摘要
针对目前跌倒检测系统准确率低、应用场景单一等问题,提出一种基于特征参数变化量的跌倒检测算法。该算法通过采集合加速度幅值的实时变化量,为人体运动剧烈程度提供更直观准确的分析。由于运动过程中人体竖直方向相对于水平地面的角度随步伐呈周期性变化,算法对走路和上下楼梯行为进行准确区分,通过实验确定不同运动状态下的报警阈值,大大提高了跌倒检测准确率。系统采用Protothread模型实现并行检测,在减小系统开销条件下满足实时多任务需求。实验证明,改进后的跌倒检测算法将准确度和特异性分别维持在98.4%和99.1%,在保证实时性和便携性的同时能更有效地识别跌倒。
Aiming at the shortcomings of the current fall detection system such as low accuracy and single application scenario,a fall detection algorithm based on the variation of feature parameters is proposed and the system parallel detection is realized.The algorithm provides a more intuitive and accurate analysis of the intensity of human motion by collecting the real-time variation of the combined acceleration amplitude.Due to the vertical direction of the human body to the horizontal ground changes periodically with the pace,the walking and going upstairs and downstairs can be accurately distinguished.Through a large number of experiments to determine the respective alarm threshold under different state of motion,the accuracy of the fall detection is greatly improved.The Protothread model is also adopted to meet the demand of real-time multitask under the condition of reducing the system overhead.The experiment shows that the improved fall detection algorithm maintains the accuracy and specificity of 98.4%and 99.1%respectively,and the system can identify the fall more effectively while ensuring the real time and portability.
作者
闫肃
肖明波
YAN Su;XIAO Ming-bo(School of Communication Engineering,Hangzhou Dianzi University,Hangzhou 310018,China)
出处
《软件导刊》
2019年第1期77-80,85,共5页
Software Guide
基金
浙江省重点实验室建设基金项目(GK130907208001)
