Abstract
Based on the clinical evidence that head position measured by the multisensory system contributes to motion control, this study suggests a biomechanical human-central nervous system modeling and control framework for sit-to-stand motion synthesis. Motivated by the evidence for a task-oriented encoding of motion by the central nervous system, we propose a framework to synthesize and control sit-to-stand motion using only head position trajectory in the high-level-task-control environment. First, we design a generalized analytical framework comprising a human biomechanical model and an adaptive neuro-fuzzy inference system to emulate central nervous system. We introduce task-space training algorithm for adaptive neuro-fuzzy inference system training. The adaptive neuro-fuzzy inference system controller is optimized in the number of membership functions and training cycles to avoid over-fitting. Next, we develop custom human models based on anthropometric data of real subjects. Using the weighting coefficient method, we estimate body segment parameter. The subject-specific body segment parameter values are used (1) to scale human model for real subjects and (2) in task-space training to train custom adaptive neuro-fuzzy inference system controllers. To validate our modeling and control scheme, we perform extensive motion capture experiments of sit-to-stand transfer by real subjects. We compare the synthesized and experimental motions using kinematic analyses. Our analytical modeling-control scheme proves to be scalable to real subjects’ body segment parameter and the task-space training algorithm provides a means to customize adaptive neuro-fuzzy inference system efficiently. The customized adaptive neuro-fuzzy inference system gives 68%–98% improvement over general adaptive neuro-fuzzy inference system. This study has a broader scope in the fields of rehabilitation, humanoid robotics, and virtual characters’ motion planning based on high-level-task-control scheme.
Highlights
Sit-to-stand (STS) movement is a skill that helps determine the functional level of a person.[1]
This study proposes a modeling framework to evaluate the role of head position trajectory as a slow dynamics in central nervous system (CNS) to carry out STS motion
A modeling framework to emulate the role of head position trajectory in physiologically relevant STS motion control by the CNS is presented
Summary
Sit-to-stand (STS) movement is a skill that helps determine the functional level of a person.[1]. Studies have shown that human motion control and maintenance of balance by central nervous system (CNS) rely on inputs from vision, proprioception, and tactile/somatosensory and vestibular systems. The multisensory integration, combined with motion control, undergoes both quick and slow alterations which are termed as fast and slow dynamics in CNS respectively. CNS anticipates set patterns of inputs from multisensory systems. The anticipated pattern of signals is a function of slow dynamics in CNS, which is due to long-term processes of learning a motion pattern or changes in motion strategy due to aging or disease.[3] A study in Siriphorn et al.[4] evaluated the role of vision in STS by collecting various parameters like weight transfer time, rising index, and center of gravity (CoG) velocity sway during STS. Results showed that there were significant differences between the two trials and suggested that visual
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