Hand Gesture Recognition Using a Radar Echo I–Q Plot and a Convolutional Neural Network

Sakamoto, Takuya; Gao, Xiaomeng; Yavari, Ehsan; Rahman, Ashikur; Borić–Lubecke, Olga; Lubecke, Victor M.

doi:10.1109/lsens.2018.2866371

Cited by 42 publications

(26 citation statements)

References 18 publications

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“…In addition to classifying human motions, radars have been recently used for gesture recognition which is an important problem in a variety of applications that involve smart homes and human-machine interface for intelligent devices [19][20][21][22][23][24][25][26][27]. The latter is considered vital in aiding the physically impaired who might be wheelchair confined or bed-ridden patients.…”

Section: Introductionmentioning

confidence: 99%

Hand Gesture Recognition based on Radar Micro-Doppler Signature Envelopes

Amin

Zeng

Shan

2019

2019 IEEE Radar Conference (RadarConf)

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We introduce a simple but effective technique in automatic hand gesture recognition using radar. The proposed technique classifies hand gestures based on the envelopes of their micro-Doppler (MD) signatures. These envelopes capture the distinctions among different hand movements and their corresponding positive and negative Doppler frequencies that are generated during each gesture act. We detect the positive and negative frequency envelopes of MD separately, and form a feature vector of their augmentation. We use the k-nearest neighbor (kNN) classifier and Manhattan distance (L1) measure, in lieu of Euclidean distance (L2), so as not to diminish small but critical envelope values. It is shown that this method outperforms both low-dimension representation techniques based on principal component analysis (PCA) and sparse reconstruction using Gaussian-windowed Fourier dictionary, and can achieve very high classification rates.

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Section: Introductionmentioning

confidence: 99%

Hand Gesture Recognition based on Radar Micro-Doppler Signature Envelopes

Amin

Zeng

Shan

2019

2019 IEEE Radar Conference (RadarConf)

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“…The presented framework was based on three different sensors, namely, (1) color camera, (2) depth camera and (3) FMCW radar. Prominent and Sakamoto and co-workers published two studies [64,65] using I-Q plot and CNN architectures. In the first study, only three gestures were used, whereas the second study demonstrated the performance against six gestures with an accuracy above 90%.…”

Section: Hgr Algorithms For Fmcwmentioning

confidence: 99%

Hand Gestures Recognition Using Radar Sensors for Human-Computer-Interaction: A Review

et al. 2021

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Human–Computer Interfaces (HCI) deals with the study of interface between humans and computers. The use of radar and other RF sensors to develop HCI based on Hand Gesture Recognition (HGR) has gained increasing attention over the past decade. Today, devices have built-in radars for recognizing and categorizing hand movements. In this article, we present the first ever review related to HGR using radar sensors. We review the available techniques for multi-domain hand gestures data representation for different signal processing and deep-learning-based HGR algorithms. We classify the radars used for HGR as pulsed and continuous-wave radars, and both the hardware and the algorithmic details of each category is presented in detail. Quantitative and qualitative analysis of ongoing trends related to radar-based HCI, and available radar hardware and algorithms is also presented. At the end, developed devices and applications based on gesture-recognition through radar are discussed. Limitations, future aspects and research directions related to this field are also discussed.

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“…In [63], hand gesture recognition using a convolutional neural network was applied to radar echo I/Q plot trajectories. The radar echo trajectories were converted to low-resolution images for training and evaluation.…”

Section: B Behavior/gesture Recognition For Smart Living and Future mentioning

confidence: 99%