School of Electrical Engineering and Telecommunications, University of New South Wales, Sydney, NSW 2052, Australia.
College of Information Science and Engineering, Northeastern University, Shenyang 110819, China.
Sensors (Basel). 2021 Oct 27;21(21):7130. doi: 10.3390/s21217130.
The growing problem of aging has led to a social concern on how to take care of the elderly living alone. Many traditional methods based on visual cameras have been used in elder monitoring. However, these methods are difficult to be applied in daily life, limited by high storage space with the camera, low-speed information processing, sensitivity to lighting, the blind area in vision, and the possibility of revealing privacy. Therefore, wise information technology of the Med System based on the micro-Doppler effect and Ultra Wide Band (UWB) radar for human pose recognition in the elderly living alone is proposed to effectively identify and classify the human poses in static and moving conditions. In recognition processing, an improved PCA-LSTM approach is proposed by combing with the Principal Component Analysis (PCA) and Long Short Term Memory (LSTM) to integrate the micro-Doppler features and time sequence of the human body to classify and recognize the human postures. Moreover, the classification accuracy with different kernel functions in the Support Vector Machine (SVM) is also studied. In the real experiment, there are two healthy men and one woman (22-26 years old) selected to imitate the movements of the elderly and slowly perform five postures (from sitting to standing, from standing to sitting, walking in place, falling and boxing). The experimental results show that the resolution of the entire system for the five actions reaches 99.1% in the case of using Gaussian kernel function, so the proposed method is effective and the Gaussian kernel function is suitable for human pose recognition.
人口老龄化问题日益严重，如何照顾独居老人已成为社会关注的焦点。传统的基于视觉摄像头的老人监测方法已经得到广泛应用。然而，这些方法在日常生活中难以应用，受到摄像头存储空间大、信息处理速度慢、对光照敏感、视觉盲区以及可能泄露隐私等因素的限制。因此，提出了一种基于微多普勒效应和超宽带（UWB）雷达的 Med 系统智能信息技术，用于识别和分类独居老人的人体姿势。在识别处理中，提出了一种改进的 PCA-LSTM 方法，通过结合主成分分析（PCA）和长短时记忆（LSTM），将人体的微多普勒特征和时间序列进行集成，以分类和识别人体姿势。此外，还研究了支持向量机（SVM）中不同核函数的分类准确性。在实际实验中，选择了两名健康男性和一名女性（22-26 岁）来模拟老年人的动作，并缓慢完成五种姿势（从坐到站、从站到坐、原地行走、跌倒和拳击）。实验结果表明，在使用高斯核函数的情况下，整个系统对这五种动作的分辨率达到 99.1%，因此，所提出的方法是有效的，高斯核函数适用于人体姿势识别。
