This paper introduces Wisture, a new online machine learning solution for recognizing touch-less dynamic hand gestures on a smartphone. Wisture relies on the standard Wi-Fi Received Signal Strength (RSS) using a Long Short-Term Memory (LSTM) Recurrent Neural Network (RNN), thresholding filters and traffic induction. Unlike other Wi-Fi based gesture recognition methods, the proposed method does not require a modification of the smartphone hardware or the operating system, and performs the gesture recognition without interfering with the normal operation of other smartphone applications. We discuss the characteristics of Wisture, and conduct extensive experiments to compare its performance against state-of-the-art machine learning solutions in terms of both accuracy and time efficiency. The experiments include a set of different scenarios in terms of both spatial setup and traffic between the smartphone and Wi-Fi access points (AP). The results show that Wisture achieves an online recognition accuracy of up to 94 (average 78 ) in detecting and classifying three hand gestures.
@cite_16: Various approaches exist to detect gestures and movements via smartphones. Most of them, however, require that the smartphone is carried on-body. The abscence of reliable ad-hoc on-line gesture detection from environmental sources inspired this project for on-line hand gesture detection on a smartphone using only WiFi RSSI. We highlight our line of work and explain problems at hand to provide information for possible future work. We will furthermore introduce wifiJedi, a smartphone application, that is able to detect movement in front of the smartphone by reading the WiFi RSSI and use this information to control a Slideshow.
@cite_2: We investigate the use of WiFi Received Signal Strength Information (RSSI) at a mobile phone for the recognition of situations, activities and gestures. In particular, we propose a device-free and passive activity recognition system that does not require any device carried by the user and uses ambient signals. We discuss challenges and lessons learned for the design of such a system on a mobile phone and propose appropriate features to extract activity characteristics from RSSI. We demonstrate the feasibility of recognising activities, gestures and environmental situations from RSSI obtained by a mobile phone. The case studies were conducted over a period of about two months in which about 12 hours of continuous RSSI data was sampled, in two countries and with 11 participants in total. Results demonstrate the potential to utilise RSSI for the extension of the environmental perception of a mobile device as well as for the interaction with touch-free gestures. The system achieves an accuracy of 0.51 while distinguishing as many as 11 gestures and can reach 0.72 on average for four more disparate ones.
K-Nearest Neighbors (K-NN) based classifiers have been widely used to recognize gestures from the RSS data. In @cite_2 and @cite_16 , the authors used window-based statistical features (e.g. mean, variance, maximum, number of peaks, etc.) applied to a K-NN classifier for recognizing hand gestures on a smartphone. They achieved accuracies of @math 90 We depart from the literature works in four different ways: (1) we do not make any modifications to the existing hardware or software applications of the phone; (2) we introduce a new traffic induction approach to enable high-frequency RSS measurements; (3) we use custom but simple signal processing techniques and an efficient LSTM-RNN machine learning method to classify over-the-air hand gestures; (4) we share the experiment datasets and the partial source codes of our solution for the community to build this research further.