title: Subject-specific lower limb waveforms planning via artificial neural network.
abstract: Robotic is gaining its popularity in gait rehabilitation. Gait pattern planning is important, in order to ensure the gait patterns induced by robotic systems on the patient are natural and smooth. It is known that the gait parameters (stride length, cadence) are the key factors, which affect gait pattern. However, a systematic methodology for gait pattern planning is missing. Therefore, a gait pattern generation methodology, GaitGen, was proposed in our previous work. In this paper, we introduce a new model to enhance the proposed methodology for generating the joint angle waveforms of the lower limb during walking, with the gait parameters and the lower limb anthropometric data as input. The walking motion was captured with a motion capture system using passive markers. The waveforms of lower limb joint angles were calculated from the experimental data and the waveforms were then decomposed into Fourier coefficients. Therefore, each joint angle waveform can be represented by a Fourier coefficient vector containing eleven elements to facilitate the waveform analysis. Multi-layer perceptron neural networks (MLPNNs) were designed to predict the Fourier coefficient vectors for specific subject and desired gait parameters. Assessment parameters such as correlation coefficient, mean absolute deviation (MAD) and threshold absolute deviation (TAD) were calculated to examine the quality of MLPNNs' prediction. The constructed waveforms from predicted Fourier coefficient vectors were compared with the actual waveforms calculated from experimental data by using the above-mentioned assessment parameters. The results show that the constructed waveforms closely match the experimental waveforms based on the assessment parameter outcomes.
authors.affiliation: School of Mechanical and Aerospace Engineering, Nanyang Technological University (NTU), Singapore 639798.
authors.forename: Trieu Phat
authors.initials: TP
authors.lastname: Luu
authors.forename: H B
authors.initials: HB
authors.lastname: Lim
authors.forename: Xingda
authors.initials: X
authors.lastname: Qu
authors.forename: K H
authors.initials: KH
authors.lastname: Hoon
authors.forename: K H
authors.initials: KH
authors.lastname: Low
mesh_terms: D000293:Adolescent; D000328:Adult; D005684:Gait / Q000502:physiology; D006801:Humans; D035002:Lower Extremity / Q000502:physiology*; D008297:Male; D016571:Neural Networks, Computer; D012371:Robotics / Q000295:instrumentation* / Q000379:methods*; D016138:Walking / Q000502:physiology; D055815:Young Adult