for Motion Detection and Joint Function Measurement

The use of IMUs has accelerated with improvements in hardware development and data processing. IMUs have been used in e-textile wearables for sports and fitness due to their low cost, portability, real-time data, and suitability for dynamic movements. They have been used to monitor gestures/poses, range of motion, steps and types of activities (e.g., walking, running, climbing). They can also be used in gait analysis to assess movement patterns and monitor rehabilitation. IMUs have shown moderate to excellent correlations with gold standard approaches for gait spatiotemporal parameters during running [70], but users should be aware of poorer accuracy in high-speed and non-sagittal plane (i.e., abduction/adduction or internal/external rotation rather than flexion/extension) measurements [68][71]. Dahl et al. have validated an IMU system (Opal Gen 2) against a gold standard optical system for sports-related common movements (e.g., cutting, running, jumping) [72]. This study found a good level of agreement between the IMU and the optical system supporting the use of IMUs for sports-related movement/rehabilitation assessment, although the paper stated that continued validation and improvement of sensor accuracy is required. Jenkins et al. have investigated the use of an IMU (LSM6DSLTR) to monitor body configuration in relation to back injury risk in weightlifting exercises [73].

4.4. Electrodes for Heart Rate, Heart Rate Variability, and Muscle Excitation/Strength

ECG is the most common biosignal measured for the monitoring of cardiovascular health and to provide early detection of arrhythmias. The conventional Ag/AgCl hydrogel electrodes used in ECG monitoring are not suitable for long-term wearable applications due to the stickiness, moisture evaporation and ease of contamination of the electrodes. Textile-based dry electrodes have been developed for wearable ECG to overcome the disadvantages of hydrogel electrodes. The electrodes form an integral part of the textile, which is then made into a wearable item that provides tight contact between the electrodes and the skin to reduce signal noise generated via the movement of the electrodes. Electrodes are mainly made by depositing conductive inks/paste on textiles using printing [74][75][76], coating the textile in the electrode material solution [77][78] or knitting [79][80], weaving [81][82], or embroidering [83][84] commercially available conductive yarns or bespoke conductive yarns such as graphene-based yarn and PDOT: PSS-modified yarn [85][86].

4.5. Piezoresistive, Piezoelectrical, and Capacitive Materials for Pressure, Contact Force/Speed, and Respiratory Rate Measurement

Measuring the pressure/force applied to an object in sports and fitness activities (e.g., running, jumping, boxing) is another important parameter to monitor during training and to assess injury risk, e.g., from collisions. Piezoresistive, capacitive, and piezoelectric materials and devices are commonly used for monitoring pressure and force through the change in electrical resistance or capacitance or the generation of electrical charge in response to pressure or force, respectively. Functional materials are integrated into textiles using standard e-textile fabrication methods (e.g., printing, weaving, knitting, embroidery) to produce conformable and flexible smart garments that can be worn in sports/fitness activities.

5. Conclusions

Wearable technologies have been widely used in sports and fitness applications at both recreational and elite levels to track performance and health conditions. Wearable sensors provide useful data in vital biosignals and key performance parameters (e.g., speed, distance, acceleration) that can inform training to improve efficacy while reducing the risk of injury. E-textiles provide a platform for the ubiquitous deployment of wearable technologies due to the soft and comfortable nature of the textiles and their suitability for everyday wearing. The level of integration varies among different techniques and applications, from directly inserting the modular sensor/electronic unit (e.g., IMUs, GPS) into a pocket of the garment to having the sensing material truly embedded into the textile to make a sensing textile (e.g., ECG/EMG electrodes, force/pressure sensing fabric) that are connected to a detachable electronic unit. These two types of integration are the most common methods used in commercial e-textile products, and the fully integrated e-textiles system (e.g., electronic units, including batteries) is still in its development stage.
Further advancement is required in order to gain the full benefit of e-textile-based sensors. This includes the development of new materials and components with improved accuracy, reliability and durability; integration of the e-textiles system with miniaturised components to reduce the overall size and its impact on the sports/fitness garment; manufacturing processes that are scalable and cost-effective for both prototyping and volume production; co-design and with end users to ensure product market fit, standardised protocols in data management/privacy and performance testing.


  1. Rum, L.; Sten, O.; Vendrame, E.; Belluscio, V.; Camomilla, V.; Vannozzi, G.; Truppa, L.; Notarantonio, M.; Sciarra, T.; Lazich, A.; et al. Wearable Sensors in Sports for Persons with Disability: A Systematic Review. Sensors 2021, 21, 1858.
  2. Aroganam, G.; Manivannan, N.; Harrison, D. Review on Wearable Technology Sensors Used in Consumer Sport Applications. Sensors 2019, 19, 1983.
  3. Clermont, C.A.; Duffett-Leger, L.; Hettinga, B.A.; Ferber, R. Runners’ Perspectives on ‘Smart’ Wearable Technology and Its Use for Preventing Injury. Int. J. Hum. Comput. Interact. 2020, 36, 31–40.
  4. Benson, L.C.; Räisänen, A.M.; Volkova, V.G.; Pasanen, K.; Emery, C.A. Workload a-WEAR-ness: Monitoring Workload in Team Sports With Wearable Technology. A Scoping Review. J. Orthop. Sports Phys. Ther. 2020, 50, 549–563.
  5. Di Paolo, S.; Lopomo, N.F.; Della Villa, F.; Paolini, G.; Figari, G.; Bragonzoni, L.; Grassi, A.; Zaffagnini, S. Rehabilitation and Return to Sport Assessment after Anterior Cruciate Ligament Injury: Quantifying Joint Kinematics during Complex High-Speed Tasks through Wearable Sensors. Sensors 2021, 21, 2331.
  6. Rana, M.; Mittal, V. Wearable Sensors for Real-Time Kinematics Analysis in Sports: A Review. IEEE Sens. J. 2021, 21, 1187–1207.
  7. Rupp, M.A.; Michaelis, J.R.; McConnell, D.S.; Smither, J.A. The Role of Individual Differences on Perceptions of Wearable Fitness Device Trust, Usability, and Motivational Impact. Appl. Ergon. 2018, 70, 77–87.
  8. Komolafe, A.; Zaghari, B.; Torah, R.; Weddell, A.S.; Khanbareh, H.; Tsikriteas, Z.M.; Vousden, M.; Wagih, M.; Jurado, U.T.; Shi, J.; et al. E-Textile Technology Review–From Materials to Application. IEEE Access 2021, 9, 97152–97179.
  9. Facts & Factors. Global E-Textiles and Smart Clothing Market Size, Share; Facts & Factors: Pune, India, 2021.
  10. STATSports. Available online: (accessed on 9 December 2023).
  11. Catapult Sports. Available online: (accessed on 9 December 2023).
  12. Shishoo, R. (Ed.) Textiles for Sportswear, 1st ed.; Elsevier: Amsterdam, The Netherlands, 2015.
  13. Komolafe, A.O.; Torah, R.N.; Wei, Y.; Nunes-Matos, H.; Li, M.; Hardy, D.; Dias, T.; Tudor, M.J.; Beeby, S.P. Integrating Flexible Filament Circuits for E-textile Applications. Adv. Mater. Technol. 2019, 4, 1900176.
  14. Wicaksono, I.; Tucker, C.I.; Sun, T.; Guerrero, C.A.; Liu, C.; Woo, W.M.; Pence, E.J.; Dagdeviren, C. A Tailored, Electronic Textile Conformable Suit for Large-Scale Spatiotemporal Physiological Sensing in vivo. NPJ Flex. Electron. 2020, 4, 5.
  15. Prevayl. Available online: (accessed on 9 December 2023).
  16. Success Stories—Learn about How Equivital Has Made a Difference. Available online: (accessed on 20 January 2024).
  17. Derrick, T.R. The Need for Interdisciplinary Research in Sports Biomechanics. Sports Biomech. 2004, 3, 1–4.
  18. McCrory, P.F. Interdisciplinary Research in Sports Medicine. Br. J. Sports Med. 2006, 40, 259–260.
  19. Stone, M.S. A Call for Interdisciplinary Research in Sports Science. J. Strength. Cond. Res. 2008, 22, 3–4.
  20. Timmons, B.W.; Steadward, R.D. The Need for Interdisciplinary Research in Sports Science. Appl. Physiol. Nutr. Metab. 2010, 35, 1–3.
  21. Lemmink, K.A.P.M. Interdisciplinary Approaches in Sports Science Research: From Theory to Practice. Eur. J. Sport Sci. 2015, 15, 181–182.
  22. Delecroix, M.; McCall, A.; Dawson, B.; Winwood, P. Interdisciplinary Approach to the Design of Effective Resistance Training in Team Sports. Strength. Cond. J. 2018, 40, 17–24.
  23. Fagher, K.; Forsberg, A.; Jacobsson, J.; Timpka, T. Interdisciplinary Research is Needed to Improve Injury Prevention and Rehabilitation in Sports. J. Orthop. Sports Phys. Ther. 2019, 49, 476–478.
  24. Hennessey, T.; Kilty, J. Embracing Complexity: Interdisciplinary Perspectives in Sports Science Research. J. Sports Sci. 2020, 38, 2073–2074.
  25. Lupo, C.; Ungureanu, A.N.; Varalda, M.; Cortis, C. Editorial: Advances in Interdisciplinary Approaches in Sports Science Research. Front. Psychol. 2020, 11, 2096.
  26. Schwesig, R.; Struder, H.K.; Faude, O. Need for Interdisciplinary Sports Science Research in the era of COVID-19. Eur. J. Sport Sci. 2021, 21, 248–251.
  27. Reid, C.; Stewart, E.; Thorne, G. Multidisciplinary Sport Science Teams in Elite Sport: Comprehensive Servicing or Conflict and Confusion? Sport Psychol. 2004, 18, 204–217.
  28. Banister, E.W.; Calvert, T.W.; Savage, M.V.; Bach, T.M. A Systems Model of Training for Athletic Performance. Aust. J. Sports Med. 1975, 7, 57–61.
  29. Wang, T.; Lin, Z.; Day, R.E.; Gardiner, B.; Landao-Bassonga, E.; Rubenson, J.; Kirk, T.B.; Smith, D.W.; Lloyd, D.G.; Hardisty, G.; et al. Programmable Mechanical Stimulation Influences Tendon Homeostasis in a Bioreactor System. Biotechnol. Bioeng. 2012, 110, 1495–1507.
  30. Bompa, T.O.; Buzzichelli, C. Periodization Training for Sports, 3rd ed.; Human Kinetics: Champaign, IL, USA, 2015.
  31. Impellizzeri, F.M.; Rampinini, E.; Coutts, A.J.; Sassi, A.; Marcora, S.M. Use of RPE-Based Training Load in Soccer. Med. Sci. Sports Exerc. 2004, 36, 1042–1047.
  32. Impellizzeri, F.M.; Marcora, S.M.; Coutts, A.J. Internal and External Training Load: 15 Years On. Int. J. Sports Physiol. Perform. 2019, 14, 270–273.
  33. Impellizzeri, F.M.; Rampinini, E.; Marcora, S.M. Physiological Assessment of Aerobic Training in Soccer. J. Sports Sci. 2005, 23, 583–592.
  34. Impellizzeri, F.M.; Jeffries, A.C.; Weisman, A.; Coutts, A.C.; McCall, A.; McLaren, S.J.; Kalkhoven, J.T. A Clarification of the (Mis)use of the Term ‘Load’ in Sport and Exercise Science: Why it is Appropriate and Scientific. SportRxiv 2021.
  35. Impellizzeri, F.M.; Shrier, I.; McLaren, S.J.; Coutts, A.J.; McCall, A.; Slattery, K.; Jeffries, A.C.; Kalkhoven, J.T. Understanding Training Load as Exposure and Dose. Sports Med. 2023, 53, 1667–1679.
  36. Verheul, J.; Nedergaard, N.J.; Vanrenterghem, J.; Robinson, M.A. Measuring Biomechanical Loads in Team Sports—From Lab to Field. Sci. Med. Footb. 2020, 4, 246–252.
  37. Malone, J.J.; Lovell, R.; Varley, M.C.; Coutts, A.J. Unpacking the Black Box: Applications and Considerations for Using GPS Devices in Sport. Int. J. Sports Physiol. Perform. 2017, 12, S2-18–S2-26.
  38. Jackson, B.M.; Polglaze, T.; Dawson, B.; King, T.; Peeling, P. Comparing Global Positioning System and Global Navigation Satellite System Measures of Team-Sport Movements. Int. J. Sports Physiol. Perform. 2018, 13, 1005–1010.
  39. Mannini, A.; Sabatini, A.M. Machine Learning Methods for Classifying Human Physical Activity from On-Body Accelerometers. Sensors 2010, 10, 1154–1175.
  40. Chambers, R.; Gabbett, T.J.; Cole, M.H.; Beard, A. The Use of Wearable Microsensors to Quantify Sport-Specific Movements. Sports Med. 2015, 45, 1065–1081.
  41. Ghosh, I.; Ramamurthy, S.R.; Chakma, A.; Roy, N. DeCoach: Deep Learning-based Coaching for Badminton Player Assessment. Pervasive Mob. Comput. 2022, 83, 101608.
  42. Perri, T.; Reid, M.; Murphy, A.; Howle, K.; Duffield, R. Validating an algorithm from a trunk-mounted wearable sensor for detecting stroke events in tennis. J. Sports Sci. 2022, 40, 1168–1174.
  43. Beaupré, G.S.; Orr, T.E.; Carter, D.R. An approach for time-dependent bone modeling and remodeling—Application: A preliminary remodeling simulation. J. Orthop. Res. 1990, 8, 662–670.
  44. Ahola, R.; Korpelainen, R.; Vainionpää, A.; Jämsä, T. Daily impact score in long-term acceleration measurements of exercise. J. Biomech. 2010, 43, 1960–1964.
  45. Burland, J.P.; Outerleys, J.B.; Lattermann, C.; Davis, I.S. Reliability of wearable sensors to assess impact metrics during sport-specific tasks. J. Sports Sci. 2021, 39, 406–411.
  46. Armitage, M.; Beato, M.; McErlain-Naylor, S.A. Inter-unit reliability of IMU Step metrics using IMeasureU Blue Trident inertial measurement units for running-based team sport tasks. J. Sports Sci. 2021, 39, 1512–1518.
  47. Shorten, M.R.; Winslow, D.S. Spectral Analysis of Impact Shock during Running. Int. J. Sport Biomech. 1992, 8, 288–304.
  48. Zandbergen, M.A.; Ter Wengel, X.J.; van Middelaar, R.P.; Buurke, J.H.; Veltink, P.H.; Reenalda, J. Peak tibial acceleration should not be used as indicator of tibial bone loading during running. Sports Biomech. 2023, 1–18.
  49. Hughes, G.T.G.; Camomilla, V.; Vanwanseele, B.; Harrison, A.J.; Fong, D.T.P.; Bradshaw, E.J. Novel technology in sports biomechanics: Some words of caution. Sports Biomech. 2021, 1–9.
  50. Preatoni, E.; Bergamini, E.; Fantozzi, S.; Giraud, L.I.; Orejel Bustos, A.S.; Vannozzi, G.; Camomilla, V. The Use of Wearable Sensors for Preventing, Assessing, and Informing Recovery from Sport-Related Musculoskeletal Injuries: A Systematic Scoping Review. Sensors 2022, 22, 3225.
  51. Santos-Gago, J.M.; Ramos-Merino, M.; Vallarades-Rodriguez, S.; Álvarez-Sabucedo, L.M.; Fernández-Iglesias, M.J.; García-Soidán, J.L. Innovative Use of Wrist-Worn Wearable Devices in the Sports Domain: A Systematic Review. Electronics 2019, 8, 1257.
  52. Cosoli, G.; Spinsante, S.; Scalise, L. Wrist-worn and chest-strap wearable devices: Systematic review on accuracy and metrological characteristics. Measurement 2020, 159, 107789.
  53. Passos, J.; Lopes, S.I.; Clemente, F.M.; Moreira, P.M.; Rico-González, M.; Bezerra, P.; Rodrigues, L.P. Wearables and Internet of Things (IoT) Technologies for Fitness Assessment: A Systematic Review. Sensors 2021, 21, 5418.
  54. Bent, B.; Goldstein, B.A.; Kibbe, W.A.; Dunn, J.P. Investigating sources of inaccuracy in wearable optical heart rate sensors. NPJ Digit. Med. 2020, 3, 18.
  55. Cosoli, G.; Antognoli, L.; Veroli, V.; Scalise, L. Accuracy and Precision of Wearable Devices for Real-Time Monitoring of Swimming Athletes. Sensors 2022, 22, 4726.
  56. Lopez, G.; Abe, S.; Hashimoto, K.; Yokokubo, A. On-Site Personal Sport Skill Improvement Support Using Only a Smartwatch. In Proceedings of the 2019 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops), Kyoto, Japan, 11–15 March 2019; pp. 158–164.
  57. Whoop. Available online: (accessed on 9 December 2023).
  58. Weippert, M.; Kumar, M.; Kreuzfeld, S.; Arndt, D.; Rieger, A.; Stoll, R. Comparison of three mobile devices for measuring R-R intervals and heart rate variability: Polar S810i Suunto t6 and an ambulatory ECG system. Eur. J. Appl. Physiol. 2010, 109, 779–786.
  59. Camomilla, V.; Bergamini, E.; Fantozzi, S.; Vannozzi, G. Trends supporting the in-field use of wearable inertial sensors for sport performance evaluation: A systematic review. Sensors 2018, 18, 873.
  60. Wang, J.; Wang, Z.; Gao, F.; Zhao, H.; Qiu, S.; Li, J. Swimming Stroke Phase Segmentation Based on Wearable Motion Capture Technique. IEEE Trans. Instrum. Meas. 2020, 69, 8526–8538.
  61. Incus Performance. Available online: (accessed on 9 April 2023).
  62. Marco, B.; Giuseppe, C.; Adam, S.; Dello, I.A. The Validity and Between-Unit Variability of GNSS Units (STATSports Apex 10 and 18 Hz) for Measuring Distance and Peak Speed in Team Sports. Front. Physiol. 2018, 9, 1288.
  63. James, R.; Mark, C.; Mikael, J.; Marco, B. Quantifying and Comparing the Match Demands of U18, U23, and 1ST Team English Professional Soccer Players. Front. Physiol. 2021, 12, 706451.
  64. Long, C.; Jo, E.; Nam, Y. Development of a yoga posture coaching system using an interactive display based on transfer learning. J. Supercomput. 2022, 78, 5269–5284.
  65. Komondo Technologies, Inc. Available online: (accessed on 9 December 2023).
  66. Fleisig, G.S.; Laughlin, W.A.; Aune, K.T.; Cain, E.L.; Dugas, J.R.; Andrews, J.R. Differences among fastball, curveball, and change-up pitching biomechanics across various levels of baseball. Sports Biomech. 2016, 15, 128–138.
  67. Myontec Ltd. Available online: (accessed on 9 December 2023).
  68. Pihlainen, K.; Pesola, A.J.; Helén, J.; Häkkinen, K.; Finni, T.; Ojanen, T.; Vaara, J.P.; Santtila, M.; Raitanen, J.; Kyröläinen, H. Training-Induced Acute Neuromuscular Responses to Military Specific Test during a Six-Month Military Operation. Int. J. Environ. Res. Public Health 2021, 18, 215.
  69. Vieira, D.; Carvalho, H.; Providência, B. E-Textiles for Sports: A Systematic Review. JBBBE 2022, 57, 37–46.
  70. Zeng, Z.; Liu, Y.; Hu, X.; Meihua, T.; Wang, L. Validity and reliability of inertial measurement units on lower extremity kinematics during running: A systematic review and meta-analysis. Sports Med. Open 2022, 8, 86.
  71. Yeadon, M.R.; Pain, M.T.G. Fifty years of performance-related sports biomechanics research. J. Biomech. 2023, 155, 111666.
  72. Dahl, K.D.; Dunford, K.M.; Wilson, S.A.; Turnbull, T.L.; Tashman, S. Wearable sensor validation of sports-related movements for the lower extremity and trunk. Med. Eng. Phys. 2020, 84, 144–150.
  73. Jenkins, L.; Weerasekera, R. Sport-related back injury prevention with a wearable device. Biosens. Bioelectron. X 2022, 11, 100202.
  74. Paul, G.; Torah, R.; Beeby, S.; Tudor, J. Novel active electrodes for ECG monitoring on woven textiles fabricated by screen and stencil printing. Sens. Actuator A Phys. 2015, 221, 60–66.
  75. Ferri, J.; Llinares, R.; Segarra, I.; Cebrián, A.; Garcia-Breijo, E.; Millet, J. A new method for manufacturing dry electrodes on textiles. Validation for wearable ECG monitoring. Electrochem. Commun. 2022, 136, 107244.
  76. Nigusse, A.B.; Malengier, B.; Mengistie, D.A.; Van Langenhove, L. A washable silver-printed textile electrode for ECG monitoring. Eng. Proc. 2021, 6, 63.
  77. Shathi, M.A.; Chen, M.; Khoso, N.A.; Rahman, M.T.; Bhattacharjee, B. Graphene coated textile based highly flexible and washable sports bra for human health monitoring. Mater. Des. 2020, 193, 108792.
  78. Trindade, I.G.; Martins, F.; Baptista, P. High electrical conductance poly(3,4-ethylenedioxythiophene) coatings on textile for electrocardiogram monitoring. Synth. Met. 2015, 210 Part B, 179–185.
  79. Meghrazi, M.A.; Tian, Y.; Mahnam, A.; Bhattachan, P.; Eskandarian, L.; Kakhki, S.T.; Popovic, M.R.; Lankarany, M. Multichannel ECG recording from waist using textile sensors. BioMed. Eng. OnLine 2020, 19, 48.
  80. An, X.; Stylios, G.K. A Hybrid Textile Electrode for Electrocardiogram (ECG) Measurement and Motion Tracking. Materials 2018, 11, 1887.
  81. Arquilla, K.; Webb, A.K.; Anderson, A.P. Woven electrocardiogram (ECG) electrodes for health monitoring in operational environments. In Proceedings of the 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), Montreal, QC, Canada, 20–24 July 2020; pp. 4498–4501.
  82. Brehm, P.J.; Anderson, A.P. Modeling the Design Characteristics of Woven Textile Electrodes for long—Term ECG Monitoring. Sensors 2023, 23, 598.
  83. Bystricky, T.; Moravcova, D.; Kaspar, P.; Soukup, R.; Hamacek, A. A comparison of embroidered and woven textile electrodes for continuous measurement of ECG. In Proceedings of the 39th International Spring Seminar on Electronics Technology (ISSE), Pilsen, Czech Republic, 18–22 May 2016; pp. 7–11.
  84. Nigusse, A.B.; Malengier, B.; Mengistie, D.A.; Maru, A.; Tseghai, G.B.; Van Langenhove, L. Embroidered Textiles Electordes for Long-Term ECG Monitoring. In Proceedings of the 8th International Conference on Intelligent Textiles & Mass Customisation, Montreal, QC, Canada, 18–21 September 2022; p. 012002.
  85. Zhao, J.; Deng, J.; Liang, W.; Zhao, L.; Dong, Y.; Wang, X.; Lin, L. Water-retentive, 3D knitted textile electrode for long-term and motion state bioelectrical signal acquisition. Compos. Sci. Technol. 2022, 227, 109606.
  86. Alshabouna, F.; Lee, H.S.; Barandun, G.; Tan, E.; Cotur, Y.; Asfour, T.; Gonzalez-Macia, L.; Coatsworth, P.; Núnez-Bajo, E.; Kim, J.S.; et al. PEDOT:PSS-modified cotton conductive thread for mass manufacturing of textile-based electrical wearable sensors by computerized embroidery. Mater. Today 2022, 59, 56–67.
Contributors MDPI registered users' name will be linked to their SciProfiles pages. To register with us, please refer to : , , , ,
View Times: 239
Revisions: 2 times (View History)
Update Date: 26 Feb 2024