Filippo Motta

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.

This study aimed to evaluate the use of wearable sensors to estimate L5-S1 flexion moments during symmetric lifting tasks by integrating inertial motion capture (IMC), surface electromyography (sEMG), and the calibrated EMG-informed neuromusculoskeletal (CEINMS) framework. Eight participants performed stoop and squat lifting at four load levels while gold-standard optoelectronic motion capture, ground reaction forces, inertial measurement units (IMUs), and sEMG were recorded. IMC-based inverse dynamics showed excellent agreement with OMC ( ρ = 0.83–0.96; ICC > 0.91), with mean errors around 2–3 Nm. CEINMS moments calibrated using wearable data exhibited strong reliability and captured the expected load-dependent increases in lumbar moments, although peak values during heavy lifting were systematically underestimated. IMC consistently underestimated L5-S1 flexion angles by 2°, likely contributing to the reduced peak moment estimates. Overall, findings support the feasibility of a wearables-only approach for ergonomic risk assessment. Future work should focus on improving lumbo-sacral flexion angle estimation from IMUs, potentially through multi-sensor lumbar configurations.

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.

Markerless human pose estimation is increasingly used for kinematic assessment, but evidence of its applicability to upper limb movements across different ranges of motion (ROM) remains limited. This study examined the accuracy and reliability of a markerless pose estimation system for shoulder, elbow and wrist flexion–extension analysis under full and partial ROM tasks. Ten healthy participants performed standardized movements which were synchronously recorded, with an optoelectronic motion capture system used as a reference. Joint angles were compared using RMSE, percentage RMSE (%RMSE), accuracy (Acc), intraclass correlation coefficients (ICC), and Pearson correlation of ROM values. The markerless system reproduced the temporal morphology of the movement with high coherence, showing ICC values above 0.91 for the elbow and 0.94 for the shoulder in full ROM trials. Wrist tracking presented the lowest RMSE values and low inter-subject variability. The main critical aspect was a systematic underestimation of maximum flexion, especially at the shoulder, indicating a magnitude bias likely influenced by occlusion and joint geometry rather than by temporal fluctuations. Despite this limitation, the system adapted consistently to different ROM amplitudes, maintaining proportional variations in joint excursion across tasks. Overall, the findings outline the conditions in which markerless pose estimation provides reliable upper limb kinematics and where methodological improvements are still required, particularly in movements involving extreme flexion and occlusion.

Aquatic physical activity plays a vital role in sports training and rehabilitation, yet precise motion analysis in water remains challenging due to the limitations of traditional motion capture systems. This study validates the use of experimental wearable inertial measurement units, TinyTag, combined with OpenSense software to estimate joint angles on land and underwater. The method was assessed through a two-phase approach: First, TinyTag-OpenSense joint angle estimates were validated against the gold standard motion analysis system in a controlled laboratory setting; second, the framework was applied to analyze lower-limb joint range of motion during four hydrotherapy exercises in 15 healthy adults on land and in water. The results demonstrated good agreement with the reference system for squat and knee-to-chest exercises, with a root mean square error below 5 ° and a Spearman correlation coefficient greater than 0.85. The underwater assessment revealed an increase of at least 10% (p¡0.005) in range of motion for all exercises except squats and frontal leg swing, highlighting the supportive effects of water. This study presents a validated open source methodology for underwater motion tracking, offering a solution for clinical and research applications in aquatic rehabilitation and sports science.

Ski mountaineering (Skimo) combines mountain climbing and skiing, with ascents on skis or by carrying them, followed by ski descents. Despite its popularity, the literature lacks integrated biomechanical analyses during indoor training, limiting evidence-based recommendations for training, injury prevention, and performance improvement in this sport. This study analyzed four Skimo athletes during uphill walking and running on a 9° inclined treadmill at 7 km/h and 8 km/h. Kinematics was assessed by an optoelectronic system, and surface electromyography recorded the muscular activity of twelve bilateral muscles (Erector Spinae, Rectus Abdominis, Rectus Femoris, Biceps Femoris, Tibialis Anterior, Gastrocnemius Lateralis). Rectus Femoris and Biceps Femoris co-activation supported knee stability during the load support phase, especially while running. Running at 8 km/h was the most demanding condition, with increased knee flexion throughout the task cycle. Switching from 7 to 8 km/h reduced the maximum extension and increased the maximum flexion of both hip and knee and required the Gastrocnemius’ recruitment in the swing phase. Regardless of task and speed, Rectus Abdominis and Erector Spinae played a key role in stabilizing the trunk. This study provided a biomechanical characterization of two motor gestures typical in Skimo, highlighting how task typology and velocity could influence kinematics and muscle activation.

This study aims to improve the assessment of biomechanical risk in manual lifting tasks by introducing a method for calculating composite lifting index (CLI) using wearable inertial measurement units (IMUs). While the revised NIOSH lifting equation (RNLE) is widely used to evaluate the risk associated with lifting tasks, traditional methods often struggle with accuracy, especially in complex tasks. To address this, we compared the CLI values obtained using IMUs with those derived from a gold standard optoelectronic system during laboratory tests involving three levels of lifting risk. Ten participants performed standardized lifting tasks under controlled conditions, and the results showed that the IMU-based method provided comparable accuracy to the optoelectronic system, with negligible differences. Despite some variability in horizontal multiplier (HM) values, the IMU system demonstrated potential for real-world applications due to its ease of use and automatic calculation capabilities. Future improvements may include refining distance measurements and expanding the method for more complex lifting scenarios. This novel approach offers a practical and precise tool for ergonomic risk assessments, particularly in dynamic work environments.

Work-related musculoskeletal disorders (WRMSDs) are a leading cause of chronic conditions among working-age adults. Preventing these disorders is crucial to reducing their impact, and quantitative analysis through sensors can help identify their causes and guide ergonomic solutions. This systematic review aims to compile research from 2000 to 2023 published in English and sourced from Web of Science, Scopus, or PubMed that examines workers’ movements during tasks using wearable sensor systems that are applicable in workplace settings. The goal is to identify the job sectors that have been studied and highlight tasks lacking ergonomic risk research. A total of 111 papers were selected through a screening process across three databases, assessed using the McMaster risk of bias tool. The studies span various job sectors and report on the use of different technologies for data collection and study population sizes. The review identifies existing research on WRMSD risks utilizing wearable systems in different job sectors, drawing attention to under-researched areas that warrant further study. It serves as a foundation for future research aimed at understanding the causes of WRMSDs and developing solutions supported by wearable technologies to mitigate these risks.