The field of medicine is rapidly adapting to the onset of the digital age. As the digitalization of all areas of medicine continues to progress, so do the potential applications in sports medicine. Personalized medicine has been on the rise for the past decade, as new technologies allow for innovation in individualized treatment. Digitalization of sports medicine offers an opportunity for real-time feedback that could enhance and specialize treatment and rehabilitation. (1

Wearable devices that monitor movement are becoming commonplace in the field of sports medicine. Being able to evaluate specific parameters related to performance, such as acceleration of players during attack maneuvers, is invaluable for both individual and team sports. (2,3) Digital sensors can be used to evaluate individual and team performance, as well as for rehabilitation and sports injury prevention. Sensors can detect signs of exhaustion and fatigue which can be utilized to prevent injuries or overexertion. Physiotherapists and trainers can use these data collected from individual players to tailor exercises and training programs. (3) Esurgi is currently developing one such wearable device called the Joint Spy, which is a knee sleeve that provides real-time monitoring and feedback on knee joint motion. It can identify high risk movements and help the wearer train safer movement patterns during high intensity sports, preventing injury. Studies have already shown that detection of biochemical factors, mental alertness, and physiological status reduces injury risk and improves athlete performance. (4)

Artificial intelligence (AI) is often associated with the digitalization of medicine. AI provides an opportunity for faster, more effective analysis of large data pools, such as the ones obtained from wearable sports monitoring devices. (5) A human trainer or physiotherapist can only analyze a limited amount of data single handedly, but a computer can pour through thousands of data points in seconds. Artificial neural networks (ANN) allow computers to “learn” from previous experience, or inputs, and make highly effective analyses of biodata. (5) ANN can recognize patterns in movement and deviations from optimal performance in sports. It can be used in tandem with digitized sports monitoring systems to provide feedback and improve training and sports performance. ANN can also recognize movement patterns that suggest injury or potential for injury which can be used as a method of injury prevention. (6) A study using ANN in college football determined which factors contribute most to knee injuries which is vital to injury prevention. (7) Another study used ANN to evaluate technical skills and performance in youth soccer players. The study indicated that ANN prediction methods strongly predicted player performance. These techniques can lower costs and time necessary to evaluate players and can define the optimal parameters necessary to predict performance quality. (8) In combination with the digitalization of sports medicine treatments and therapies, AI is the future of large scale data analysis and sports team monitoring; it can revolutionize sports training, rehabilitation, and injury prevention while improving team cohesiveness and performance.


1. Hecksteden, A., & Meyer, T. (2018). Personalized Sports Medicine – Principles and tailored implementations in preventive and competitive sports. Deutsche Zeitschrift Für Sportmedizin, 2018(03), 73-80.

2. Badau, D., Camarda, A., Serbanoiu, S., Virgil, T., Bondoc-Ionescu, D., & Badau, A. (2010). Performance management in sports for all. INTERNATIONAL JOURNAL OF EDUCATION AND INFORMATION TECHNOLOGIES, 4(2).

3. Rigamonti, L., Albrecht, U., Lutter, C., Tempel, M., Wolfarth, B., & Back, D. A. (2020). Potentials of Digitalization in Sports Medicine. Current Sports Medicine Reports, 19(4), 157-163.

4. 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 internal and external workload of the athlete. Npj Digital Medicine, 2(1).

5. Bartlett R. (2006). Artificial intelligence in sports biomechanics: new dawn or false hope?. Journal of sports science & medicine, 5(4), 474–479.

6. Barfod, K. W. (2019). Digitalization and Machine Learning. The Future of Orthopaedic Sports Medicine, 113-114. 

7. Qilin, S., Xiaomei, W., Xiaoling, F., Yuanping, C., & Shaoyong, W. (2016). Study on knee joint injury in college football training based on artificial neural network. RISTI, (E10).

8. Abdullah, M. R., Maliki, A. H., Musa, R. M., Kosni, N. A., & Juahir, H. (2016). Intelligent Prediction of Soccer Technical Skill on Youth Soccer Player’s Relative Performance Using Multivariate Analysis and Artificial Neural Network Techniques. International Journal on Advanced Science Engineering Information Technology, 6(5).

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