Significant Change Spotting for Periodic Human Motion Segmentation of Cleaning Tasks Using Wearable Sensors.
Significant Change Spotting for Periodic Human Motion Segmentation of Cleaning Tasks Using Wearable Sensors.
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DOI：
10.3390/s17010187
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发表时间：
2017-01-19
期刊：
影响因子：
--
通讯作者：
Chan CT
中科院分区：
文献类型：
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作者：
Liu KC;Chan CT
世界各地老龄化人口的比例正在迅速增加，这将给社会和医疗体系带来压力。近年来，科技的进步为日常生活活动(ADL)的自动监测创造了新的机会，以提高老年人的生活质量，为他们提供足够的医疗服务。这种自动的ADL监测需要在细粒度水平上的可靠的ADL信息，特别是对于真实世界中身体姿势与环境之间的交互状态。在这项工作中，我们提出了一种在清洁任务执行过程中周期性人体运动分割的显著变化检测机制。提出了一种新的基于手势显著变化搜索的方法，该方法可以处理活动识别中的关键技术问题，如连续数据分割、个体差异和类别歧义。三种典型的机器学习分类算法被用来识别显著变化候选，包括支持向量机(SVM)、k近邻(KNN)和朴素贝叶斯(NB)算法。总体而言，通过使用支持向量机分类器，该方法的F1得分达到了96.41%。实验结果表明，该方法能够满足ADL自动监测中细粒度人体运动分割的要求。
The proportion of the aging population is rapidly increasing around the world, which will cause stress on society and healthcare systems. In recent years, advances in technology have created new opportunities for automatic activities of daily living (ADL) monitoring to improve the quality of life and provide adequate medical service for the elderly. Such automatic ADL monitoring requires reliable ADL information on a fine-grained level, especially for the status of interaction between body gestures and the environment in the real-world. In this work, we propose a significant change spotting mechanism for periodic human motion segmentation during cleaning task performance. A novel approach is proposed based on the search for a significant change of gestures, which can manage critical technical issues in activity recognition, such as continuous data segmentation, individual variance, and category ambiguity. Three typical machine learning classification algorithms are utilized for the identification of the significant change candidate, including a Support Vector Machine (SVM), k-Nearest Neighbors (kNN), and Naive Bayesian (NB) algorithm. Overall, the proposed approach achieves 96.41% in the F1-score by using the SVM classifier. The results show that the proposed approach can fulfill the requirement of fine-grained human motion segmentation for automatic ADL monitoring.