Evidence map›Paper›PMID 38544062›Full record

ArticleSensors (Basel, Switzerland)2024

TRANS-CNN-Based Gesture Recognition for mmWave Radar.

Huafeng Zhang, Kang Liu, Yuanhui Zhang, Jihong Lin

Erratum issuedAbstract read
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In one paragraph

Article in Sensors (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

4 authors.

Huafeng ZhangCollege of Mechanical and Electrical Engineering, China Jiliang University, Hangzhou 310018, China.ORCID 0009-0004-1641-9073
Kang LiuCollege of Mechanical and Electrical Engineering, China Jiliang University, Hangzhou 310018, China.ORCID 0000-0002-9149-4661
Yuanhui ZhangCollege of Mechanical and Electrical Engineering, China Jiliang University, Hangzhou 310018, China.
Jihong LinCollege of Mechanical and Electrical Engineering, China Jiliang University, Hangzhou 310018, China.

Funding

mmWave Radar Joint Lab Project NO.2022-2024-03103-21179Natural Science Foundation of Zhejiang Province of China NO. 21F010057
6 · The paper itself

Abstract

In order to improve the real-time performance of gesture recognition by a micro-Doppler map of mmWave radar, the point cloud based gesture recognition for mmWave radar is proposed in this paper. Two steps are carried out for mmWave radar-based gesture recognition. The first step is to estimate the point cloud of the gestures by 3D-FFT and the peak grouping. The second step is to train the TRANS-CNN model by combining the multi-head self-attention and the 1D-convolutional network so as to extract the features in the point cloud data at a deeper level to categorize the gestures. In the experiments, TI mmWave radar sensor IWR1642 is used as a benchmark to evaluate the feasibility of the proposed approach. The results show that the accuracy of the gesture recognition reaches 98.5%. In order to prove the effectiveness of our approach, a simply 2Tx2Rx radar sensor is developed in our lab, and the accuracy of recognition reaches 97.1%. The results show that our proposed gesture recognition approach achieves the best performance in real time with limited training data in comparison with the existing methods.

Indexed as

dynamic gesture recognitionmmWave radarmulti-head self-attention mechanismpoint cloudTRANS-CNN

Identifiers

PMID38544062
PMCPMC10974769

What OpenQuestion holds

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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.