Robust Gait Phase Estimation With Discrete Wavelet Transform for Walking Assistance on Multiple Terrains

Abstract

Gait phase detection is crucial to realize personalized assistive functions of lower limb exoskeletons. A common method in gait phase estimation is the adaptive oscillator, which performs well in periodic gaits. However, these types of methods fail in gait phase estimation under aperiodic gait cycles. Although some modified methods have been proposed for gait phase estimation under multiple terrains, they usually require dataset training, and the estimation accuracy is highly dependent on the collected dataset. To realize accurate and stable recognition of gait phase, a novel gait phase estimation method is proposed to estimate the gait phase without dataset training. This method, by incorporating discrete wavelet transform (DWT) with adaptive oscillators (AOs), can identify non-periodic mutations online and reset the oscillator at an appropriate time to avoid the divergence phenomenon when the adaptive oscillator is subjected to mutation signals. In this proposed method, the hip angle is measured by an inertial measurement unit (IMU) sensor and the measured data is then processed using discrete wavelet transform to detect the maximum hip flexion angle (MFA) and non-periodic mutations. The gait phase is finally estimated by a modified adaptive oscillator. Ground walking tests with a variety of speeds by six subjects were conducted under different walking conditions and the results show that the new method works robustly for gait phase estimation under multiple terrains. The method is demonstrated on a hip exoskeleton in walking assistance.