ArticleSensors (Basel, Switzerland)2021
Highly-Optimized Radar-Based Gesture Recognition System with Depthwise Expansion Module.
Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
6 citing papers in PubMed, 27 citations in OpenAlex.
- Neuromorphic-inspired multi-view global-local fusion for IR-UWB radar dynamic gesture recognition.Frontiers in neuroscience · 2026Article
- A Joint Gesture-Identity Recognition Framework Based on 4D Millimeter-Wave Radar Sensing.Sensors (Basel, Switzerland) · 2025Article
- Radar-Based Gesture Recognition Using Adaptive Top-K Selection and Multi-Stream CNNs.Sensors (Basel, Switzerland) · 2025Article
- Real-Time Hand Gesture Recognition in Clinical Settings: A Low-Power FMCW Radar Integrated Sensor System with Multiple Feature Fusion.Sensors (Basel, Switzerland) · 2025Article
- Smart furniture using radar technology for cardiac health monitoring.Scientific reports · 2025Article
- Dynamic Gesture Recognition Based on FMCW Millimeter Wave Radar: Review of Methodologies and Results.Sensors (Basel, Switzerland) · 2023Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 2 institutions in 3 countries.
Funding
Abstract
The increasing integration of technology in our daily lives demands the development of more convenient human-computer interaction (HCI) methods. Most of the current hand-based HCI strategies exhibit various limitations, e.g., sensibility to variable lighting conditions and limitations on the operating environment. Further, the deployment of such systems is often not performed in resource-constrained contexts. Inspired by the MobileNetV1 deep learning network, this paper presents a novel hand gesture recognition system based on frequency-modulated continuous wave (FMCW) radar, exhibiting a higher recognition accuracy in comparison to the state-of-the-art systems. First of all, the paper introduces a method to simplify radar preprocessing while preserving the main information of the performed gestures. Then, a deep neural classifier with the novel Depthwise Expansion Module based on the depthwise separable convolutions is presented. The introduced classifier is optimized and deployed on the Coral Edge TPU board. The system defines and adopts eight different hand gestures performed by five users, offering a classification accuracy of 98.13% while operating in a low-power and resource-constrained environment.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.