Force Myography Based Novel Strategy for Locomotion Classification

Abstract

This paper presents a new, simple, and efficient strategy for locomotion classification using a force myography (FMG) system developed in-house. The FMG signals were acquired from eight calibrated force sensors. These sensors were wrapped around the thigh and captured volumetric changes in thigh muscles during locomotion. The data were collected from eight able-bodied subjects and two unilateral transfemoral amputees while walking in different terrains, i.e., level walk, ramp ascent, ramp descent, stair ascent, and stair descent (SD). The repeatability of FMG signals within all the five locomotion modes was expressed in terms of variance ratio (VR). For all locomotion modes, except SD, the VR ranges from 0.1 to 0.3, showing high repeatability within the locomotion modes for able-bodied and amputee subjects. Furthermore, these locomotion modes are characterized by computationally simplified time-domain features of FMG signals and classified using linear discriminant analysis (LDA). The subject-dependent LDA model gave an average classification accuracy of 99.5% ( ${\text{SD}}\,= \,{\text{0.5}}$ ) and 96.1% ( ${\text{SD}}\,= \,{\text{1.9}}$ ) for able-bodied and amputee subjects, respectively. These results show the potential of the FMG system in a real-time classification of locomotion modes for lower-limb prosthetic control.