Abstract
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.
Highlights
The economic consequences of an aging population published by the United Nations in 1956 established the population classification standard
We propose a human posture recognition method based on Ultra Wide Band (UWB) radar, and the structure of the proposed system is shown in Figure 2: 1. UWB radar is responsible for collecting human posture data, including: from standing to sitting, from sitting to standing, walking in place, falling, periodic boxing
In Ref. [32], the energy distribution characteristics of UWB radar signals of eight human postures are extracted by wavelet packet decomposition method, and the parameters C and σ of Support Vector Machine (SVM) are optimized by using improved chaos adaptive genetic algorithm (ICAGA)
Summary
The economic consequences of an aging population published by the United Nations in 1956 established the population classification standard. As a traditional non-contact sensor, the camera has high storage space requirements and information processing ability and is sensitive to light and other conditions Both camera and radar observation methods are developed based on non-wearable devices. We propose a non-contact monitoring device based on UWB radar, which can effectively solve dim light and blind area problems. Because of its high resolution, intense penetration, and low power consumption, UWB radar can effectively overcome the lighting and privacy problems of the camera, ensure all-weather work, and effectively solve the problem that the optical system does not work well due to occlusion It acts as an essential role in the monitoring of the elderly. An improved algorithm based on PCA-LSTM is proposed to integrate micro-Doppler features and time-sequence to recognize human posture.
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