Application of Wearable Miniature Non-invasive Sensory System in Human Locomotion Using Soft Computing Algorithm

The authors have designed and tested a wearable miniature non-invasive sensory system for the acquisition of gait features. The sensors are placed on anatomical segments of the lower limb, and motion data was then acquired in conjunction with electromyography (EMG) for muscle activities, and instrumented treadmill for ground reaction forces (GRF). A relational matrix was established between the limb-segment accelerations and the gait phases. A further relational matrix was established between the EMG data and the gait phases. With these pieces of information, a fuzzy rule-based system was established. This rule-based system depicts the strength of association or interaction between limb-segments accelerations, EMG, and gait phases. The outcome of measurements between the rule-based data and the randomized input data were evaluated using a fuzzy similarity algorithm. This algorithm offers the possibility to perform functional comparisons using different sources of information. It can provide a quantitative assessment of gait function.