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.