A Frame Detection Method for Real-Time Hand Gesture Recognition Systems Using CW-Radar.

CW radar convolutional neural network detection hand gesture micro-Doppler signatures real-time process

Journal

Sensors (Basel, Switzerland)
ISSN: 1424-8220
Titre abrégé: Sensors (Basel)
Pays: Switzerland
ID NLM: 101204366

Informations de publication

Date de publication:
18 Apr 2020
Historique:
received: 17 02 2020
revised: 03 04 2020
accepted: 16 04 2020
entrez: 25 4 2020
pubmed: 25 4 2020
medline: 25 4 2020
Statut: epublish

Résumé

In this paper, a method to detect frames was described that can be used as hand gesture data when configuring a real-time hand gesture recognition system using continuous wave (CW) radar. Detecting valid frames raises accuracy which recognizes gestures. Therefore, it is essential to detect valid frames in the real-time hand gesture recognition system using CW radar. The conventional research on hand gesture recognition systems has not been conducted on detecting valid frames. We took the R-wave on electrocardiogram (ECG) detection as the conventional method. The detection probability of the conventional method was 85.04%. It has a low accuracy to use the hand gesture recognition system. The proposal consists of 2-stages to improve accuracy. We measured the performance of the detection method of hand gestures provided by the detection probability and the recognition probability. By comparing the performance of each detection method, we proposed an optimal detection method. The proposal detects valid frames with an accuracy of 96.88%, 11.84% higher than the accuracy of the conventional method. Also, the recognition probability of the proposal method was 94.21%, which was 3.71% lower than the ideal method.

Identifiants

pubmed: 32325709
pii: s20082321
doi: 10.3390/s20082321
pmc: PMC7219670
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Subventions

Organisme : Ministry of Science and ICT
ID : IITP-2020-2018-0-01423
Organisme : National Research Foundation of Korea
ID : NRF-2017R1D1A1B03033422

Références

Conf Proc IEEE Eng Med Biol Soc. 2017 Jul;2017:4443-4446
pubmed: 29060883

Auteurs

Myoungseok Yu (M)

Dept. Information and Communication Engineering, Sejong University, Gunja-dong, Gwangjin-gu, Seoul 05006, Korea.

Narae Kim (N)

Dept. Information and Communication Engineering, Sejong University, Gunja-dong, Gwangjin-gu, Seoul 05006, Korea.

Yunho Jung (Y)

School of Electronics and Information Engineering, Korea Aerospace University, Goyang, Gyeonggi-do 10540, Korea.

Seongjoo Lee (S)

Dept. Information and Communication Engineering, Sejong University, Gunja-dong, Gwangjin-gu, Seoul 05006, Korea.

Classifications MeSH