Arm Motion Classification Using Time-Series Analysis of the Spectrogram Frequency Envelopes

Abstract

Hand and arm gesture recognition using radio frequency (RF) sensing modality proves valuable in man-machine interfaces and smart environments. In this paper, we use the time-series analysis method to accurately measure the similarity of the micro-Doppler (MD) signatures between the training and test data, thus providing improved gesture classification. We characterize the MD signatures by the maximum instantaneous Doppler frequencies depicted in the spectrograms. In particular, we apply two machine learning (ML) techniques, namely, the dynamic time warping (DTW) method and the long short-term memory (LSTM) network. Both methods take into account the values as well as the temporal evolution and characteristics of the time-series data. It is shown that the DTW method achieves high gesture classification rates and is robust to time misalignment.

People Walking Classification Using Automotive Radar

“…Through the micro-Doppler components it is also possible to characterize a person's movement or to identify a fall [11]. Moreover, micro-Doppler features have been often exploited in several aspects of human recognition, such as arm motion analysis [12], identification of target human motions [13], or to distinguish people walking in a noisy background [14]. Low power Frequency Modulated Continuous Wave (FMCW) radar and micro-Doppler tracks have been recently used with various scopes, such as discriminating armed from unarmed people [15], identifying people on the basis of their gait characteristics [16][17][18] or their movements [19], and for gestures recognition [20].…”

Identification of Human Motion Using Radar Sensor in an Indoor Environment

“…Moreover, studies on classifying human motions using deep learning models trained with radar data sets have been actively conducted [ 13 ]. For example, a method of classifying arm motions by applying deep learning techniques has also been proposed [ 14 ]. Based on these studies, we propose a more advanced method that finds the moment when a person’s motion changes and at the same time discriminates the motion using a radar sensor in an indoor environment.…”

Analysis of Non-Intrusive Hand Trajectory Tracking by Utilizing Micro-Doppler Signature Obtained From Wi-Fi Channel State Information

“…Therefore, the entire object is assumed to produce only a single Doppler component at a particular time window. However, it seems that the point object assumption is not always valid for human motion sensing including hand motion since multiple Doppler components could be observed from these movements as showed in previous works of CSI-based human motion sensing [27]- [29]. In the case of hand motion, various Doppler components are generated from the fact that each part of the human limb is moving at a different pace due to non-rigid motion [30].…”

“…In the case of hand motion, various Doppler components are generated from the fact that each part of the human limb is moving at a different pace due to non-rigid motion [30]. Accumulated in CSI, these components are expressed as a micro-Doppler effect in the Doppler power spectrum [29]- [31]. Instead of the single peak spectrum as in the point object assumption, the Doppler spectrum becomes spreading out and contains multiple peaks owing to the presence of multiple Doppler components.…”