A Gait Monitoring System Based on Air Pressure Sensors Embedded in a Shoe

Measurement of ground contact forces (GCFs) provides necessary information to detect human gait phases. In this paper, a new analysis method of the GCF signals is discussed for detection of the gait phases. Human gaits are complicated, and the gait phases cannot be exactly distinguished by comparing sensor outputs to a threshold. This paper proposes a method by fuzzy logic for detecting the gait phases continuously and smoothly. The smooth and continuous detection of the gait phases enables a full use of information obtained from GCF sensors. For advanced rehabilitation systems, this paper also introduces a higher level algorithm that quantitatively monitors the amount of abnormalities in a human gait. The abnormalities detected by the proposed method include an improper GCF pattern as well as an incorrect sequence of the gait phases. To realize the monitoring algorithm, the gait phases are analyzed as a vector and the abnormalities are detected by simple kinematic equations. The proposed methods are implemented by using signals from sensor-embedded shoes called smart shoes. Each smart shoe has four GCF sensors installed between the cushion pad and the sole. The GCF sensor applies an air pressure sensor connected to an air bladder. A gait monitoring system that integrates the proposed methods is shown in this paper and verified for both normal and abnormal gaits.