RadarSNN: A Resource Efficient Gesture Sensing System Based on mm-Wave Radar

Arsalan, Muhammad; Santra, Avik; Issakov, Vadim

doi:10.1109/tmtt.2022.3148403

Cited by 24 publications

(5 citation statements)

References 56 publications

(26 reference statements)

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“…Conventional methods generally include patterns extraction from the collected signals and comparison with the baseline or fingerprint derived from dataset. Several NN-based algorithms have also been proposed to process the high-dimensional EM information ( 51 ) and make predictions from scattering field, radar echo signal, or channel state information (CSI), such as RadarSNN ( 52 ). However, all those networks processed the input dataflow in the digital domain, which means a limited processing speed and complex front-end analog-to-digital modules.…”

Section: Resultsmentioning

confidence: 99%

Direct electromagnetic information processing with planar diffractive neural network

Gu,

Ma,

Gao

et al. 2024

Sci. Adv.

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Diffractive neural network in electromagnetic wave–driven system has attracted great attention due to its ultrahigh parallel computing capability and energy efficiency. However, recent neural networks based on the diffractive framework still face the bottlenecks of misalignment and relatively large size limiting their further applications. Here, we propose a planar diffractive neural network (pla-NN) with a highly integrated and conformal architecture to achieve direct signal processing in the microwave frequency. On the basis of printed circuit fabrication process, the misalignment could be effectively circumvented while enabling flexible extension for multiple conformal and stacking designs. We first conduct validation on the fashion-MNIST dataset and experimentally build up a system using the proposed network architecture for direct recognition of different geometry structures in the electromagnetic space. We envision that the presented architecture, once combined with the advanced dynamic maneuvering techniques and flexible topology, would exhibit unlimited potentials in the areas of high-performance computing, wireless sensing, and flexible wearable electronics.

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Section: Resultsmentioning

confidence: 99%

Direct electromagnetic information processing with planar diffractive neural network

Gu,

Ma,

Gao

et al. 2024

Sci. Adv.

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“…Further, the evaluations are made on a static radar, whereas the radar mounted on mobile devices is likely to move within the duration of the gesture, and evaluations catering to the mobile nature of the device are required. Finally, evaluating the computational complexity of the proposed solution, and the development of low complexity alternatives (e.g., similar in spirit to [17], [20], [23]) is another direction for future work.…”

Section: Discussionmentioning

confidence: 99%

“…In this work, we provide a data-segmentation solution for online segmentation. Finally, the prior work that considers some micro-gestures and data-segmentation, e.g., [20]- [23], does not extend their evaluations to unseen users. In this work, we evaluate our data-segmentation and micro-gesture recognition solution for unseen users.…”

Section: B Prior Workmentioning

confidence: 99%

End-to-End Dynamic Gesture Recognition Using MmWave Radar

Parida

et al. 2022

IEEE Access

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Millimeter-wave (mmWave) radar sensors are a promising modality for gesture recognition as they can overcome several limitations of optic sensors typically used for gesture recognition. These limitations include cost, battery consumption, and privacy concerns. This work focuses on finger level (called micro) gesture recognition using mmWave radar. We propose a set of 6 micro-gestures that are not only intuitive and easy to perform for the user but are distinguishable based on Doppler and angle variation in time. For gesture recognition, we propose an end-to-end solution including an activity detection module (ADM) that automatically segments the data and the gesture classifier (GC) that takes the segmented data and predicts the gesture. Both the ADM and GC are based on machine learning (ML) tools. We evaluate the proposed solution using data collected from 11 users and our proposed solution achieves an end-to-end accuracy of 95%. INDEX TERMS Human-computer interface, activity detection, gesture recognition, radar, machine learningConsidering radar characteristics, we select dynamic gestures rather than static hand/finger poses in our solution. Radars have limited resolution in both the angle and range. Due to form-factor constraints, radar modules on mobile devices have only a few antennas, and as a result have limited angle resolution. While large bandwidths are available at mmWave bands, the range resolution is still several centimeters, which is too coarse for differentiating fingers' positions. Fortunately, radars have superb Doppler (speed) measurement capability. The Doppler resolution is inversely proportional to the duration of the radar coherent processing duration, which is a design parameter. The high Doppler resolution enables the radar to capture and potentially distinguish

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“…Due to the high complexity of the conventional deep neural network, a lot of computational is needed in gesture recognition, and the majority of the energy is consumed by the multiply-accumulate (MAC) operations between layers. Therefore, researchers have proposed to reduce the computational complexity by using lightweight networks [86,87] or by optimizing the classification model through methods such as pruning techniques [58,67]. In general, reducing the complexity of the algorithm while ensuring accuracy and meeting the real-time requirements of the application is a great challenge for gesture recognition and will also be the focus of future studies.…”

Section: Real Time and Complexity Of Gesturesmentioning

confidence: 99%

Dynamic Gesture Recognition Based on FMCW Millimeter Wave Radar: Review of Methodologies and Results

Tang,

Wu,

2023

Sensors

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As a convenient and natural way of human-computer interaction, gesture recognition technology has broad research and application prospects in many fields, such as intelligent perception and virtual reality. This paper summarized the relevant literature on gesture recognition using Frequency Modulated Continuous Wave (FMCW) millimeter-wave radar from January 2015 to June 2023. In the manuscript, the widely used methods involved in data acquisition, data processing, and classification in gesture recognition were systematically investigated. This paper counts the information related to FMCW millimeter wave radar, gestures, data sets, and the methods and results in feature extraction and classification. Based on the statistical data, we provided analysis and recommendations for other researchers. Key issues in the studies of current gesture recognition, including feature fusion, classification algorithms, and generalization, were summarized and discussed. Finally, this paper discussed the incapability of the current gesture recognition technologies in complex practical scenes and their real-time performance for future development.

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RadarSNN: A Resource Efficient Gesture Sensing System Based on mm-Wave Radar

Cited by 24 publications

References 56 publications

Direct electromagnetic information processing with planar diffractive neural network

Direct electromagnetic information processing with planar diffractive neural network

End-to-End Dynamic Gesture Recognition Using MmWave Radar

Dynamic Gesture Recognition Based on FMCW Millimeter Wave Radar: Review of Methodologies and Results

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