Human Activity Classification with Adaptive Thresholding using Radar Micro-Doppler

Radar systems are increasingly being used for healthcare applications for human activity recognition due to their advantages for privacy compliance, contactless sensing, and insensitivity to lighting conditions. The proposed classification algorithms are often very complex, hence requiring significant computational resources. We propose an adaptive thresholding algorithm used as a 'mask' to highlight the region of interest from the micro-Doppler signature. The mask is then applied to spectrogram information. These masked signatures are used for handcrafted feature extraction and classification. A quadratic-SVM classifier is employed based on the features from the information acquired. The preliminary results show that an accuracy of 91.3% is achieved using sequential forward feature selection with feature fusion. Based on our initial result, a Naïve Bayes combiner is used to improve the overall performance further. With this strategy, the accuracy of classification reaches 92.5% for six activities. Additionally, we compare our findings to those of other models utilizing the same database. The results demonstrate that high accuracy can be achieved when adaptive thresholding is used with the SVM method, and computational resources may significantly decrease.