基于人体加速度多特征融合和K近邻算法的跌倒检测

Fall Detection Based on Multi-feature Fusion of Human Body Acceleration and K-Nearest Neighbor

目的探索基于人体加速度的跌倒检测方法。方法 2017年9月至11月,6例健康青年志愿者完成13个跌倒动作和11个日常活动动作。通过两个加速度传感器采集人体动作信息,每个加速度传感器提取81个加速度特征参数。通过主成分分析降维,输入K近邻(KNN)算法分类器对跌倒和日常动作进行识别。结果跌倒检测敏感性100%,特异性99.76%,检测时间216 ms。结论加速度多特征融合和KNN算法可以实现跌倒的及时有效检测。

Objective To develop a kind of algorithm for fall detection based on human acceleration.Methods From September to November,2017,six healthy postgraduates participating in the experiment completed 13 acts of falls and eleven of activities of daily life.The information of activities was collected through two acceleration sensors,81 acceleration features were extracted from each sensor,and were reduced dimension through principal component analysis.K-nearest neighbor was used to detect the falls and activities of daily living.Results The sensitivity of fall detection was 100%,the specificity was 99.76%,and the detection time was 216 ms.Conclusion The algorithm of multi-feature fusion of human body acceleration and K-nearest neighbor is accurate and timely.