## 8 Discussion
Finally, those wearable devices often employ ambiguous terminology, such as \"bone load\", \"limb load\", \"step intensity\", \"impact score\", or \" biomechanical load\", which could be confusing and misleading for end-users [72]. As a result, biofeedback from existing wearable devices may be ambiguous, not validated and potentially harmful for users [72]. Yet, the estimation of the full GRF waveforms from which well-known and standardized loading rate variables (i.e., impact peak, active peak, loading rate, and impulse) could be accurately extrapolated, may represent a viable solution to aid in the decision-making of coaches and sport scientists. In this direction, the system and prototypes developed and described in this paper, encompasses the design and integration of a performance-evaluation system able to reliably support coaches and athletes in their real-world practice in a number of sports, such as soccer, rugby, etc. The developed system meets the requirements set by the targeted task in terms of low-power, sampling rate (>200 Hz), wireless synchronization, activity detection, accuracy, number of metrics provided, and graphical visualization. Future work will investigate the possibility to adopt the developed system in agonistic settings, such as a real soccer match, and collect appropriate feedback on its usefulness from actual professional end-users. The developed system, however, presents some limitations. The implemented activity and event recognition algorithms are specific for the considered case study, and may not be suitable for recognizing running and jumping from other activities (e.g., walking, sprinting). The adoption of machine learning-based human activity recognition approaches [73; 74; 75] will be considered for future developments, as well as possible improvements (such as the use of machine learning models) related to the estimation of specific jumping-related metrics (e.g., depth). Moreover, this study did not consider the impact that different running surfaces or running shoes may have on the gait of the athletes [76] and, consequently, on the system performance. Further field-testing is required to investigate the effect of the running surface, as well as runners' fatigue, on the developed system. Finally, the enhancement of the Device1-Device2 wireless link reliability via automatic adjustment of communication factors (i.e., packet size, sampling rate) [77] for a more robust synchronization will also be considered. Further investigations will be also carried out with regards to the industrial design of the system so as to ensure that sensor placement is always correct in terms of orientation and position, and to guarantee the absence of gradual changes which could affect the accuracy of the system. It is also worth noting that, at the current stage, the developed system does not provide a measure of \"correctness\" (e.g., if a test subject is running or jumping \"safely\"), but it provides all the considered metrics to the coaches involved in the training and leave the ultimate decision on the exercise \"correctness\" to the human user. However, a fully automated evolution of the system will be also taken into consideration in future work through the implementation of a traffic-light alarm system which would indicate how \"safely\" the exercise is performed (for example, green may be interpreted as things should continue as per normal, amber suggests caution that if left unattended could pose a risk, while red raises an alarm and indicates action is required). Indeed, the concept of traffic-light systems is currently widespread in sport science thanks to its ease of use [78]. However, the implementation of these traffic-light alarms would require the acquisition of massive datasets with the final aim of providing personalized red/amber/green feedbacks to the end-users based on baselines/benchmarks obtained from each athlete under testing. Finally, it must be recognized that the literature lacks a consensus regarding the accuracy levels required by coaches to find the technology acceptable.