About: Real-Time Radar-Based Gesture Detection and Recognition Built in an Edge-Computing Platform

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About: Real-Time Radar-Based Gesture Detection and Recognition Built in an Edge-Computing Platform Goto Sponge NotDistinct Permalink

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Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	Real-Time Radar-Based Gesture Detection and Recognition Built in an Edge-Computing Platform
Creator	Fei, Tai Li, Xibo Sun, Yuliang Warnecke, Alexander Warsitz, Ernst
Source	ArXiv
abstract	In this paper, a real-time signal processing frame-work based on a 60 GHz frequency-modulated continuous wave (FMCW) radar system to recognize gestures is proposed. In order to improve the robustness of the radar-based gesture recognition system, the proposed framework extracts a comprehensive hand profile, including range, Doppler, azimuth and elevation, over multiple measurement-cycles and encodes them into a feature cube. Rather than feeding the range-Doppler spectrum sequence into a deep convolutional neural network (CNN) connected with recurrent neural networks, the proposed framework takes the aforementioned feature cube as input of a shallow CNN for gesture recognition to reduce the computational complexity. In addition, we develop a hand activity detection (HAD) algorithm to automatize the detection of gestures in real-time case. The proposed HAD can capture the time-stamp at which a gesture finishes and feeds the hand profile of all the relevant measurement-cycles before this time-stamp into the CNN with low latency. Since the proposed framework is able to detect and classify gestures at limited computational cost, it could be deployed in an edge-computing platform for real-time applications, whose performance is notedly inferior to a state-of-the-art personal computer. The experimental results show that the proposed framework has the capability of classifying 12 gestures in real-time with a high F1-score.
has issue date	2020-05-20(xsd:dateTime)
bibo:doi	10.1109/jsen.2020.2994292
has license	arxiv
sha1sum (hex)	395b7d32bab7febb793fa3fd8b52e00194f80866
schema:url	https://doi.org/10.1109/jsen.2020.2994292
resource representing a document's title	Real-Time Radar-Based Gesture Detection and Recognition Built in an Edge-Computing Platform
resource representing a document's body	covid:395b7d32bab7febb793fa3fd8b52e00194f80866#body_text
is schema:about of	named entity 'time-stamp' named entity 'classify' named entity 'IEEE SENSORS JOURNAL' named entity 'Gesture' named entity 'GESTURES' named entity 'INPUT' named entity 'TO REDUCE' named entity 'FEATURE' named entity 'HIGH' named entity 'PROFILE' named entity 'PERFORMANCE' named entity 'MEASUREMENT' named entity 'ADDITION' named entity 'ACTIVITY DETECTION' named entity 'MODULATED' named entity 'AZIMUTH' named entity 'COULD BE' named entity 'ELEVATION' named entity 'RECURRENT' named entity 'CNN' named entity 'shallow' named entity 'gesture recognition' named entity 'CNN' named entity 'classifying' named entity 'deep convolutional neural network' named entity 'extracts' named entity 'computational complexity' named entity 'experimental' named entity 'real-time' named entity 'gesture' named entity 'profile' named entity 'gesture recognition' named entity 'time-stamp' named entity 'CNN' named entity 'GHz' named entity 'CNN' named entity 'CNN' named entity 'radar' named entity 'low latency' named entity 'radar-based' named entity 'experimental results' named entity 'signal processing' named entity 'modulated continuous wave' named entity 'computational complexity' named entity 'azimuth' named entity 'Gesture Detection' named entity 'Radar-Based' named entity 'score' named entity 'GHz' named entity 'CNN' named entity 'radar-based' named entity 'radar-based' named entity 'electromagnetic waves' named entity 'azimuth' named entity 'radar' named entity 'softmax function' named entity 'algorithm' named entity 'CNN' named entity 'data processing' named entity 'voice activity detection' named entity 'CNN' named entity 'gesture recognition' named entity 'neural networks' named entity 'silicon' named entity 'gesture recognition' named entity 'long short-term memory' named entity 'LSTM' named entity 'complex amplitude' named entity 'CNN' named entity 'Quad-core' named entity 'precision and recall' named entity 'waveform' named entity 'CNN' named entity 'RWM' named entity 'edge-computing' named entity 'phase difference' named entity 'Doppler radar' named entity 'computational complexity' named entity 'beat signal' named entity 'radar' named entity 'bandwidth'

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