In this work, a hybrid radio frequency (RF)- and acoustic-based activity recognition system was developed to demonstrate the advantage of combining two non-invasive sensors in Human Activity Recognition (HAR) systems and smart assisted living. We used a hybrid approach, employing RF and acoustic signals to recognize falling, walking, sitting on a chair, and standing up from a chair. To our knowledge, this is the first work that attempts to use a mixture of RF and passive acoustic signals for Human Activity Recognition purposes. We conducted experiments in the lab environment using a Vector Network Analyzer measuring the 2.4 GHz frequency band and a microphone array. After recording data, we extracted the Mel-spectrogram feature of the audio data and the Doppler shift feature of the RF measurements. We fed these features to six classification algorithms. Our result shows that using a hybrid acoustic- and radio-based method increases the accuracy of recognition compared to just using only one kind of sensory data and shows the possibility of expanding for a variety of other different activities that can be recognized. We demonstrate that by using a hybrid method, the recognition accuracy increases in all classification algorithms. Among these classifiers, five of them achieve over 98% recognition accuracy.
Keywords: RF-based HAR; acoustic-based HAR; data fusion; human activity recognition; hybrid activity recognition; machine learning.