Development and evaluation of a prior-to-impact fall event detection algorithm.
Development and evaluation of a prior-to-impact fall event detection algorithm.
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DOI：
10.1109/tbme.2014.2315784
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发表时间：
2014-07
期刊：
影响因子：
--
通讯作者：
Lockhart TE
中科院分区：
文献类型：
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作者：
Liu J;Lockhart TE
自动跌倒事件检测因其在跌倒报警系统和穿戴式跌倒伤害预防系统中的潜在应用而受到近年来的研究关注。然而，现有的跌倒检测研究面临着种种局限性。目前的研究旨在利用二维信息（即躯干角速度和躯干角度）开发和验证一种新的跌倒检测算法。10名健康的老年人参与了一项实验室研究。使用惯性测量装置测量了在滑倒和各种日常活动中矢状躯干的角运动学。平均而言，新算法能够在撞击之前检测到向后跌倒，灵敏度为100%，特异性为95.65%，响应时间为255 ms。因此，可以得出结论，新的跌倒检测算法能够有效地检测老年人运动中的跌倒。
Automatic fall event detection has attracted research attention recently for its potential application in fall alarming system and wearable fall injury prevention system. Nevertheless, existing fall detection research is facing various limitations. The current study aimed to develop and validate a new fall detection algorithm using 2-D information (i.e., trunk angular velocity and trunk angle). Ten healthy elderly were involved in a laboratory study. Sagittal trunk angular kinematics was measured using inertial measurement unit during slip-induced backward falls and a variety of daily activities. The new algorithm was, on average, able to detect backward falls prior to impact, with 100% sensitivity, 95.65% specificity, and 255 ms response time. Therefore, it was concluded that the new fall detection algorithm was able to effectively detect falls during motion for the elderly population.