Implementation of a real-time human movement classifier using a triaxial accelerometer for ambulatory monitoring
Implementation of a real-time human movement classifier using a triaxial accelerometer for ambulatory monitoring
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DOI：
10.1109/titb.2005.856864
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发表时间：
2006-01-01
影响因子：
--
通讯作者：
Celler, BG
中科院分区：
文献类型：
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作者：
Karantonis, DM;Narayanan, MR;Celler, BG
对人体运动的实时监测可以提供关于个体的功能能力程度和一般活动水平的有价值的信息。本文提出了一种实时分类系统的实现与从一个单一的，腰上安装的三轴加速度计单元采集的数据相关联的人体运动的类型。该系统提出的主要进步是使用嵌入式智能在可穿戴设备上执行绝大多数信号处理。以这种方式，系统区分活动和休息时段，识别佩戴者的姿势取向，检测诸如行走和福尔斯的事件，并且提供代谢能量消耗的估计。进行了一项涉及6名受试者的实验室试验，结果表明，在涉及与正常日常活动相关的各种运动的一系列12项任务（283次测试）中，总体准确率为90.8%。活动和休息之间的区别没有错误;姿势方向的识别准确率为94.1%，行走分类的确定性较低（准确率为83.3%），检测可能的福尔斯的准确率为95.6%。结果表明，实现基于加速度计，实时运动分类器，使用嵌入式智能的可行性。
The real-time monitoring of human movement can provide valuable information regarding an individual's degree of functional ability and general level of activity. This paper presents the implementation of a real-time classification system for the types of human movement associated with the data acquired from a single, waist-mounted triaxial accelerometer unit. The major advance proposed by the system is to perform the vast majority of signal processing onboard the wearable unit using embedded intelligence. In this way, the system distinguishes between periods of activity and rest, recognizes the postural orientation of the wearer, detects events such as walking and falls, and provides an estimation of metabolic energy expenditure. A laboratory-based trial involving six subjects was undertaken, with results indicating an overall accuracy of 90.8% across a series of 12 tasks (283 tests) involving a variety of movements related to normal daily activities. Distinction between activity and rest was performed without error; recognition of postural orientation was carried out with 94.1% accuracy, classification of walking was achieved with less certainty (83.3% accuracy), and detection of possible falls was made with 95.6% accuracy. Results demonstrate the feasibility of implementing an accelerometry-based, real-time movement classifier using embedded intelligence.