WiGest: A ubiquitous WiFi-based gesture recognition system

By Heba Abdelnasser, Moustafa Youssef, Khaled A. Harras. IEEE Conference on Computer Communications, 2014

563 citations recorded by the source corpus.

We present WiGest: a system that leverages changes in WiFi signal strength to sense in-air hand gestures around the user's mobile device. Compared to related work, WiGest is unique in using standard WiFi equipment, with no modifications, and no training for gesture recognition. The system identifies different signal change primitives, from which we construct mutually independent gesture families. These families can be mapped to distinguishable application actions. We address various challenges including cleaning the noisy signals, gesture type and attributes detection, reducing false positives due to interfering humans, and adapting to changing signal polarity. We implement a proof-of-concept prototype using off-the-shelf laptops and extensively evaluate the system in both an office environment and a typical apartment with standard WiFi access points. Our results show that WiGest detects the basic primitives with an accuracy of 87.5% using a single AP only, including through-the-wall non-line-of-sight scenarios. This accuracy increases to 96% using three overheard APs. In addition, when evaluating the system using a multi-media player application, we achieve a classification accuracy of 96%. This accuracy is robust to the presence of other interfering humans, highlighting WiGest's ability to enable future ubiquitous hands-free gesture-based interaction with mobile devices.

Subfields: Electrical and Electronic Engineering, Human-Computer Interaction

View Paper: https://www.semanticscholar.org/paper/6738d0eca6d9de42068ab26a9314df98971f4d51

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Citations in the corpus

Zero-Effort Cross-Domain Gesture Recognition with Wi-Fi (2019, methodology): “Pioneer attempts such as E-eyes [45], CARM [44], WiGest [1] and WIMU [38] have been proposed.”
Capturing the human figure through a wall (2015, background): “…requiring them to carry a wireless transmitter, e.g., [Adib et al. 2014; Joshi et al. 2015; Adib and Katabi 2013; Wang et al. 2014b; Pu et al. 2013; Abdelnasser et al. 2015; Chetty et al. 2012; Wilson and Patwari 2011; Youssef et al. 2007; Nan-nuru et al. 2013; Seifeldin et al. 2013; Adib et al.…”
UbiBreathe: A Ubiquitous non-Invasive WiFi-based Breathing Estimator (2015, background): “…more detailed channel state information (CSI) in standard WiFi networks, have emerged as ubiquitous solutions for presence detection [1,4,10–12,18,20,34,36,40,44] , tracking individuals [19, 33, 35, 39] , recognition of different human activities [3,8,16,42], and gesture recognition systems [2,32].”
WiFi Sensing with Channel State Information (2019, background): “Wavelet Filter is similar to LPFs; the major difference is that it uses Discrete Wavelet Transform (DWT) instead of FFT. Details of signal transform methods and frequency-domain filters are introduced in Sections 3.2 and 3.3.”
Cross-Domain WiFi Sensing with Channel State Information: A Survey (2022, background): “On the other hand, for most recognition applications, such as activity recognition [88, 91], gesture recognition [1, 47], and human/object identification [8, 9], the number of classes that need to be recognized ranges from two to several hundred.”
Device-free gesture tracking using acoustic signals (2016, background): “Instead, they recognize predeﬁned gestures, such as punch, push, or sweep [27,29,30].”
Understanding and Modeling of WiFi Signal Based Human Activity Recognition (2015, background): “RSSI Based: RSSI based human activity recognition systems leverage the signal strength changes caused by human activities [3,22,23].”
Towards Environment Independent Device Free Human Activity Recognition (2018, background): “Some researchers [1, 36, 40, 49] propose to recognize human activities through analyzing the RSSI values.”
Emotion recognition using wireless signals (2016, background): “Past work leverages this phenomenon to sense human motion: it transmits an RF signal and analyzes its reflections to track user locations [5], gestures [6, 50, 56, 10, 61, 3], activities [59, 60], and vital signs [7, 19, 20].”
WiFi CSI Based Passive Human Activity Recognition Using Attention Based BLSTM (2019, background): “proposed a gesture recognition system termed WiGest with WiFi RSS measurements [11].”
Device-Free Human Activity Recognition Using Commercial WiFi Devices (2017, background): “Received Signal Strength Indicator (RSSI) based human activity recognition systems rely on the received signal strength changes caused by human activities [3], [22], [23].”
CoSDEO 2016 Keynote: A decade later — Challenges: Device-free passive localization for wireless environments (2016, background): “ing [14]–[16], whole-home activity and gesture recognition [17]–[20], robust detection [21]–[24], cyber-physical systems security [25], [26], traffic congestion and speed estimation [27], [28], reducing deployment overhead [29]–[31], understanding the underlying system [32]–[36], human counting [9],”
A review of hand gesture and sign language recognition techniques (2017, methodology): “Abdelnasser et al. [92] proposed a gesture recognition system using WiFi named WiGest.”
Widar3.0: Zero-Effort Cross-Domain Gesture Recognition With Wi-Fi (2021, methodology): “Pioneer attempts such as E-eyes [10], CARM [11], WiGest [12] and WIMU [13] have been proposed.”
Wireless Sensing for Human Activity: A Survey (2020, background): “[33] devise WiGest that further improves the recognition accuracy of 8 hand gestures to 96%.”