Environment and Person-independent Gesture Recognition with Non-static RFID Tags Leveraging Adaptive Signal Segmentation

Gesture recognition for human machine interaction enhances the efficiency, safety, and usability of industrial and factory automation systems. We investigate hand-gesture recognition using battery-less body-worn reflective tags. Particularly, we propose two methods for hand gesture recognition using radio frequency identification (RFID). From backscattered signals we utilize in-phase and quadrature (IQ) constellation, as well as the phase. We convert extracted IQ samples into images and interprete them for gestures using a pre-trained VGG16. As a second approach we alternatively conduct pre-processing on the phase of the backscattered signals and propose Zero Crossing-Modified Derivative (ZCMD) for signal segmentation. Through signal resampling and wavelet denoising we mitigate undesired fluctuations introduced during this process, while retaining crucial signal characteristics. Subsequently, we integrate time-domain and frequency-domain features of the signals and train a random forest classifier based on these features to identify different gestures. Utilizing battery-free body-worn RFID tags, we are able to outperform a state-of-the art method and recognize four gestures with an accuracy of 81 % with the VGG16-based model. Employing phase, we achieve an accuracy of 94 %.