This paper introduces a real-time gait phase detection system and algorithm utilizing non-contact distance sensing, specifically designed for wearable robotic applications. Two Time-of-Flight (ToF) sensors are positioned on the outer heel and the fifth metatarsophalangeal (MTP) joint to monitor foot-to-ground distance throughout the gait cycle. These sensors are housed in overshoe-type modules to minimize interference with natural walking and allow for easy attachment to various types of footwear. The proposed method segments gait phases using a lightweight, thresholdbased algorithm that is both computationally efficient and physically interpretable. Experimental validation with a healthy subject shows that the system reliably detects stance and swing phases, producing temporal patterns that are comparable to those of conventional pressure-based systems. Importantly, the system provides continuous data even during swing phases, facilitating smoother transitions for control systems. The simplicity and wearability of the hardware indicate its potential for real-time control in lower-limb wearable robots, gait assistance devices, and ambulatory monitoring systems.
The understanding of impaired neural control of gait after stroke is important to evaluate mobility impairments focused on improving walking function. Previous studies have shown that the central nervous system may control gait via muscle synergies, which modularly organizes multiple muscles. However, there are insufficient studies to evaluate mobility impairments, using muscle synergy during walking in post-stroke patients. Thus, the purpose of this study was to determine if the variability of muscle synergies during gait reflects impaired motor performance. Electromyography (EMG) signals were collected from five persons with post-stroke hemiparesis and five similarly age healthy persons, as they walked on a treadmill at a comfortable speed. EMG signals were decomposed using non-negative matrix factorization and the variability of muscle synergies was calculated using a synergy stability index (SSI). We also investigated correlation between the SSI and Fugl-Meyer assessment and Berg Balance Scale, which are clinical evaluation indicators. Post-stroke patients were found to have variable muscle synergies. We also observed a positive proportional relation, between SSI and clinical motor impair evaluation indicators. These results could yield a quantitative assessment of gait after stroke, and provide a causal relationship between internal neuromuscular mechanisms and functional performance.
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