Gait analysis is a well-known tool used to evaluate human locomotion. This analysis is usually performed in movement analysis laboratories equipped with optoelectronic cameras. However, many wearable devices based on the use of Inertial Measurement Units (IMUs) have been developed to overcome the limitations of the gold standard technology. LUBU Technologies developed insoles equipped with IMUs and algorithms for the extraction of gait parameters such as gait events, temporal parameters and stride length. The aim of the present study was the validation of these parameters against an optoelectronic reference system. Twenty people were involved in performing a series of indoor walking trials to assess the accuracy of the insole measurements and an additional group of ten people were asked to walk outdoors to preliminarily assess total distance estimation under ecological walking conditions. The results showed low errors and good agreement for all the parameters computed by the insoles. Moreover, the outdoor assessment offered preliminary insights into performance in a more ecological setting. Overall, the findings suggest that LUBU insoles may represent a valid wearable tool for estimating gait spatio-temporal parameters in healthy young adults. However, further investigations are required in older adults or populations with pathological gait patterns.

Running-specific prostheses are widely used in Paralympic sport and their performance assessment under real running conditions requires wearable monitoring systems capable of capturing both prosthesis dynamics and athlete kinematics. Existing solutions have been validated in laboratory settings and short trials, leaving open challenges related to autonomy, robustness, and applicability to prolong outdoor running. This study aimed to design and evaluate a wearable IMU-based monitoring system for assessing prosthesis dynamics and athlete kinematics during long-distance running. Four IMU sensors mounted on prosthetic blades and sockets were deployed, addressing practical constraints of battery life, memory, and outdoor robustness. The system was tested during a 35 km trail running race by a former Paralympic athlete with bilateral transtibial amputation. The race course was segmented into nine slope-based sectors, and stride-level metrics, including impact acceleration, vibration peak, damping ratio, and damped natural frequency, were extracted from blade-mounted IMU data and compared across sectors using non-parametric statistics and effect size measures. The results suggest that the proposed system may capture meaningful variations in prosthesis dynamics across different running conditions, with moderate to large effect sizes observed for most metrics. These preliminary findings support the feasibility of the proposed approach for in-field prosthesis monitoring.