Zakaria Nor Aini, Kuwae Yutaka, Tamura Toshiyo, Minato Kotaro, Kanaya Shigehiko
a Biomedical Imaging and Informatics Department, Nara Institute of Science and Technology , Nara , Japan.
Comput Methods Biomech Biomed Engin. 2015;18(4):426-37. doi: 10.1080/10255842.2013.805211. Epub 2013 Aug 21.
We examined falling risk among elderly using a wearable inertial sensor, which combines accelerometer and gyrosensors devices, applied during the Timed Up and Go (TUG) test. Subjects were categorised into two groups as low fall risk and high fall risk with 13.5 s duration taken to complete the TUG test as the threshold between them. One sensor was attached at the subject's waist dorsally, while acceleration and gyrosensor signals in three directions were extracted during the test. The analysis was carried out in phases: sit-bend, bend-stand, walking, turning, stand-bend and bend-sit. Comparisons between the two groups showed that time parameters along with root mean square (RMS) value, amplitude and other parameters could reveal the activities in each phase. Classification using RMS value of angular velocity parameters for sit-stand phase, RMS value of acceleration for walking phase and amplitude of angular velocity signal for turning phase along with time parameters suggests that this is an improved method in evaluating fall risk, which promises benefits in terms of improvement of elderly quality of life.
我们使用一种可穿戴惯性传感器对老年人的跌倒风险进行了检测，该传感器结合了加速度计和陀螺仪传感器设备，在定时起立行走（TUG）测试过程中使用。受试者被分为低跌倒风险和高跌倒风险两组，以完成TUG测试所用的13.5秒时长作为两组之间的阈值。一个传感器背侧附着在受试者的腰部，同时在测试过程中提取三个方向的加速度和陀螺仪传感器信号。分析分阶段进行：坐-弯、弯-站、行走、转身、站-弯和弯-坐。两组之间的比较表明，时间参数以及均方根（RMS）值、幅度和其他参数可以揭示每个阶段的活动。使用坐立阶段角速度参数的RMS值、行走阶段加速度的RMS值、转身阶段角速度信号的幅度以及时间参数进行分类表明，这是一种评估跌倒风险的改进方法，有望在改善老年人生活质量方面带来益处。
