Gait phase detection from thigh kinematics using machine learning techniques

Intelligent orthotic devices require accurate detection of gait events for real-time control. For orthoses that control the knee, an ideal system would only locate sensors at the thigh and knee, thereby facilitating sensor and electronics integration with the assistive device. To determine potential gait phase identification approaches, classification was implemented using J-48 Decision Tree, Random Forest, Multi-layer Perceptrons, and Support Vector Machine classifiers, along with 5-fold (5-FCV) and 10-fold cross validation (10-FCV). Knee angle, thigh angular velocity, and thigh acceleration were obtained from 31 able-bodied participants during walking (10 strides each). Strides were segmented into Loading Response, Push-Off, Swing, and Terminal Swing and features were extracted using a 0.1 second sliding window. Gait phase classification was performed with and without the knee angle parameter. J-48 Decision Tree with the knee angle parameter was ranked the best classifier due to its second highest classification accuracy of 97.5% and lowest mean absolute error of 0.014. Results without the knee angle parameter differed by only 0.5% and 0.003. Therefore, an inertial sensor with accelerometer and gyroscope output, located at the thigh, is a viable approach for classifying gait phases for intelligent orthosis control.