Telerehabilitation, a specialized domain within telemedicine, supports remote physical rehabilitation and progress monitoring. Wearable sensors can improve this service by providing reliable monitoring of movement parameters, offering objective information into patients’ rehabilitation sessions. This study presents the development and validation of a telerehabilitation system including a rehabilitation protocol, low-cost wearable inertial measurement units (IMUs) and a set of metrics descriptive of movement capacity to analyze rehabilitation exercises. Eleven medically stable elders (9 females, 2 males; age: 72.6 ± 5.0 years; height: 1.66 ± 0.09 m; mass: 67.8 ± 9.8 kg) performed 12 rehabilitation upper/lower limb and trunk exercises. Movement analysis was conducted using a prototypical IMU sensor and commercially available IMU as a reference. Each exercise was automatically segmented into single repetitions, from which selected metrics were computed. Bland–Altman analysis was performed to evaluate measurement agreement and consistency between the systems across all parameters. Results indicate acceptable measurement agreement for key rehabilitation metrics, including movement quantity, accelerations intensity, and movement smoothness. However, angular velocity and movement stability reveal technical limitations requiring refinement prior to clinical implementation. Balancing measurement reliability and affordability of telerehabilitation system remains a crucial factor to offer an effective service to individuals with diverse health conditions.

Telerehabilitation, a specialized domain within telemedicine, supports remote physical rehabilitation and progress monitoring. Wearable sensors can improve this service by providing reliable monitoring of movement parameters, offering objective information into patients’ rehabilitation sessions. This study presents the development and validation of a telerehabilitation system including low-cost wearable inertial measurement units (IMUs) to analyze rehabilitation exercises. Eleven medically stable elders (9 females, 2 males; age: 72.6 ± 5.0 years; height: 1.66 ± 0.09 m; mass: 67.8 ± 9.8 kg) performed 12 rehabilitation upper\lower limb and trunk exercises. Movement analysis was conducted using a prototypical IMU sensor and commercially available IMU as a reference. Each exercise was automatically segmented into single repetitions, from which various parameters descriptive of movement capacity were computed. Bland-Altman analysis was performed to evaluate measurement agreement and consistency between the systems across all parameters. Results indicate acceptable measurement agreement for key rehabilitation metrics, including movement quantity, accelerations intensity, and movement smoothness. However, other metrics reveal technical limitations requiring refinement prior to clinical implementation. Balancing the reliability of measurement and the accessibility of telerehabilitation system remains a crucial factor for the successful implementation of this service.

Background: Respiratory exercises play a key role in rehabilitation programs, especially for older adults and individuals with chronic pulmonary conditions. Despite growing interest in wearable sensors for home-based care, structured reference metrics to quantitatively characterize respiratory exercises are still limited. This study aimed to provide a quantitative characterization of respiratory exercises and evaluate the level of agreement between a low-cost prototypical sensor and a commercial one. Methods: Eleven older adults (9 females; age = 72.6 ± 5.0 years; height = 1.66 ± 0.09 m; mass = 68 ± 10 kg) performed a structured respiratory exercises protocol. Algorithms were developed to identify respiratory cycles, their execution time, and parameters related to respiratory capacity, using accelerometer signals from the two wearable sensors placed on the rib cage. Results: The average respiratory cycle duration ranged from 2.8 to 4.3 s, with normalized inspiratory and expiratory peaks. Tidal volume variability was minimal, confirming consistency in breathing patterns across exercises. User comfort was high (mean VAS = 8.7). Sensor comparison confirmed strong agreement between the two sensors in detecting respiratory cycles, though some variability was observed in timing and tidal volume estimation. Conclusions: These findings suggest that even simple accelerometers can reliably capture key respiratory parameters, supporting the feasibility of using wearable sensors to monitor structured respiratory exercises performed in home-based settings.

Soccer is practiced by professionals, amateurs, and recreational players. The physical assessment tools used by professionals are rarely available in recreational settings. Given the widespread participation and potential health benefits of soccer activity, it becomes essential to identify simple and accessible indicators that can help to characterize physical ability in non-professional players. This cross-sectional observational work explores which health status and lifestyle indices can be useful to estimate physical ability in recreational male soccer players when field testing is not feasible. Sixty-six participants volunteered in the study. Five performance field tests were conducted, and a related overall physical ability index (KPI_tot) was defined, while a questionnaire was developed to investigate nine BIO_Indices (BMI, age, physical activity level, job, alcohol consumption, smoking habits, sports career, occurring injuries, medical history). Data for the selected performance tests are reported for the recruited recreational athletes. KPI_tot was estimated from BIO_Indices, using a stepwise backward regression. The selected model, named SOCCER_Index, incorporates six out of nine BIO_Indices, excluding smoking habits, sports career, and medical history (R² = 0.536). In conclusion, with a simple questionnaire, an estimate of soccer players’ physical ability can be obtained. Further data collection is needed to obtain a more generalizable and robust SOCCER_Index.

Background: Telerehabilitation can improve access to rehabilitation services for older adults, but limited clinical supervision may increase the risk of undetected compensatory movements. This study evaluated whether inertial measurement unit (IMU) based machine learning model could automatically detect compensations during rehabilitation exercises. Methods: Five exercise-specific classifiers were developed using a controlled dataset of compensatory movements performed by fifteen healthy kinesiologists. For each exercise, 450 correct and 450 compensatory repetitions were collected while participants wore both the prototype and commercial IMUs. Biomechanical and statistical features were extracted, and LASSO regression was applied for feature selection. Five different classifiers were optimized through grid search and evaluated using subject-wise leave-one-out cross-validation. The selected models were subsequently validated in eleven community-dwelling older adults aged 69.7 ± 7.1 years. Results: In controlled conditions, the prototype achieved performance comparable to the commercial IMU (accuracy and precision ≥ 95%). In older adults, accuracy ranged from 67% to 96%, whereas precision ranged from 32% to 100%, indicating exercise-dependent differences. Conclusions: A low-cost IMU combined with lightweight machine learning can detect compensatory movements with performance comparable to a research-grade sensor under controlled conditions. Larger clinical samples, different exercises and multi-level compensation labels are required to improve generalizability and support real-time telerehabilitation feedback.