基于肌电信号的人体站立扰动平衡过程中踝关节力矩的计算

基于肌电信号的人体站立扰动平衡过程中踝关节力矩的计算(任务书,开题报告,论文15000字)
摘要
    我们通常说的表面肌电信号是指在肌肉表面通过电极获取并记录下来的神经肌肉系统活动时的生物电信号，它和肌肉的功能状态和活动状态有着不同程度的联系，由于不同的肢体活动是由不同的肌肉收缩模式产生的，所以肌电信号的特征也存在着差异，通过对表面肌电信号特征进行分析就可以区分不同的动作。在现代科技领域，表面肌电信号的研究已经扩展到了临床医学、运动医学、生物医学与工程等诸多领域，并且已经得到了广泛的应用，同时在智能假肢的研究方面，表面肌电信号已经成为功能性点刺激的理想控制信号。
    本文通过对表面肌电信号进行分析，探索肌电信号与踝关节力矩之间的关系。在对肌电信息进行提取时采用表面肌电信号检测，其优点是对人体无损伤，能在一定程度上反映神经肌肉的活动，由于表面肌电信号是一种非常微弱的电信号，故在对其进行采集时，要消除噪声和干扰，提高其保真度。在对实验进行分析时，采用贝叶斯线性回归，利用MATLAB软件进行数据分析，来描述肌电信号和关节力矩的数学关系，最后验证结果发现肌电信号与踝关节力矩之间存在线性关系。
关键词：表面肌电信号  肌肉模型   线性回归  关节力矩
 
Abstract
  We usually say that the surface of the EMG signal refers to the muscle surface through the electrode to obtain and record the neuromuscular system activity of the biological signal, it and the muscle function status and activity status has a different degree of contact, due to different physical activity Is different from the muscle contraction mode, so the characteristics of EMG signals are also different, by analyzing the characteristics of the surface of EMG signal can distinguish between different actions. In the field of modern science and technology, the study of surface electromyography has been extended to many fields such as clinical medicine, sports medicine, biomedicine and engineering, and has been widely used. At the same time, the surface EMG signal has become Functional point to stimulate the ideal control signal.
    In this paper, the surface of the EMG signal analysis, to explore the relationship between EMG signal and ankle joint torque. In the extraction of EMG information using surface EMG signal detection, the advantage is no damage to the human body, to a certain extent, reflect the activities of neuromuscular, because the surface EMG signal is a very weak electrical signal, so in the When collecting, to eliminate noise and interference, to improve its fidelity. In the analysis of the experiment, Bayesian linear regression was used to analyze the mathematical relationship between the EMG signal and the joint torque using MATLAB software. Finally, the results showed that there was a linear relationship between the EMG signal and the ankle joint torque.
Key words: sEMG   muscle model   linear regression    joint moment
 
目录
第一章 绪论    1
1.1 课题研究背景    1
1.2 表面肌电信号特征提取和识别研究的发展现状    1
1.2.1表面肌电信号特征提取方法    1
1.2.2 表面肌电信号模式识别方法    3
1.3 人体骨骼肌肉系统力学计算技术的研究现状    3
1.4 智能假肢的研究现状    4
1.5 本文研究的主要内容    5
第二章 肌电信号的采集和预处理    6
2.1 表面肌电信号特点    6
2.2 表面肌电信号的采集    6
2.2.1 肌电信号采集方式    6
2.2.2 表面肌电信号采集设备    7
2.2.3 表面肌电信号采集过程    8
2.3 表面肌电信号的预处理    11
2.3.1 噪声来源    11
2.3.2 小波去噪    12
2.3.3 阈值去噪    12
第三章 肌肉模型    16
3.1 HILL肌肉模型    16
3.1.1 收缩元力    17
3.1.2 串联弹性元力和并联弹性元力    17
3.2 肌肉生理结构    17
3.2.1 肌肉横截面积    17
3.2.2 肌肉结构指数    18
3.2.3 肌肉最大收缩速度    19
第四章  数据分析与处理    20
4.1 数据采集过程    20
4.2 数据处理与研究结果    21
4.2.1 线性回归分析    22
第五章 总结    24
参考文献    25
致谢    26