Abstract
Human Activity Recognition (HAR) is becoming increasingly useful for applications such as well-being monitoring and personalizing smart spaces. Traditional methods for HAR often require wearable devices or cameras. The former is not feasible for every environment and the latter has strong privacy concerns. The mmWave radar has been shown to be a promising alternative as it does not imply the same privacy concerns and does not require the users to have wearable devices. In this paper we have used a low- cost mmWave radar to generate micro-Doppler spectrograms to ultimately classify different activities. For this, multiple classifiers and methods of spectrogram filtering have been examined. Finally, a Time-Distributed Convolutional Neural Network in conjunction with a Bi-Directional Long Short-Term Memory has attained an average accuracy of 99.62% on a dataset of 5 activities, involving 2 participants.
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