Introduction
 Monitoring biomechanics is crucial in sports and rehabilitation, and frontal knee angle is of special interest in these applications. Current solutions – optical motion capture (OMC), or inertial measurement units suits – are costly, spatially constrained, and impractical for use in daily life. Textile-based wearable systems are a valuable alternative for unobtrusive movement monitoring. Textile-based wearables for knee angle monitoring have mostly been used for sagittal angle prediction, however, frontal knee angle measurement is more challenging. We investigated the design and performance of a smart garment for the detection of knee joint angles in three planes during different activities.
 Methods
 We equipped a pair of tight pants with four helical auxetic yarn capacitive strain sensors (Cuthbert et al., 2022) placed close to the knees. The exact positioning was optimized with an OMC study: markers were placed in potential sensor locations (Gholami et al., 2019) and the pairs of markers whose distance had the highest mutual information with knee angles were selected for sensor placement. A healthy participant performed walking and turning around, and knee ab/adduction activities wearing the sensorized prototype. The latter activity emphasized knee motion in the frontal and transverse planes. The capacitances from the sensors were recorded with a custom electronics board that transmitted data wirelessly to a smartphone. Multiple regression algorithms were implemented to predict knee angles from the strain sensors data, with the ground truth obtained from the OMC data recorded simultaneously during the experiments.
 Results
 The optimal sensor placements were above the kneecaps, orientated as the vastus medialis and the rectus femoris. Xgboost regression algorithm yielded best performance for walking with root mean square errors (RMSE) of 10.79°, 3.77°, and 2.49° for the sagittal, frontal, and transverse angles, respectively. Linear regression performed the best for knee ab/adduction with RMSEs of 8.96°, 6.33°, and 1.58° for the sagittal, frontal, and transverse angles (Fornaciari, 2023).
 Discussion/Conclusion
 The smart garment system was overall able to track the knee angle in three planes. The larger errors, compared with previous works (Gholami et al., 2019), reported for the walking and turning around movement are likely because of high variations in the movements of the participants during turning around. Additionally, the proposed system showed capability to monitor frontal and transverse angles with an average RMSE of 3.5°. The larger error values of the sagittal angles are likely because of higher range of motion in that plane. The proposed system allows for continuous and unobtrusive knee angle monitoring outside of the laboratory settings in the comfortable form factor of smart clothing.
 References
 Cuthbert, T. J., Hannigan, B. C., Roberjot, P., Shokurov, A. V., & Menon, C. (2022). HACS: Helical auxetic yarn capacitive strain sensors with sensitivity beyond the theoretical limit. Advanced materials, 35(10) Article 2209321. https://doi.org/10.1002/adma.202209321
 Fornaciari, A. (2023). Wearable technology for lower limb movement monitoring [Master’s thesis]. Politecnico di Milano.
 Gholami, M., Rezaei, A., Cuthbert, T. J., Napier, C., & Menon, C. (2019). Lower body kinematics monitoring in running using fabric-based wearable sensors and deep convolutional neural networks. Sensors, 19(23), Article 5325. https://doi.org/10.3390/s19235325
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Feb 9, 2024
Luanne Lys De Souza Oliveira + 3
Open Access
