Periodicity Detection of Quasi-Periodic Slow-Speed Gait Signal Using IMU Sensor

摘要

Wearable sensors have emerged as low-cost portable gait assessment tools in the past decade. However, it is necessary to process the signal collected from the sensors to analyze the human gait. Different spatio-temporal features for example, stride length, step length etc. can be obtained by detecting specific events in a gait cycle. Extraction of a single cycle is an important step for detection of events such as heel strike, foot flat, toe off, and mid-swing. Existing methods assume gait signals are fully periodic, whereas they are actually quasi-periodic. This quasi-periodicity increases with decrease in walking speed. Individuals with abnormal gaits are unable to walk in high speed. We propose a novel approach to extract cycles from gait signals with varying periodicity which are appropriate for low gait speed. To discriminate normal and abnormal gait pattern, we consider normal and equinus gait data in experimental analysis. Three participants walked normally, two participants simulated walking with equinus on the right leg, and two participants simulated walking with equinus on both legs. We have used two Sparkfun 9DoF Razor Inertial Measurement Unit (IMU) sensors having sampling frequency of 200 Hz. The performance of proposed approach is demonstrated through statistical error analysis. It outperforms the existing techniques, specially in case of low speed gait data. The complexity analysis is also presented to evaluate the efficiency of the proposed algorithm. Finally, Fisher's Discriminant Ratio is applied on the entire feature set to identify most prominent features to discriminate between normal and abnormal gait pattern.