## 8 Discussion  
Despite the various technologies currently adopted for injury prevention, the injury rate in sport is increasing [67]. This may be due to the current technology not yet providing key metrics to allow both the coach and athletes to make training and performance-related decisions (i.e., athlete wellness, musculoskeletal screening scores, training load, fitness, and fatigue) in real-time and on-the-field. Additionally, compared to specific orthopedic conditions for which clinical prediction rules exist, athletic training is lagging significantly behind in the area of clinical prediction modeling [68]. Wearables have been already used by athletes and sports teams for a number of years, in particular for tracking information associated with health and fitness. However, the development of tools for performance evaluation and prevention of running-related injuries is still in its infancy [68]. A number of works [69; 70] have indeed considered the use of in-soles for pressure plantar monitoring and for the estimation of biomechanical loads associated with running injuries, and several companies have launched such products as shown in the review by [PERSON] et al. [71]. However, most of the products on the market focus only on the pressure heat map /distribution and they are still accompanied with practical long-term issues related to reliability, short lifespan, rapid degradation, feebleness, and high cost. IMU-based wearable products (i.e., RunScribe [56], GaitUp [58], Stryd [62], IMeasureU [63], ViPerform [64]) are well-known already for providing biomechanical variables, such as contact time, swing time, cadence, stride time, stride length, speed, pronation angle, foot-strike type, distance covered, asymmetry, stiffness, and impact. However, current IMU-based wearable devices on the market provide loading parameters without specifying \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Products** & **Sport** & **Parameters Calculated** & **Number of Sensors/Body** & **Sampling Frequency** \\  & & & Cadence, range of motion, & **Position** & **(Hz)** \\  & & & tibial impact & 1 on ankle & NA \\ \hline TgForce [61] & Running & Peak acceleration (in g), & & & \\  & & cadence & & & \\ \hline Stryd [62] & Running & Ground contact time, & & & \\  & & vertical oscillation, & & & \\  & & running power, distance, & & & \\  & & leg stiffness, cadence & & & \\ \hline IMeasureU [63] & \begin{tabular}{c} Jumping, sprinting, \\ counter rotation, \\ swimming, power meter \\ \end{tabular} & Steps, cumulative impact & Up to 8 sensors on the & & \\  & & load, cumulative bone & body (possibility to sync & 500 \\  & & stimulus & with VICON MoCap) & & \\ \hline ViPerform (DorsaVi) [64] & \begin{tabular}{c} Functional tests, \\ hamstring tests, knee \\ movement tests, running \\ tests \\ \end{tabular} & \begin{tabular}{c} Symmetry, average \\ ground reaction force, \\ peak acceleration, ground \\ contact time, cadence, \\ distance, speed \\ \end{tabular} & 1 per tibia (with possibility & 100 \\ \hline MyVert [65] & Jumping, running & \begin{tabular}{c} Jump metrics, landing \\ impact features, drills \\ run feature, energy feature, \\ power/intensity features, \\ stress features \\ \end{tabular} & 1 on center-of-mass & NA \\ \hline K-50 (K-Sport) [66] & Soccer-related movements & \
\begin{tabular}{c} 300 parameters including \\ physical, technical, and \\ tactical information \\ \end{tabular} & 1 on the chest (also & \\  & & including GPS, UWB, and & 50 \\ \hline Proposed system & Jumping, running &
\begin{tabular}{c} 13 running temporal, \\ spatial, and performance \\ metrics, full vertical GRF \\ waveform, and jumping \\ metrics for two different \\ jump forms \\ \end{tabular} & 1 on each tibia (for & \\  & running), 1 on & 238 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: \(Cont.\)how they have been extrapolated or correlated to the GRF curve. Moreover, several devices fail to provide scientific evidence and validations on how accurate their outcomes are.