Prosthetic Leg Locomotion-Mode Identification Based on High-Order Zero-Crossing Analysis Surface Electromyography
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Prosthetic Leg Locomotion-Mode Identification Based on High-Order Zero-Crossing Analysis Surface Electromyography LIU Lei 1∗ (
),
YANG Peng 2 (
),
LIU Zuojun 2 (),
SONG Yinmao 1 ()
(1. College of Building Environment Engineering, Zhengzhou University of Light Industry, Zhengzhou 450002, China; 2. College of Artificial Intelligence and Data Science, Hebei University of Technology, Tianjin 300130, China)
© Shanghai Jiao Tong University and Springer-Verlag GmbH Germany, part of Springer Nature 2020 Abstract: The research purpose was to improve the accuracy in identifying the prosthetic leg locomotion mode. Surface electromyography (sEMG) combined with high-order zero-crossing was used to identify the prosthetic leg locomotion modes. sEMG signals recorded from residual thigh muscles were chosen as inputs to pattern classifier for locomotion-mode identification. High-order zero-crossing were computed as the sEMG features regarding locomotion modes. Relevance vector machine (RVM) classifier was investigated. Bat algorithm (BA) was used to compute the RVM classifier kernel function parameters. The classification performance of the particle swarm optimization-relevance vector machine (PSO-RVM) and RVM classifiers was compared. The BA-RVM produced lower classification error in sEMG pattern recognition for the transtibial amputees over a variety of locomotion modes: upslope, downgrade, level-ground walking and stair ascent/descent. Key words: intelligent prosthesis, surface electromyography (sEMG), relevance vector machine(RVM), high-order zero-crossing, bat algorithm (BA), locomotion-mode identification CLC number: TP 391 Document code: A
0 Introduction Due to stroke, spinal cord injury, brain injury and other reasons, the number of people with disabilities has been increasing rapidly. Powered lower limb prostheses can assist users in a variety of ambulation modes. At present, relatively mature prostheses have been produced, e.g., C-Leg from Otto Bock in Germany, Genium series prosthesis, Rheo Knee from Ossur in Iceland[1-2] . Researchers have started exploring different approaches for using residual muscles of lower limb amputees for powered lower limb prosthesis controllers. There were two categories: the biomechanical signals were collected such as lower limb joint angle, inertial navigation information, and foot pressure[3-6] to identify ambulation modes; another was directly continuous surface electromyography (sEMG) control[7-8] . sEMG signals were used as signal sources, and the research on ambulation mode recognition focuses on two aspects: the feature extraction method of sEMG signals and the classification algorithm of locomotionReceived: 2020-08-24 Accepted: 2020-09-24 Foundation item: the Center Plain Science and Technology Innovation Talents (No. 194200510016), the Science and Technology Innovation Team Project of Henan Province University (No. 19IRTSTHN013), and the Key Scientific Research Support Project for Institutions of Higher Learning in Henan Province (No. 18A413014) ∗E-mail: [email protected]
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