| Summary: | 碩士 === 國立臺灣師範大學 === 機電科技研究所 === 100 === The analysis of human equilibrium, also known as postural stability, is a topic of great interest for the brain research and medicine community. A wide range of techniques and methodologies has been developed, but the choice of instrumentations and sensors depends on the requirement of the specific application. In this paper, we propose a methodology to analyze the human sway dynamics under several different stability conditions. The proposed methodology consists of three major steps: Firstly, the human postural sway acceleration signals were collected by using some 3-axis MEMS accelerometers. Secondly, we use a zero phase filter, named Iterative Gaussian Filter (IGF), to remove the noise from the collected signals. Thirdly, a popular complexity measure, named multi-scale entropy (MSE), is used to quantify the complexity of the filtered acceleration signal. We found that the MSE curves can be used to quantify different human stability conditions. The MSE curves derived from weak-stability-conditions lies below that derived from strong-stability conditions. Several experiments were designed to study the effect of the vision (eyes closed, eyes open) and brain resources (calculation, reading) on the human postural stability. Through the experimental results, we give two hypotheses: (1) loss of vision will decrease the human postural stability; (2) decrease of the brain resources will decrease the human postural stability. These experimental results demonstrated the feasibility and effectiveness of the proposed methodology on the analysis of human postural sway dynamics.
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