Abstract
We construct a public dataset for WiFi-based Activity Recognition named WiAR with sixteen activities operated by ten volunteers in three indoor environments. It aims to provide public signal data for researchers to reduce the cost of collected signal data and conveniently evaluate the performance of WiFi-based human activity recognition in different domains. First, we introduce the basic knowledge of WiFi signals regarding RSSI, CSI, and wireless hardware. Second, we explain the characteristics of WiAR dataset in terms of activities types, data format, data acquisition ways, and influence factors. Third, the proposed framework can estimate the quality of the shared signal data provided by other peers. Finally, we select and use five classification algorithms and two deep learning algorithms to evaluate the performance of WiAR dataset on human activity recognition. The results show that the accuracy of WiAR dataset is higher than 80% using machine learning algorithms and 90% using deep learning algorithms in different indoor environments.
Highlights
Human Activity Recognition (HAR) is increasing popular in practical applications including smart homes [1]–[4], user authentication service [5]–[7], healthcare monitoring [8]–[10], and smart space management [11]–[13]
WiAR dataset provides Received Signal Strength Indicator (RSSI) and CSI information and raw WiFi signals reflected by human activity and several features extracted from raw data
Compared to machine learning algorithms, the accuracy of activity recognition using deep learning algorithms is higher than 90% averagely
Summary
Human Activity Recognition (HAR) is increasing popular in practical applications including smart homes [1]–[4], user authentication service [5]–[7], healthcare monitoring [8]–[10], and smart space management [11]–[13]. WiAR dataset provides RSSI and CSI information and raw WiFi signals reflected by human activity and several features extracted from raw data. We consider the activity data collected in the empty room as a baseline dataset to explore the characteristics of WiFi signals reflected by human activity. In WiAR dataset, we use 20MHz bandwidth with 30 subcarriers in 5GHz which can provide stable signals compared to 2.4GHz. Collected activity data for each sample contain many packets with three antennas corresponding to 90 subcarriers as shown in the following equation (3). The signal pattern corresponding to an activity holds a tight trend, and signals
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