This paper presents a portable inertial measurement unit (IMU)-based motion sensing system and proposed an adaptive gait phase detection approach for non-steady state walking and multiple activities (walking, running, stair ascent, stair descent, squat) monitoring. The algorithm aims to overcome the limitation of existing gait detection methods that are time-domain thresholding based for steady-state motion and are not versatile to detect gait during different activities or different gait patterns of the same activity. The portable sensing suit is composed of three IMU sensors (wearable sensors for gait phase detection) and two footswitches (ground truth measurement and not needed for gait detection of the proposed algorithm). The acceleration, angular velocity, Euler angle, resultant acceleration, and resultant angular velocity from three IMUs are used as the input training data and the data of two footswitches used as the training label data (single support, double support, swing phase). Three methods 1) Logistic Regression (LR), 2) Random Forest Classifier (RF), and 3) Artificial Neural Network (NN) are used to build the gait phase detection models. The result shows our proposed gait phase detection with Random Forest Classifier can achieve 98.94% accuracy in walking, 98.45% in running, 99.15% in stair-ascent, 99.00% in stair-descent, and 99.63% in squatting. It demonstrates that our sensing suit can not only detect the gait status in any transient state but also generalize to multiple activities. Therefore, it can be implemented in real-time monitoring of human gait and control of assistive devices.
Machine Learning Based Adaptive Gait Phase Estimation Using Inertial Measurement Sensors
- Views Icon Views
- Share Icon Share
- Search Site
Yang, J, Huang, T, Yu, S, Yang, X, Su, H, Spungen, AM, & Tsai, C. "Machine Learning Based Adaptive Gait Phase Estimation Using Inertial Measurement Sensors." Proceedings of the 2019 Design of Medical Devices Conference. 2019 Design of Medical Devices Conference. Minneapolis, Minnesota, USA. April 15–18, 2019. V001T09A010. ASME. https://doi.org/10.1115/DMD2019-3266
Download citation file: