Feasibility Study on Radar-based Human Activity Recognition

The growing interest in employing radar for human activity recognition is driven by the exponential rise in the incidence and risk of falls associated with aging, compounded by diminished leg strength, prolonged medication side effects, visual impairments, and other variables that contribute to decreasing strength. In comparison to contact devices and other non-contact devices, radar exhibits considerable advantages in terms of non-contact capability, accuracy, resilience, detection range, and privacy security. Radar-based Human Activity Recognition (HAR) works by using a Doppler frequency shift to figure out what people are doing. This shift creates unique Doppler signatures. The Doppler frequency shift is when electromagnetic waves change their frequency and wavelength depending on how fast the observer is moving compared to the source. This paper presents Radar based human activity recognition based on a convolutional neural network. Specifically, this paper utilized public datasets available by University of Glasgow, United Kingdom. The radar utilizes Novelda's X4 system-on-chip (SoC), with an integrated receiver and transmitter antenna, providing very precise distance and motion measurements. The target was located 0.45 meters from the radar at the time of data collection. The investigation makes use of PyTorch to implement classification through CNN architectures. The CNN model demonstrates effective ability to detect human activities within radar-based RF images. Although the model proves resilient it requires a larger collection of labelled data to reach higher performance standards.