HEALTHY STATE MONITOR OF UPPER LIMB FOR SPACE FLIGHT TASK BASED ON SIGNAL ANALYSES OF MULTIPLE MUSCLE FORCES
HEALTHY STATE MONITOR OF UPPER LIMB FOR SPACE FLIGHT TASK BASED ON SIGNAL ANALYSES OF MULTIPLE MUSCLE FORCES
复制标题
基于多肌力信号分析的航天任务上肢健康状态监测
DOI：
10.1142/s0219519417500610
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发表时间：
2017-06
影响因子：
0.8
通讯作者：
李凡
中科院分区：
文献类型：
--
作者：
李凡
提出了一种新的航天飞行任务上肢健康状态监测方法。该方法无需携带其他复杂的在轨诊断设备，只需使用普通的运动仪器采集和分析航天员的多种肌力，即可推断出航天员上肢肌群出现严重肌肉萎缩的部位。首先，对典型的上肢多肌力数据进行了积累。进行了为期45天的6度头低位卧床休息实验和多肌力测试实验，收集相应的数据。这些数据既包括健康状态的肌力数据，也包括不健康状态的相关数据。其次，利用小波包变换(WPT)和经验模式分解(EMD)方法计算上述数据的信号特征。第三，利用相关信号特征训练支持向量机分类器。最后，利用训练好的支持向量机对航天员在轨上肢的健康状况进行评估。如果支持向量机的输出为负，则可以使用C均值方法和欧几里德距离来定位异常的肌力和肌群。重点介绍了上肢典型肌群健康状态评价的概念。对传统的基于诊断的方法、基于肌电(EMG)的肌力分析方法和本文提出的方法进行了比较。大量的地面实验结果验证了该方法的有效性。
A novel healthy state monitor method of upper limb for space flight task is proposed. Without taking other complex diagnosis equipment in orbit, this method only uses the ordinary exercise instruments to collect and analyze the multiple muscle forces of astronauts, and deduces where the serious muscle atrophy occurs in their muscle groups of upper limb. First, the typical multiple muscle forces data of upper limb are accumulated. A 45-day 6-degree head-down tilt bed rest experiment together with a multiple muscle forces test experiment are carried out to collect the corresponding data. These data include both the muscle force data of healthy state and the related data of unhealthy state. Second, the Wavelet Packet Transform (WPT) and the Empirical Mode Decomposition (EMD) methods are used to compute the signal features of these data above. Third, a Support Vector Machine (SVM) classifier is trained by the related signal features. Finally, the trained SVM can be utilized to evaluate the healthy state of upper limb in orbit for astronaut. If the output of SVM is negative, the C-means method and the Euclidean distance can be used to locate the abnormal muscle forces and muscle groups. The concept of typical muscle group health state evaluation for upper limb is emphasized in this paper. The comparisons among the traditional diagnosis-based method, the electromyogram (EMG)-based muscle forces analysis method, and the proposed method are made. Many experiment results on ground have verified the effectiveness of proposed method.
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影响因子：
0.8
作者：
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通讯作者：
Mourad Talbi;S. Abid;A. Cherif
影响因子：
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作者：
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通讯作者：
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影响因子：
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作者：
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影响因子：
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作者：
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通讯作者：
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DOI：
10.1109/tnsre.2015.2454503
发表时间：
2016-07-01
影响因子：
4.9
作者：
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通讯作者：
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