Indoor Human Activity Recognition Based on Ambient Radar with Signal Processing and Machine Learning

Indoor human activity recognition has been extensively investigated. However, most of the solutions require sensors e.g. 9-axis IMU be equipped on human body or use image processing that presents privacy issues. This work proposes an ambient radar sensor based a solution to recognize the activities that humans normally perform in indoor environments. This solution uses a 7.8 GHz radar to emit 16 pulse signals every second and samples the reflected signals at 128 KHz to capture the fine dynamics of human activities. This solution designs a set of data preprocessing algorithms, including a data refining algorithm to filter outlier data, a contrastive divergence algorithm to remove background static reflection, and a transformation algorithm to convert the signal data into feature- rich spatial location changes. This solution also develops schemes to separate a collection of various activities into individuals. A lowpass frequency filter is designed to remove unwanted noisy data and the motion intensity is used to classify the activities into two high-level groups. It uses a slope-based approach and a k- means clustering to further finely recognize each activity. This solution has been extensively evaluated in a spacious research lab room and shows outstanding accuracy.