权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

Portable Classification System of Daily Activity and Exercise Load

便携式日常活动和运动负荷分类系统

基本信息

批准号：
07555394
负责人：
INOOKA Hikaru
金额：
$ 1.54万
依托单位：
Tohoku University
依托单位国家：
日本
项目类别：
Grant-in-Aid for Scientific Research (B)
财政年份：
1995
资助国家：
日本
起止时间：
1995 至 1996
项目状态：
已结题

项目摘要

This study proposes new algorithm to classify and record human walking pattern and daily activity by using a portable measurement system.Classification method of human gait developed by Inooka et al is programd in the system. The method classifies the human gait into walking flat, going up the stairs and going down the stairs. The system performs real-time classification of human gait and records the estimated walking patterns into the Holter ECG recorder with the ECG of human. It is suggested that the system yields the relation between human walking patterns and the ECG.Modification algorithm of human walking path is also proposed because numerical error is included in the estimated walking path calculated by the integration of acceleration and angular velocity. This study utilizes the estimated action and already-known landmarks within an error ellipse. If one landmark has a relation to the estimated action, then the calculated path is modified to pass the landmark. Existence of a chair as a landmark is related to action of sitting down, for example.Moreover, we proposed a method to classify the human activity into four kinds of patterns : walking, rest, movement and lying. These activities are derived by statistical characteristics of acceleration and comparison the acceleration wave form with reference wave form already measured. Furthermore, the continuous activity is determined by an automaton of behavior transition which defines sequence of human behavior. The application of the automaton in running its course and tracing back is effective to the reliable estimation of human activity. The experimental results show that the human activity in a room is favorably estimated by using the automaton.

本研究提出了一种新的算法，利用一个便携式测量系统来分类和记录人的步行模式和日常活动，并在该系统中编程了Inooka等人提出的人步态分类方法。该方法将人的步态分为平地行走、上楼梯和下楼梯。该系统对人体步态进行实时分类，并将估计的步态模式与人体的心电图一起记录到霍尔特ECG记录仪中。该系统能够反映人体行走模式与心电图之间的关系，并针对加速度和角速度积分计算的行走路径存在数值误差的问题，提出了人体行走路径的修正算法。这项研究利用了估计的行动和已知的地标内的误差椭圆。如果一个地标与所估计的动作有关，则修改所计算的路径以通过该地标。例如，作为地标的椅子的存在与坐下的动作有关。此外，我们提出了将人类活动分为四种模式的方法：步行，休息，运动和躺下。这些活动是通过加速度的统计特性和加速度波形与已测量的参考波形的比较得出的。此外，连续的活动是由一个自动机的行为转变，它定义了人类的行为序列。自动机在运行过程和回溯过程中的应用对人类活动的可靠估计是有效的。实验结果表明，该自动机能较好地估计房间内的人类活动。