Locomotion Modes and Environmental Features Recognition Using Laser Distance Sensors

Abstract

Recognition of locomotion mode (LM) has been used to control the lower limb powered prosthetic and orthotic devices for navigating autonomously on a variety of terrains. In addition, the knowledge of environmental features (EFs) can be utilized to modulate the joint kinetics of prosthetic devices in a precise manner. In this article, a novel LM recognition strategy has been proposed along with EFs estimation utilizing a combination of laser distance sensors (LDSs) and inertial measurement units (IMUs). LM was classified from outputs of eight sensors, i.e., six LDSs and two IMUs, utilizing support vector machine (SVM) followed by a majority voting scheme. A cloud of points was generated in the sagittal plane, which was further used to determine the EFs leveraging curve fitting techniques. The efficiency of the proposed method was tested with six healthy subjects and for a variety of LMs, such as walking on the level-ground, ramp, and stairs. The overall accuracy of LM classification and EF estimation was found to be equal to 97.85% and 95.36%, respectively. The proposed study is an accurate, computationally inexpensive, and cost-effective solution and can be used efficiently to control lower limb prosthetic devices. The novelty of this study is the simplification of the LM classification and EF estimation methods compared to a camera-based technique to facilitate real-time control of powered prostheses.