Trends and Future Challenges

While there are increasing development and research in wearable sensor technology, there are some improvement opportunities to expand biosensors’ capabilities and applications. Accuracy, privacy, security, accessibility, cost, compatibility, acceptability, data interpretation, lack of standards, and scientific peer-reviewed evidence for safety and efficacy are some of the most necessary characteristics to assess and research more in-depth (Dunn et al., 2018). Other future challenges are categorized in hardware design (e.g., power consumption, fault detection) (Cvetković et al., 2018), ergonomics (e.g., attachment, placement, size, versatility) (Zheng et al., 2014), network challenges (e.g., data security, topology, routing algorithms) (Mitra et al., 2012), data fusion opportunities relies on data manipulation (e.g., filtering, classification, computational complexity, and feature extraction) (Gravina et al., 2017).

Wearable devices for heart rate quantification and other motion variables have a promising future in quantifying internal and external load variables. The latest research has innovated certain features that will soon be a reality. These sensors have been incorporated with capabilities to assess heart rate throughout non-contact registering.

Concerning the integration of internal and external load data integration, the main challenge is to improve the accuracy, reliability, and validity of energy expenditure calculations based on both heart rate and mechanical sensors using wearable devices (Cvetković et al., 2018).

Besides, hand in hand with the development of new heart rate and motion sensors and the capture, processing, and analysis of novel methods, in some cases in real-time, allows registering of a large amount of information. The collected data can be up to a thousand data per second in an amount of up to 100–1000 variables (Bonomi, 2013). This means a significant challenge when analyzing and interpreting a large amount of information effectively and efficiently so that it is available promptly (Rojas-Valverde et al., 2019).

These data sets or combinations of data sets obtained related to heart rate and motion variables

whose variability, complexity, volume, and speed of growth hinder their capture, processing,

management, or analysis using conventional technologies and tools is called big data. This

information requires new machine learning techniques and data mining methods to manage and

report biosensors device data in sport properly.

Lessons Learned and Concluding Remarks

Real-time monitoring of combined heart rate and movement sensors is presented as a reliable and accurate option to register both internal and external load. Using this physical and physiological information, stakeholders in exercise and sport science will have a broader perspective of training loads in real-time.

Some facilities make these devices accessible to technology. Due to size and weight

characteristics, it can be worn freely on any relevant part of the body. Manufacturers usually use

different fusion approaches data and features of multiple modalities to register energy expenditure

to combine both internal and external load variables.

References

Achten, J., & Jeukendrup, A. E. (2003). Heart rate monitoring. Sports Medicine, 33(7), 517–538.

doi:10.2165/00007256-200333070-00004

Bandodkar, A. J., & Wang, J. (2014). Non-invasive wearable electrochemical sensors: A review. Trends in Biotechnology, 32(7), 363–371. doi:10.1016/j.tibtech.2014.04.005

Bonomi, A. G. (2013). Towards valid estimates of activity energy expenditure using an accelerometer:

Searching for a proper analytical strategy and big data. Journal of Applied Physiology, 115(9), 1227–1228.

doi:10.1152/japplphysiol.01028.2013

Currell, K., & Jeukendrup, A. E. (2008). Validity, reliability and sensitivity of measures of sporting performance. Sports Medicine (Auckland, N.Z.), 38(4), 297–316. doi:10.2165/00007256-200838040-00003 Cvetković, B., Szeklicki, R., Janko, V., Lutomski, P., & Luštrek, M. (2018). Real-time activity monitoring with a

wristband and a smartphone. Information Fusion, 43, 77–93. doi:10.1016/j.inffus.2017.05.004

Dunn, J., Runge, R., & Snyder, M. (2018). Wearables and the medical revolution. Personalized Medicine, 15(5), 429–448. doi:10.2217/pme-2018-0044

Engström, E., Ottosson, E., Wohlfart, B., Grundström, N., & Wisén, A. (2012). Comparison of heart rate measured by Polar RS400 and ECG, validity and repeatability. Advances in Physiotherapy, 14(3), 115–122.

doi:10.3109/14038196.2012.694118

Eston, R. G., Rowlands, A. V., & Ingledew, D. K. (1998). Validity of heart rate, pedometry, and accelerometry for predicting the energy cost of children’s activities. Journal of Applied Physiology, 84(1), 362–371.

doi:10.1152/jappl.1998.84.1.362

Etiwy, M., Akhrass, Z., Gillinov, L., Alashi, A., Wang, R., Blackburn, G., Gillinov, S. M., Phelan, D., Gillinov, A. M., Houghtaling, P. L., Javadikasgari, H., & Desai, M. Y. (2019). Accuracy of wearable heart rate monitors in cardiac rehabilitation. Cardiovascular Diagnosis and Therapy, 9(3), 262–271. doi:10.21037/cdt.2019.04.08 Foster, C., Florhaug, J. A., Franklin, J., Gottschall, L., Hrovatin, L. A., Parker, S., Doleshal, P., & Dodge, C.

(2001). A new approach to monitoring exercise training. Journal of Strength and Conditioning Research.

doi:10.1519/1533-4287(2001)015<0109:ANATME>2.0.CO;2

Gómez-Carmona, C. D., Bastida Castillo, A., Rojas-Valverde, D., de la Cruz Sánchez, E., García-Rubio, J., Ibáñez, S. J., & Pino-Ortega, J. (2020). Lower-limb dynamics of muscle oxygen saturation during the back- squat exercise: Effects of training load and effort level. The Journal of Strength & Conditioning Research, 34(5), 1227–1236.

Gravina, R., Alinia, P., Ghasemzadeh, H., & Fortino, G. (2017). Multi-sensor fusion in body sensor networks:

State-of-the-art and research challenges. Information Fusion, 35, 68–80. doi:10.1016/j.inffus.2016.09.005 Gutiérrez-Vargas, R., Gutiérrez-Vargas, J. C., Ugalde-Ramírez, J. A., & Rojas-Valverde, D. (2018). Kinematics

and thermal sex responses during an official beach handball game in Costa Rica: A pilot study. Archivos de Medicina Del Deporte, 36(1), 7–14.

Impellizzeri, F. M., Marcora, S. M., & Coutts, A. J. (2019). Internal and external training load: 15 Years on.

International Journal of Sports Physiology and Performance, 14(2), 270–273. doi:10.1123/ijspp.2018-0935 Lai, X., Liu, Q., Wei, X., Wang, W., Zhou, G., & Han, G. (2013). A survey of body sensor networks. Sensors,

13(5), 5406–5447. doi:10.3390/s130505406

Laukkanen, R. M. T., & Virtanen, P. K. (1998). Heart rate monitors: State of the art. Journal of Sports Sciences, 16(sup1), 3–7. doi:10.1080/026404198366920

Lee, C. M., & Gorelick, M. (2011). Validity of the smarthealth watch to measure heart rate during rest and exercise. Measurement in Physical Education and Exercise Science, 15(1), 18–25.

doi:10.1080/1091367X.2011.539089

Léger, L., & Thivierge, M. (1988). Heart rate monitors: Validity, stability, and functionality. The Physician and Sportsmedicine, 16(5), 143–151. doi:10.1080/00913847.1988.11709511

Li, G., & Wen, D. (2020). Wearable biochemical sensors for human health monitoring: Sensing materials and manufacturing technologies. Journal of Materials Chemistry B, 8(16), 3423–3436. doi:10.1039/C9TB02474C McLaren, S. J., Macpherson, T. W., Coutts, A. J., Hurst, C., Spears, I. R., & Weston, M. (2018). The

relationships between internal and external measures of training load and intensity in team sports: A meta- analysis. Sports Medicine (Auckland, N.Z.), 48(3), 641–658. doi:10.1007/s40279-017-0830-z

Meijer, G. A., Westerterp, K. R., Koper, H., & ten Hoor, F. (1989). Assessment of energy expenditure by recording heart rate and body acceleration. Medicine and Science in Sports and Exercise, 21(3), 343–347.

Mitra, U., Emken, B. A., Lee, S., Li, M., Rozgic, V., Thatte, G., Vathsangam, H., Zois, D., Annavaram, M., Narayanan, S., Levorato, M., Spruijt-Metz, D., & Sukhatme, G. (2012). KNOWME: A case study in wireless body area sensor network design. IEEE Communications Magazine, 50(5), 116–125.

doi:10.1109/MCOM.2012.6194391

Navalta, J. W., Montes, J., Bodell, N. G., Salatto, R. W., Manning, J. W., & DeBeliso, M. (2020). Concurrent heart rate validity of wearable technology devices during trail running. PLoS One, 15(8), e0238569.

doi:10.1371/journal.pone.0238569

Pasadyn, S. R., Soudan, M., Gillinov, M., Houghtaling, P., Phelan, D., Gillinov, N., Bittel, B., & Desai, M. Y.

(2019). Accuracy of commercially available heart rate monitors in athletes: A prospective study.

Cardiovascular Diagnosis and Therapy, 9(4), 379–385. doi:10.21037/cdt.2019.06.05

Paulson, T. A. W., Mason, B., Rhodes, J., & Goosey-Tolfrey, V. L. (2015). Individualized internal and external training load relationships in elite wheelchair rugby players. Frontiers in Physiology, 6.

doi:10.3389/fphys.2015.00388

Rahman, H., Ahmed, M., Begum, S., & Funk, P. (2016). Real Time Heart Rate Monitoring from Facial RGB Color Video Using Webcam.

Rojas-Valverde, D., Gómez-Carmona, C. D., Gutiérrez-Vargas, R., & Pino-Ortega, J. (2019). From big data mining to technical sport reports: The case of inertial measurement units. BMJ Open Sport & Exercise Medicine, 5(1). doi:10.1136/bmjsem-2019-000565

Rossi, A., Perri, E., Pappalardo, L., Cintia, P., & Iaia, F. M. (2019). Relationship between external and internal workloads in elite soccer players: Comparison between rate of perceived exertion and training load. Applied Sciences, 9(23), 5174. doi:10.3390/app9235174

Seshadri, D. R., Li, R. T., Voos, J. E., Rowbottom, J. R., Alfes, C. M., Zorman, C. A., & Drummond, C. K.

(2019). Wearable sensors for monitoring the physiological and biochemical profile of the athlete. Npj Digital Medicine, 2(1), 1–16. doi:10.1038/s41746-019-0150-9

Sharma, P., Hui, X., Zhou, J., Conroy, T. B., & Kan, E. C. (2020). Wearable radio-frequency sensing of respiratory rate, respiratory volume, and heart rate. Npj Digital Medicine, 3(1), 1–10. doi:10.1038/s41746- 020-0307-6

Stahl, S. E., An, H.-S., Dinkel, D. M., Noble, J. M., & Lee, J.-M. (2016). How accurate are the wrist-based heart rate monitors during walking and running activities? Are they accurate enough? BMJ Open Sport & Exercise Medicine, 2(1), e000106. doi:10.1136/bmjsem-2015-000106

Terbizan, D. J., Dolezal, B. A., & Albano, C. (2002). Validity of seven commercially available heart rate monitors. Measurement in Physical Education and Exercise Science, 6(4), 243–247.

doi:10.1207/S15327841MPEE0604_3

Wei, J. (2014). How wearables intersect with the cloud and the internet of things: Considerations for the developers of wearables. IEEE Consumer Electronics Magazine, 3(3), 53–56.

doi:10.1109/MCE.2014.2317895

Zhang, Q., Xu, G., Wang, M., Zhou, Y., & Feng, W. (2014). Webcam based non-contact real-time monitoring for the physiological parameters of drivers. The 4th Annual IEEE International Conference on Cyber Technology in Automation, Control and Intelligent, 648–652. doi:10.1109/CYBER.2014.6917541

Zhang, Y., Weaver, R. G., Armstrong, B., Burkart, S., Zhang, S., & Beets, M. W. (2020). Validity of Wrist-Worn photoplethysmography devices to measure heart rate: A systematic review and meta-analysis. Journal of Sports Sciences, 38(17), 2021–2034. doi:10.1080/02640414.2020.1767348

Zheng, Y., Ding, X., Poon, C. C. Y., Lo, B. P. L., Zhang, H., Zhou, X., Yang, G., Zhao, N., & Zhang, Y. (2014).

Unobtrusive sensing and wearable devices for health informatics. IEEE Transactions on Biomedical Engineering, 61(5), 1538–1554. doi:10.1109/TBME.2014.2309951

Part II

Data Transmission

7 Data Acquisition and Transmission

Amaia Méndez Zorrilla, Iñigo Orue Saiz, Aritz Badiola

Bengoa and Julen Badiola Martínez

Introduction

The advances and cheapening of technology, both hardware and software, has caused its foray into fields of application unthinkable years ago. These areas include sports and, more specifically, team sports.

For many decades, the recording of matches for later analysis and visualization was a common practice for the improvement of individual and group training techniques. Subsequently, digital image processing techniques allowed to automate part of the analysis, but presented problems with overlapping players, dress colors and changes in light and shadow conditions.

In relation to the previous problems, it should be noted that many of the tracking and movement analysis systems are composed of two cameras (Pers & Kovacic, 2000) that cover the entire playing field, leading into either a distorted vision (Vieira et al., 2017) due to the types of cameras used (wide angle cameras, fisheye lens), or a difficulty in interpreting images correctly, due to the distance with the elements to be tracked.

On the other hand, the use of a single camera is usually ruled out due to the large amount of ground to be covered, and the main reasons for choosing only two cameras are the price (since the fewer cameras, the lower the cost) and the complexity of the system, since greater calibration as well as a method for joint interpretation of the data obtained (such as the interpretation of overlapping data, etc.) would be needed.

This standard involving use of two cameras to cover the entire playing field over time has been changing, with use being made of camera arrays located at different points, ranging from those located in the same area that open the angle up to the entire field (Halvorsen et al., 2013) to cameras spread over different points (Liang et al., 2020).

Due to the problems mentioned, Electronic Performance and Tracking Systems (from now on referred to as EPTS) have become the standard systems used to position team sports players.

These technologies are used to monitor and improve individual and

group performance. Their purpose is to record players’ positions (and the

ball or equivalent) and, in combination with other data captured by other

devices (accelerometers, gyroscopes or pulsometers), may provide very

useful information to enable coaching staff to improve training practice.

Positioning systems started to be developed in the 1990s with the Global Positioning System (GPS), which offered the chance for real-time tracking of human movement. But it was not until the 2000s when scientists began to see the possibilities offered by the application of location and positioning systems in sport. With them, they were able to take measurements such as distance traveled, as well as the length and distance of sprints performed by athletes in specific sports.

The first team sports in which GPS began to be used were soccer and rugby, these being sports generally played on open fields (all of them popular in different countries). In fact, the use of GPS is limited to outdoors, as GPS systems are unable to track movement patterns indoors.

However, the wealth of data obtained and the efficiency of its use, has led to the development of a system capable of positioning the player indoors.

Thus, and by way of a response to GPS limitations, the Local Positioning System (LPS) was born, this being a new GPS system used in sports such as basketball and hockey.