【摘要】 独居老人摔倒等姿态检测是当今备受关注的问题.基于机器视觉的方法存在隐私侵入,成本高和实现过程复杂等问题,而基于加速度传感的方法对静止姿态识别存在困难.为此提出一种基于室内定位技术的老人姿态检测方案.首先在人体关键节点安装可穿戴接收标签,然后采用超宽带UWB测距方法,实现人体关键部位的定位和跟踪.在姿态估计算法中,分别采用最小二乘和改进的扩展卡尔曼滤波算法来抑制噪声,提高定位精度.仿真实验表明,改进的扩展卡尔曼滤波算法误差较小,可以较好地识别老人摔倒等姿态信息.更多还原

【Abstract】 Solitary elderly person posture recognition, especially when falling down, is a problem of concern today. The traditional method based on machine vision is flawed with too much privacy invasion, high cost and complex factors such as the implementation process, while the method based on acceleration sensor has a lower recognition rate in the stillness of the gesture. This paper introduces a new kind of body posture recognition scheme that employs indoor positioning technologies. The main job is to build an indoor positioning system, and paste tags to the key parts of the clothes and hat. The tags can receive ultra-wideband(UWB) signal from the positioning system. The UWB signal is used to get the distance which is important for the positioning. Finally, body posture can be easily recognized. In gesture recognition algorithm, this paper USES the least squares and the improved extend Kalman filter to suppress the noise of the distances measurement, so as to improve the accuracy of location. The simulation algorithm shows that the improved extend Kalman filter is effective.更多还原