Extracting Muscle Geometrical Features With a Fabric-Based Wearable Sensor for Human Motion Intent Recognition

Fabric-based wearable sensing is receiving increasing attention in the field of wearable robots. In our study, we propose a fabric-based sensing method for human motion recognition/estimation. The approach was developed with an elastic sleeve integrated with four bend sensors and the superellipse-based construction algorithm. Unlike existing techniques, our method can extract muscular geometrical features in the anatomical cross-sectional plane. To validate our method, we conducted evaluations on 14 subjects, including time response evaluations, isometric grip force estimation, forearm/lower limb joint angle estimation, discrete lower limb posture recognition, and continuous gait phase estimation. First, our method produced comparable results to the state-of-the-art approaches. The average
$R^{2}$
values for joint angle estimation were 0.84–0.94, the average accuracy for lower limb posture recognition was 99.78%, and the average estimation error for gait phase was below 1% of a complete gait cycle. Second, we accomplished tasks that existing fabric-based mechanical sensors are unable to achieve. We demonstrated that our method detected motion onsets before the actual joint movements in voluntary dorsiflexion and sit-to-stand transition tasks. In addition, we achieved isometric grip force estimation with an average
$R^{2}$
of 0.89. Unlike stretch-based methods that measure the response of movements, our method extracts human motion intents before the actual movements occur. This extends the measurement scope of fabric-based wearable sensing for human motion recognition. In future work, we will focus on sensor integration and robot control to further enhance our method's capabilities.