Lower-limb Motion Intent Recognition Based on Sensor Fusion and Fuzzy Multi-task Learning

Lower-limb motion intent recognition is a crucial aspect of wearable robot control and human-machine collaboration. Among the various sensors used for this purpose, the electromyogram (EMG) sensor remains one of the most widely employed. However, EMG signals are highly susceptible to electrical noise, motion artefacts, and perspiration, which can compromise their quality. To address these challenges, we designed an air-pressure mechanomyography (PMMG) sensor and developed a wearable multi-modal sensor system that incorporates PMMG thigh-ring, inertial measurement unit (IMU), and force-sensitive resistor (FSR). To enhance gait phase and locomotion mode recognition performance, we proposed a gate multi-task TSK fuzzy inference system (GMT-TSK-FIS) algorithm that enables simultaneous handling of multiple recognition tasks. This approach enabled the development of a lower-limb motion intent recognition system that can simultaneously recognize gait phase and locomotion mode based on GMT-TSK-FIS. The experimental results showed that the accuracy of gait phase and locomotion mode recognition was 98.28% and 99.96%, respectively. Furthermore, the study demonstrated that multi-modal sensor fusion outperformed single-modal sensor fusion, while multi-task recognition exhibited better performance than single-task recognition.