Abstract
ABSTRACTHuman activity tracking plays a vital role in human–computer interaction. Traditional human activity recognition (HAR) methods adopt special devices, such as cameras and sensors, to track both macro- and micro-activities. Recently, wireless signals have been exploited to track human motion and activities in indoor environments without additional equipment. This study proposes a device-free WiFi-based micro-activity recognition method that leverages the channel state information (CSI) of wireless signals. Different from existed CSI-based micro-activity recognition methods, the proposed method extracts both amplitude and phase information from CSI, thereby providing more information and increasing detection accuracy. The proposed method harnesses an effective signal processing technique to reveal the unique patterns of each activity. We applied a machine learning algorithm to recognize the proposed micro-activities. The proposed method has been evaluated in both line of sight (LOS) and none line of sight (NLOS) scenarios, and the empirical results demonstrate the effectiveness of the proposed method with several users.
Highlights
Human motion and activity analysis has received increasing attention in recent decades because of advances in computing and sensing technologies as well as interest in action and gesture recognition applications such as security and surveillance, human–computer interaction, and gaming (Campbell et al 2008)
The proposed method has been evaluated in both line of sight (LOS) and none line of sight (NLOS) scenarios, and the empirical results demonstrate the effectiveness of the proposed method with several users
channel state information (CSI) is collected at detection point (DP), which is a laptop installed with Ubuntu and the open source CSI-Tool (Halperin et al 2011)
Summary
Human motion and activity analysis has received increasing attention in recent decades because of advances in computing and sensing technologies as well as interest in action and gesture recognition applications such as security and surveillance, human–computer interaction, and gaming (Campbell et al 2008). Traditional human activity recognition (HAR) approaches have proposed various novel methods which are applied in different sensing areas, such as security, entertainment, and healthcare (Lara and Labrador 2013; Poppe 2010). The primary drawbacks of sensor-based sensing mechanisms are the burdens imposed by installation in test areas or the human body as well as inconvenient usage, for the patients or elderly
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
