Teach Your WiFi-Device: Recognise Simultaneous Activities and Gestures from Time-Domain RF-Features

Teach Your WiFi-Device: Recognise Simultaneous Activities and Gestures from Time-Domain RF-Features

Stephan Sigg (Goettingen University, Goettingen, Germany), Shuyu Shi (Department of Informatics, Graduate University for Advanced Studies, Japan) and Yusheng Ji (Department of Informatics, Graduate University for Advanced Studies, Japan)
Copyright: © 2014 |Pages: 15
DOI: 10.4018/ijaci.2014010102

Abstract

The authors consider two untackled problems in RF-based activity recognition: the distinction of simultaneously conducted activities of individuals and the recognition of gestures from purely time-domain-based features. Recognition is based on a single antenna system. This is important for the application in end-user devices which are usually single-antenna systems and have seldom access to more sophisticated, e.g. frequency-based features. In case studies with software defined radio nodes utilised in an active, device-free activity recognition (DFAR) system, the authors observe a good recognition accuracy for the detection of multiple simultaneously conducted activities with two and more receive devices. Four gestures and two baseline situations are distinguished with good accuracy in a second case study.
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Introduction

Recognition of human activities is frequently achieved by inertial and body-worn sensors (Yang, Xue, Fang, & Tang, 2012; Lukowicz, Pentland, & Ferscha, 2012). In some application cases, however, these sensing systems are not applicable. This touches, in particular, cases with non-cooperating persons or crowd that can not easily be equipped with sensing devices. While primarily video or audio have been heavily investigated as environmental sensing sources (Aggarwal & Ryoo, 2011; Chaquet, Carmona, & Fern´aNdez-Caballero, 2013; Kunze & Lukowicz, 2007; Schuermann & Sigg, 2013), also less intrusive RF-based systems can be employed for the localisation of entities or the recognition of activities from non-equipped individuals. In these systems, the fluctuation in the signal strength, phase or frequency of a received RF-signal is exploited. Individuals in the proximity of a receive antenna blocking or reflecting signal propagation paths might incur characteristic patterns in the signal strength propagation (Scholz, Sigg, Schmidtke, & Beigl, 2011). These systems are called device-free since the monitored entity is not equipped with any receive or transmit device (Sigg, Scholz, Shi, Ji, & Beigl, 2013; M. Youssef, Mah, & Agrawala, 2007). While the localisation of individuals has received much attention (see (Zhang, Liu, Guo, Gao, & Ni, 2012) and references therein), current work is shifting to other domains such as the counting of multiple subjects (Xu et al., 2013), the sensing of traffic situations (Ding, Banitalebi, Miyaki, & Beigl, 2011; Kassem, Kosba, & Youssef, 2012), or the distinction of activities (Sigg, Scholz, et al., 2013; Shi, Sigg, & Ji, 2012b; Patwari & Wilson, 2011). In previous studies, in particular in active installations, which incorporate a transmitter as part of the system, typically a single receiver and a single individual conducting activities at a time are investigated. We focus in this study on three relevant but yet untackled research issues in device-free recognition systems:

  • 1 The simultaneous recognition of activities from multiple persons by single-antenna receive devices

  • 2 The impact of multiple single-antenna receive devices on the recognition accuracy

  • 3 The recognition of gestures from single antenna devices on time-domain features

All features are extracted from the time domain of a received signal since contemporary end-user devices are not applicable of deriving more sophisticated, such as frequency domain features. We employ USRP (http://www.ettus.com) software defined radio (SDR) devices placed in an environment for an active device-free activity recognition system. With such RF-sensing capabilities, a seamless integration of pro-active systems in smart environments becomes possible. For instance (See Figure 1), consider touch-free interaction or the recognition of activities from possibly multiple persons in a smart environment. This article is an extended version of our work published in (Sigg, Shi, & Ji, 2013). We add a comprehensive discussion of related work, further details on the simultaneous recognition of multiple activities (in particular adding precision and recall for all results) and a complete novel study on the recognition of gestures from time-domain features.

Figure 1.

Utilisation of RF-fluctuation

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