Abstract
Channel state information (CSI)-based human activity recognition plays an essential role in various application domains, such as security, healthcare, and Internet of Things. Most existing CSI-based activity recognition approaches rely on manually designed features that are classified using traditional classification methods. Furthermore, the use of deep learning methods for CSI-based activity recognition is still at its infancy with most of the existing approaches focus on recognizing single-human activities. The current study explores the feasibility of utilizing deep learning methods to recognize human-to-human interactions (HHIs) using CSI signals. Particularly, we introduce an end-to-end deep learning framework that comprises three phases, which are the input, feature extraction, and recognition phases. The input phase converts the raw CSI signals into CSI images that comprise time, frequency, and spatial information. In the feature extraction phase, a novel convolutional neural network (CNN) is designed to automatically extract deep features from the CSI images. Finally, the extracted features are fed to the recognition phase to identify the class of the HHI associated with each CSI image. The performance of our proposed framework is assessed using a publicly available CSI dataset that was acquired from 40 different pairs of subjects while performing 13 HHIs. Our proposed framework achieved an average recognition accuracy of 86.3% across all HHIs. Moreover, the experiments indicate that our proposed framework enabled significant improvements over the results achieved using three state-of-the-art pre-trained CNNs as well as the results obtained using four different conventional classifiers that employs traditional handcrafted features.
Highlights
Human activity recognition has many applications in several domains [1], such as human-computer interaction, security and surveillance, and healthcare
We compare the results achieved by our proposed E2EDLF with the results achieved by traditional handcrafted features that are extracted from the channel state information (CSI) signals and classified using four different conventional classifiers, including a multi-class support vector machine classifier, k-NN classifier, naive Bayes classifier, and decision tree classifier
EXPERIMENTAL RESULTS AND DISCUSSION we present the publicly available CSI dataset of human-to-human interactions (HHIs) that was previously published by our research group [28] and used in this work to assess the performance of our proposed E2EDLF
Summary
Human activity recognition has many applications in several domains [1], such as human-computer interaction, security and surveillance, and healthcare. Traditional human activity recognition approaches employ different sensing technologies, such as cameras [2], wearable sensors [3], and radars [4]. Radar-based approaches utilize special devices to acquire the signals, such as universal software radio peripheral [10], [11], that have a limited range of coverage. Commercial off-the-shelf Wi-Fi devices that run according to the IEEE 802.11n standard utilize the multiple-input multiple-output (MIMO) technology with the orthogonal frequency-division multiplexing (OFDM) scheme to send and receive different Wi-Fi signals over multiple transmitreceive antenna pairs [18]. The propagation of wireless signals between a transmit-receive antenna pair is characterized by the CSI metric [16], which represents the channel frequency response (CFR) measured for a transmit-receive antenna pair and a particular OFDM subcarrier frequency [5]. Where s ∈ [1, · · · , NS] represents the index of the OFDM subcarrier frequency, NS is the number of the OFDM subcarrier frequencies, i represents the index of the transmitted and received packets, As(i) and Bs(i) are the ith transmitted and received packets associated with the OFDM subcarrier frequency s, respectively, NT and NR represent the number of transmitting and receiving antennas, respectively, N represents noise, and Hs(i) is a complex-valued matrix of dimensions NT × NR that comprises the CSI measurements of the MIMO channel for the OFDM subcarrier frequency s.
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