Real Time Level Ground Walking vs Stair-Climbing Locomotion Mode Detection

This paper presents a real-time classification method of ground-level walking and stair climbing, which is a crucial information of natural human locomotion in robotic prosthesis control. Two Inertial Measurement Units (IMU) were mounted on an earlier developed measurement exoskeleton system (one IMU in the shank and the other IMU on the thigh) to monitor the locomotion states. A pair of force-sensing resistors were also incorporated into the shoe insole for plantar pressure measurement. The sensors were interfaced with an STM32L476RG microcontroller powered by a rechargeable battery. The data collection was performed on two healthy subjects. Three features (Thigh IMU x-axis accelerometer minimum value, Shank IMU z-axis gyroscope maximum value, and x-axis gyroscope variance) were computed from the sensors signal. Classification of ground-level walking vs. stair climbing events was performed using Linear Discriminant Analysis (LDA). The accuracy, sensitivity, and specificity were obtained on the training set as 96.50%, 96.32%, and 96.66%, respectively. After implementing the classifier in the embedded system, the sensor system was tested in real-time for 26 minutes with an accuracy of 87.21%, the sensitivity of 90.48%, and the specificity of 86.75%. The results indicate that the system can detect the locomotion states with reasonable accuracy, which could be further implemented in determining the control strategy of a powered intelligent prosthesis in the real-time.