Concurrent Validity of Wearable Nanocomposite Strain Sensor With Two-Dimensional Goniometer and Its Reliability for Monitoring Knee Active Range of Motion in Multiple Participants

Abstract

The range of motion (ROM) of joints in the human body is essential for movement and functional performance. Real-time monitoring of joint angles is crucial for confirming pathologic biomechanics, providing feedback during rehabilitation, and evaluating the treatment efficacy. This study aims to evaluate the concurrent validity of a wearable nanocomposite strain sensor with a two-dimensional electrical goniometer and its repeatability for measuring knee ROM during repetitive joint movements in 10 healthy female participants. The participants performed seated knee flexion and extension in three sessions, during which knee ROM was measured simultaneously using the two devices. A statistical analysis was conducted using the intraclass correlation coefficient (ICC) and Bland-Altman analysis. The strain sensor demonstrated excellent concurrent validity (ICC =0.94) and good reliability (ICC =0.87), with biases close to zero and the magnitude of disagreements lying within ±5-10° for validity and ±10-15° for reliability. The standard deviation of the mean (SEM) for absolute reliability was 2.18°, with the width of variability based on SEM at 9.88°. The results indicate that the strain sensor exhibits clinically acceptable accuracy and precision, comparable to the existing wearable sensors. However, careful interpretation is required for variations in repeated measurements exceeding 10°. Future research should focus on enhancing the sensor attachment and calibration methods, along with broadening the application scope to more dynamic activities, other joints, and patients with specific pathologies. The strain sensor presents significant potential for real-time and continuous monitoring of joint angles during real-world activities as well as rehabilitation programs.

Keywords

• Digital health
• joint range of motion
• strain sensors
• validation
• wearable sensors