# Review on Wearable Technology in Sports: Concepts, Challenges and Opportunities
## 7 Applications of Wearables in Sports
### Measurement and Monitoring of Athletic Performance  
The performance of an athlete depends on their technical, tactical, physiological, and psychological/social characteristics. To design an effective training program, it is important to be aware of the physical demands of the sport, the athlete's capacity that can be determined through various tests, and the different components of conditioning training [136]. Proper training programs can enhance athlete performance and reduce the risk of injuries. Therefore, monitoring both training and competition loads is important in making more objective decisions when designing training programs, reducing the risk of injury for athletes, and improving their performance [137]. Considering the relationship between injuries and performance in athletes, monitoring athletes is a crucial element in sports that require high performance. There are various monitoring tools available to track an athlete's training load, their response to that load, and their relative fitness or fatigue status [138]. By collecting data from athletes, it is possible to identify their strengths and weaknesses and monitor progress toward improving performance or providing information for proper training programs. However, there are challenges in measuring training load, especially in quickly assessing a large number of athletes in laboratory settings, and providing coaches with the necessary information for the development of athletic performance [139]. For these reasons, sports teams have recently started using wearable sensors to provide a measurable indicator of the \"workload\" of athletes [140]. In the context of sports training, training load is defined as the manipulated input variable to elicit the desired training response [141]. Relative biological (both physiological and psychological) stressors experienced by an athlete during training or competition, such as cardiac load, blood lactate, oxygen consumption, and perceived exertion levels (RPE), are defined as internal training loads [137]. Tracking changes in internal load is used to optimize training load management and determine training adaptations. Metrics that objectively measure the work performed by an athlete during training or competition and are evaluated independently of internal loads (e.g., speed, distance covered, body load, acceleration, metabolic power, shots taken, or interventions made) are defined as external training loads [137]. The accumulation of training stress and resulting fatigue without adequate recovery can increase the risk of an unfavorable training response and injuries in players. Therefore, monitoring training loads allows coaches and clinicians to assess fatigue and physical demands in real-time, reduce the risk of injury, and optimize performance. For example, WTs incorporating GNSS and triaxial accelerometers can be used to track real-time external training load in athletes. The total running distance and intensity of sprints or collisions can be determined using GNSS sensors [100]. Studies have shown that GNSS data is used in sports such as team sports (Australian Football, Rugby, National Football League, National Hockey League) [142] and swimming [49] to integrate the physical capacity of athletes with game-specific skills, tactics, or strategic information, as well as for injury prevention. Today, WTs designed to measure performance metrics have become accessible throughout the sports community. Monitoring an athlete's training load is seen as important by many to determine their adherence to the training program and minimize the risk of non-functional overreaching (weeks to months of fatigue), injury, and illness [143]. Therefore, monitoring training and competition loads regularly is essential for athletes to achieve optimal athletic performance. Sports teams have a long history of using data to evaluate players. Today, advanced devices equipped with sensors can be worn by players to directly collect data [144].