标题: 整合惯性感测器与肌电讯号量测之穿戴式跌倒感测器
A wearable fall detector using inertial sensors and electromyography
作者: 廖显庭
Liao, Sian-Ting
杨秉祥
Yang, Bing-Shiang
机械工程学系
关键字: 跌倒;辨识;肌电讯号;惯性感测器;fall;detection;electromyography;inertial sensor
公开日期: 2010
摘要:   跌倒是高龄者事故伤害与死亡的主要原因,若能在跌倒发生时,快速且准确的侦测跌倒,有助于降低跌倒伤害,减少相关之医疗照护成本。惯性感测器已广泛用于人体动作辨识,本实验室先前研究则指出,肌电讯号有助于提升跌倒辨识速度。本研究承接过往之技术,藉由整合惯性感测器与肌电讯号量测,改善使用上之便利性,开发可穿戴式的即时跌倒辨识系统。
  本研究之跌倒感测器,由设置于使用者腰部前方的三轴加速规与三轴陀螺仪,以及双脚骨直肌上的机电讯号量测装置组成。实验共募集六位受测者(23.3±1.0 yrs; 168.7±6.4 cm; 60.70±3.98kg),在模拟环境中执行日常生活与模拟跌倒动作。日常动作包含蹲、坐、走与捡拾物品等;跌倒则模拟高龄者最常见的绊倒,在受测者行进时,使用绳索装置随机干扰受测者左脚或右脚的平衡,使受测者跌落防护软垫上。本研究使用肌电讯号与运动学资料,设立最佳化门槛值辨识跌倒。使用肌电讯号进行辨识时,由于量测参数有复数个,因此建立四种不同的整合辨识方法,并建立完整的统计评估流程,判断何种辨识方式具有最佳效能。
  使用惯性感测器辨识跌倒之Sensitivity为89.6%,Specificity为98.3%;肌电讯号之Sensitivity为72.9%,Specificity为92.0%,惯性系统辨识准确率较高,肌电讯号则在辨识速度具有优势。整合两者进行综合判断,Sensitivity为81.2%,Specificity为98.6%,虽然准确率比单独使用惯性感测器时下降,但在辨识速度上却有所提升,领先时间为264±178 毫秒,增加约50毫秒。在95%信心水准下,Sensitivity之信赖区间下限仍超过70%,证实利用本研究开发之跌倒辨识方法,此穿戴式感测器已经可以准确的侦测跌倒。
    Falls are leading causes of unintentional injuries and deaths in the elderly. To detect falls early and accurately is important for reducing fall-related socioeconomic cost. Inertial sensors have been used to distinguish fall from activities of daily living (ADLs). Our previous study found that using electromyography (EMG) signals on fall detection has advantage on recognition speed. Therefore, the aim of this study was to further develop a wearable fall detector by using inertial and electromyography combined sensors to detect fall events before impact.
    We have established a prototype of a fall detector, combing a tri-axis accelerometer and a tri-axis gyroscope, attached to the frontal surface of waist, and two-channel EMG sensors, recording the activities of bilateral rectus femoris muscles. Six subjects (23.3±1.0 yrs; 168.7±6.4 cm; 60.7±3.98kg) volunteered to the experiment for evaluating the performance of the fall detector. Each subject performed several ADLs in a mimic living environment and a few unexpected simulated trips, induced by a custom-made device attached to the ankle, were interspersed among the ADLs. Self-developed detecting algorithms were used to distinguish falls (trips) from ADLs, and a optimal detecting algorithm was then determined based on the detecting performance using a set of statistical analyses.
    Using inertial sensors alone could detect fall with 89.6% sensitivity and 98.3% specificity. By using EMG alone, sensitivity was 72.9% and specificity 92.0%. The fall detector using the combined sensor could identify falls before the impact between the human body and floor, with sensitivity of 81.2% and specificity of 98.6% with 264±178 ms mean lead time. To detect fall using inertial sensors are more accurate than using EMG signals alone. However the addition of EMG sensors to inertia sensors allowed our system to have about 50 ms more lead time. Besides, the lower limit of the 95% confidence interval of the system sensitivity was 73.0%, which may still be suitable for some real-life application. In conclusion, we have demonstrated the possibility of using EMG sensors combined with accelerometers to provide accurate and fast trip-fall detection.
URI: http://140.113.39.130/cdrfb3/record/nctu/#GT079714525
http://hdl.handle.net/11536/44685
显示于类别:Thesis