Towards a Diffraction-based Sensing Approach on Human Activity Recognition

Abstract

In recent years, wireless sensing has been exploited as a promising research direction for contactless human activity recognition. However, one major issue hindering the real deployment of these systems is that the signal variation patterns induced by the human activities with different devices and environmental settings are neither stable nor consistent, resulting in unstable system performance. The existing machine learning based methods usually take the "black box" approach and fails to achieve consistent performance. In this paper, we argue that a deep understanding of radio signal propagation in wireless sensing is needed, and it may be possible to develop a deterministic sensing model to make the signal variation patterns predictable. With this intuition, in this paper we investigate: 1) how wireless signals are affected by human activities taking transceiver location and environment settings into consideration; 2) a new deterministic sensing approach to model the received signal variation patterns for different human activities; 3) a proof-of-concept prototype to demonstrate our approach and a case study to detect diverse activities. In particular, we propose a diffraction-based sensing model to quantitatively determine the signal change with respect to a target's motions, which eventually links signal variation patterns with motions, and hence can be used to recognize human activities. Through our case study, we demonstrate that the diffraction-based sensing model is effective and robust in recognizing exercises and daily activities. In addition, we demonstrate that the proposed model improves the recognition accuracy of existing machine learning systems by above 10%.

Human Activity Sensing with Wireless Signals: A Survey

“…Table 4 provides a summary of the models between human activity characteristics and critical parameters of wireless signal propagation. [36,69], Coarse-grained estimation [14,18,19,[23][24][25][26]37,40,42,43,46,47,49,50,[52][53][54] Distance (dLoS) Fresnel zone model [106][107][108][109][110] Direction Fresnel zone model [106], AoA with antenna array [11,15,65,[111][112][113][114][115] aw Signal Distance, Direction, Velocity mD-Track [116] 3.1. Phase…”

“…The phase difference can derive the rough speed of the human movement. Fresnel zone model [106][107][108][109][110] Direction Fresnel zone model [106], AoA with antenna array [11,15,65,[111][112][113][114][115] aw Signal Distance, Direction, Velocity mD-Track [116] 3. 1…”

Modelling, Analysis, and Simulation of the Micro-Doppler Effect in Wideband Indoor Channels with Confirmation Through Pendulum Experiments

“…Then, the STFTX H (q) ( f , t) was computed for each subcarrier q. After that the spectrogram S( f , t) is computed according to (42). Figure 5a-c exhibit the spectrograms ofS( f , t), S( f , t), andŜ( f , t) of the channel model with IMU data as inputs, the channel model fed with the mechanical model as inputs, and the recorded CSI data, respectively.…”

“…The Fresnel zone diffraction model has been described in [38][39][40][41]. Such a model has been used for CSI-based human activity recognition [42], human respiration sensing [43], and indoor human detection [44]. The Fresnel zone model is an envelope model that does not contain any phase information.…”

WiFreeze: Multiresolution Scalograms for Freezing of Gait Detection in Parkinson’s Leveraging 5G Spectrum with Deep Learning

“…WiFi sensing-based monitoring systems only require a WiFi router or access point and one or more WiFi enabled devices. WiFi sensing with CSI measurements have been used in various applications, such as human presence/localization [20][21][22], activity recognition [23][24][25], fall classification and detection [26][27][28][29], gesture recognition [30][31][32], and user identification [33][34][35].Recent work has leveraged WiFi sensing for human presence detection and localization. Qian et al[20] used a WiFi-based Multiple Inputs and Multiple Outputs (MIMO) system and CSI measurements to detect presence of humans with dynamic movement speeds utilizing a Support Vector Machine (SVM), resulting in a true positive rate greater than 93%.…”