使用信号分析和机器学习的身体动作分类
Physical Action Categorization using Signal Analysis and Machine Learning
摘要：
全球成千上万的人的日常生活因与肢体运动有关的身体或精神残疾而遭受痛苦。这些人的生活质量可以通过使用辅助应用程序和系统得到改善。在这种情况下，从运动到计算机辅助应用程序的物理动作的映射可以为解决方案指明方向。表面肌电图（sEMG）是一种非侵入性的机制，通过它我们可以将身体运动转化为信号进行分类和应用。在本文中，我们提出了一个基于机器学习的框架分类的4个物理动作。该框架从时域、频域、高阶统计量和通道间统计量的贡献出发，研究了不同模态的各种特征。接下来，我们使用该特征集对k-NN、SVM和ELM分类器进行了对比分析。还记录了特征集的不同组合的效果。最后，分类器的准确性与SVM和1-NN为基础的分类器的一个子集的功能分别给出了95.21和95.83的准确性。此外，我们还提出，通过使用PCA进行降维，在仅使用原始特征集的9.22%的情况下，准确率仅下降不到5.55%。这些发现有助于算法设计者在考虑算法执行所需资源的情况下选择最佳方法。
英文摘要：
Daily life of thousands of individuals around the globe suffers due to physical or mental disability related to limb movement. The quality of life for such individuals can be made better by use of assistive applications and systems. In such scenario, mapping of physical actions from movement to a computer aided application can lead the way for solution. Surface Electromyography (sEMG) presents a non-invasive mechanism through which we can translate the physical movement to signals for classification and use in applications. In this paper, we propose a machine learning based framework for classification of 4 physical actions. The framework looks into the various features from different modalities which contribution from time domain, frequency domain, higher order statistics and inter channel statistics. Next, we conducted a comparative analysis of k-NN, SVM and ELM classifier using the feature set. Effect of different combinations of feature set has also been recorded. Finally, the classifier accuracy with SVM and 1-NN based classifier for a subset of features gives an accuracy of 95.21 and 95.83 respectively. Additionally, we have also proposed that dimensionality reduction by use of PCA leads to only a minor drop of less than 5.55% in accuracy while using only 9.22% of the original feature set. These finding are useful for algorithm designer to choose the best approach keeping in mind the resources available for execution of algorithm.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
