%0 Journal Article %T Lower Limb Wearable Capacitive Sensing and Its Applications to Recognizing Human Gaits %A Enhao Zheng %A Baojun Chen %A Kunlin Wei %A Qining Wang %J Sensors %D 2013 %I MDPI AG %R 10.3390/s131013334 %X In this paper, we present an approach to sense human body capacitance and apply it to recognize lower limb locomotion modes. The proposed wearable sensing system includes sensing bands, a signal processing circuit and a gait event detection module. Experiments on long-term working stability, adaptability to disturbance and locomotion mode recognition are carried out to validate the effectiveness of the proposed approach. Twelve able-bodied subjects are recruited, and eleven normal gait modes are investigated. With an event-dependent linear discriminant analysis classifier and feature selection procedure, four time-domain features are used for pattern recognition and satisfactory recognition accuracies (97:3% ¡À 0:5%, 97:0% ¡À 0:4%, 95:6% ¡À 0:9% and 97:0% ¡À 0:4% for four phases of one gait cycle respectively) are obtained. The accuracies are comparable with that from electromyography-based systems and inertial-based systems. The results validate the effectiveness of the proposed lower limb capacitive sensing approach in recognizing human normal gaits. %K wearable gait sensors %K human body capacitance %K capacitive sensing %K muscle shape changes %K human normal gaits %K pattern recognition %U http://www.mdpi.com/1424-8220/13/10/13334