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基于惯性体传感器网络的预冲击跌倒识别方法的探索与实现。

Exploration and implementation of a pre-impact fall recognition method based on an inertial body sensor network.

机构信息

Shenzhen Key Laboratory for Low-cost Healthcare, and Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China.

出版信息

Sensors (Basel). 2012 Nov 8;12(11):15338-55. doi: 10.3390/s121115338.

Abstract

The unintentional injuries due to falls in elderly people give rise to a multitude of health and economic problems due to the growing aging population. The use of early pre-impact fall alarm and self-protective control could greatly reduce fall injuries. This paper aimed to explore and implement a pre-impact fall recognition/alarm method for free-direction fall activities based on understanding of the pre-impact lead time of falls and the angle of body postural stability using an inertial body sensor network. Eight healthy Asian adult subjects were arranged to perform three kinds of daily living activities and three kinds of fall activities. Nine MTx sensor modules were used to measure the body segmental kinematic characteristics of each subject for pre-impact fall recognition/alarm. Our analysis of the kinematic features of human body segments showed that the chest was the optimal sensor placement for an early pre-impact recognition/alarm (i.e., prediction/alarm of a fall event before it happens) and post-fall detection (i.e., detection of a fall event after it already happened). Furthermore, by comparative analysis of threshold levels for acceleration and angular rate, two acceleration thresholds were determined for early pre-impact alarm (7 m/s/s) and post-fall detection (20 m/s/s) under experimental conditions. The critical angles of postural stability of torso segment in three kinds of fall activities (forward, sideway and backward fall) were determined as 23.9 ± 3.3, 49.9 ± 4.1 and 9.9 ± 2.5 degrees, respectively, and the relative average pre-impact lead times were 329 ± 21, 265 ± 35 and 257 ± 36 ms. The results implied that among the three fall activities the sideway fall was associated with the largest postural stability angle and the forward fall was associated with the longest time to adjust body angle to avoid the fall; the backward fall was the most difficult to avoid among the three kinds of fall events due to the toughest combination of shortest lead time and smallest angle of postural stability which made it difficult for the self-protective control mechanism to adjust the body in time to avoid falling down.

摘要

老年人因跌倒导致的意外伤害,由于人口老龄化的不断增加,引发了大量的健康和经济问题。使用早期的预冲击跌倒警报和自我保护控制可以大大减少跌倒伤害。本文旨在探索和实现一种基于理解跌倒前冲击时间和身体姿势稳定性角度的自由方向跌倒活动的预冲击跌倒识别/警报方法,使用惯性体传感器网络。安排 8 名健康的亚洲成年受试者进行三种日常活动和三种跌倒活动。使用九个 MTx 传感器模块测量每个受试者的身体节段运动学特征,用于预冲击跌倒识别/警报。我们对人体节段运动学特征的分析表明,胸部是进行早期预冲击识别/警报(即在跌倒事件发生之前预测/警报)和跌倒后检测(即检测已经发生的跌倒事件)的最佳传感器放置位置。此外,通过对加速度和角速率阈值的比较分析,确定了两个加速度阈值,用于在实验条件下进行早期预冲击警报(7 m/s/s)和跌倒后检测(20 m/s/s)。三种跌倒活动(前向、侧向和后向跌倒)中躯干段姿势稳定性的临界角度分别确定为 23.9 ± 3.3、49.9 ± 4.1 和 9.9 ± 2.5 度,相对平均预冲击前导时间分别为 329 ± 21、265 ± 35 和 257 ± 36 ms。结果表明,在三种跌倒活动中,侧向跌倒与最大姿势稳定性角度相关,而前向跌倒与调整身体角度以避免跌倒的时间最长相关;由于最短的前导时间和最小的姿势稳定性角度的最艰难组合,后向跌倒是三种跌倒事件中最难避免的,这使得自我保护控制机制难以及时调整身体以避免跌倒。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/18c4/3522966/ce975de0a74d/sensors-12-15338f1.jpg

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