Context: Wearable sensor devices have notable advantages, such as cost-effectiveness, easy to use, and real-time feedback. Wirelessness ensures full-body motion, which is required during movement in a challenging environment such as during sports. Research on the reliability and validity of commercially available systems, however, is indispensable. Objective: To confirm the test–retest reliability and concurrent validity of a commercially available body-worn sensor—BTS G-WALK^® sensor system—for spatiotemporal gait parameters with the GAITRite^® walkway system as golden standard. Design: Reliability and concurrent validity study. Setting: Laboratory setting. Participants: Thirty healthy subjects. Main Outcome Measures: Spatiotemporal parameters: speed, cadence, stride length, stride duration, stance duration, swing duration, double support, and single support. Results: In terms of test–retest reliability of the BTS G-WALK^® sensor system, intraclass correlation coefficient values for both the spatial and temporal parameters were excellent between consecutive measurements on the same day with intraclass correlation coefficient values ranging from .85 to .99. In terms of validity, intraclass correlation coefficient values between measurement systems showed excellent levels of agreement for speed, cadence, stride length, and stride duration (range = .88–.97), and showed poor to moderate levels of agreement (range = .12–.47) for single/double support and swing/stance duration. Bland–Altman plots showed overall percentage bias values equal to or smaller than 3% with limits of agreement ≤15% (speed, cadence, stride length, stride duration, swing duration, and stance duration). Only for single and double support, the limits of agreement were higher with, respectively, −15.4% to 19.5% and −48.0% to 51.4%. Conclusion: The BTS G-WALK^® sensor system is reliable for all measured spatiotemporal parameters. In terms of validity, excellent concurrent validity was shown for speed, cadence, stride length, and stride duration. Cautious interpretation is necessary for temporal parameters based on final foot contact (stance, swing, and single/double support time).

Inertial measurement unit (IMU) records of human movement can be converted into joint angles using a sensor-to-segment calibration, also called functional calibration. This study aims to compare the accuracy and reproducibility of four functional calibration procedures for the 3D tracking of the lower limb joint angles of young healthy individuals in gait. Three methods based on segment rotations and one on segment accelerations were used to compare IMU records with an optical system for their accuracy and reproducibility. The squat functional calibration movement, offering a low range of motion of the shank, provided the least accurate measurements. A comparable accuracy was obtained in other methods with a root mean square error below 3.6° and an absolute difference in amplitude below 3.4°. The reproducibility was excellent in the sagittal plane (intra-class correlation coefficient (ICC) > 0.91, standard error of measurement (SEM) < 1.1°), good to excellent in the transverse plane (ICC > 0.87, SEM < 1.1°), and good in the frontal plane (ICC > 0.63, SEM < 1.2°). The better accuracy for proximal joints in calibration movements using segment rotations was traded to distal joints in calibration movements using segment accelerations. These results encourage further applications of IMU systems in unconstrained rehabilitative contexts.

Rehabilitation for total knee replacement (TKA) often involves in-person therapy sessions, which can be time consuming and costly. Digital rehabilitation has the potential to address these limitations, but most of these systems offer standardized protocols without considering the patient’s pain, participation, and speed of recovery. Furthermore, most digital systems lack human support in case of need. The aim of this study was to investigate the engagement, safety, and clinical effectiveness of a personalized and adaptative app-based human-supported digital monitoring and rehabilitation program. In this prospective multi-center longitudinal cohort study, 127 patients were included. Undesired events were managed through a smart alert system. Doctors were triggered when there was a suspicion of problems. The drop-out rate, complications and readmissions, PROMS, and satisfaction were collected through the app. There was only 2% readmission. Doctor actions through the platform potentially avoided 57 consultations (85% of alerts). The adherence to the program was 77%, and 89% of the patients would recommend the use of the program. Personalized human-backed-up digital solutions can help to improve the rehabilitation journey of patients after TKA, lower healthcare-related costs by lowering the complication and readmission rate, and improve patient reported outcomes.

Osteoarthritis (OA) poses a growing challenge for the aging population, especially in the hip and knee joints, contributing significantly to disability and societal costs. Exploring the integration of wearable technology, this study addresses the limitations of traditional rehabilitation assessments in capturing real-world experiences and dynamic variations. Specifically, it focuses on continuously monitoring physical activity in hip and knee OA patients using automated unsupervised evaluations within the rehabilitation process. We analyzed data from 1144 patients who used a mobile health application after surgery; the activity data were collected using the Garmin Vivofit 4. Several parameters, such as the total number of steps per day, the peak 6-minute consecutive cadence (P6MC) and peak 1-minute cadence (P1M), were computed and analyzed on a daily basis. The results indicated that cadence-based measurements can effectively, and earlier, differ among patients with hip and knee conditions, as well as in the recovery process. Comparisons based on recovery status and type of surgery reveal distinctive trajectories, emphasizing the effectiveness of P6MC and P1M in detecting variations earlier than total steps per day. Furthermore, cadence-based measurements showed a lower inter-day variability (40%) compared to the total number of steps per day (80%). Automated assessments, including P1M and P6MC, offer nuanced insights into the patients’ dynamic activity profiles.