A wearable fall detector using inertial sensors and electromyography

• Main Authors: Liao, Sian-Ting, 廖顯庭
• Other Authors: Yang, Bing-Shiang
• Format: Others
• Language: zh-TW
• Published: 2011
• Online Access: http://ndltd.ncl.edu.tw/handle/04577504718750660872

碩士 === 國立交通大學 === 機械工程學系 === 99 === 　　　　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.