Liang Shengyun, Chu Tianyue, Lin Dan, Ning Yunkun, Li Huiqi, Zhao Guoru
Annu Int Conf IEEE Eng Med Biol Soc. 2018 Jul;2018:4401-4405. doi: 10.1109/EMBC.2018.8513119.
Accidental fall can cause physical injury, fracture and other health complication, especially for elderly people living alone. Aimed to provide timely assistance after the occurrence of falling down, a pre-fall alarm system was proposed. In order to test the reliability of pre-fall alarm system, eighteen subjects who worn this device on the waist were required to participate in a series of experiments. The acceleration and angular velocity time series extracted from human motion processes were used to described human motion features. HMM-based SVM classifier was used to determine the maximum separation boundary between fall and Activities of Daily Living (ADLs). The fall detection results showed 94.91% accuracy, 97.22% Sensitivity and 93.75% Specificity. The proposed device can accurately recognize fall event, achieve additional functions, and have advantages of small size and low power consumption. Based on the findings, this pre-impact fall alarm system with detection algorithm could potentially be useful for monitoring the state of physical function in elderly population.
意外跌倒可能导致身体受伤、骨折和其他健康并发症，尤其是对于独居的老年人。为了在跌倒发生后提供及时援助，提出了一种跌倒前报警系统。为了测试跌倒前报警系统的可靠性，要求18名将该设备佩戴在腰部的受试者参加一系列实验。从人体运动过程中提取的加速度和角速度时间序列用于描述人体运动特征。基于隐马尔可夫模型的支持向量机分类器用于确定跌倒与日常生活活动（ADL）之间的最大分离边界。跌倒检测结果显示准确率为94.91%，灵敏度为97.22%，特异性为93.75%。所提出的设备能够准确识别跌倒事件，实现附加功能，并且具有体积小、功耗低的优点。基于这些发现，这种带有检测算法的碰撞前跌倒报警系统可能对监测老年人群的身体功能状态有用。
