A Novel Detection and Recognition Method for Continuous Hand Gesture Using FMCW Radar

Wang, Yong; Ren, Aihu; Zhou, Mu; Wang, Wen; Yang, Xiaobo

doi:10.1109/access.2020.3023187

Cited by 64 publications

(33 citation statements)

References 41 publications

(50 reference statements)

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“…Wang et al [ 10 ] proposed a method for continuous hand gesture detection and recognition based on the FMCW radar. RDM was acquired from raw data using a 2D FFT.…”

Section: Related Workmentioning

confidence: 99%

“…Various sensors, such as touch screen, camera, depth, voice, and radar sensors are used in HVI systems for in-vehicle device control [ 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 ]. Touch screens provide the largest amount of information to users at once and show the highest recognition accuracy.…”

Section: Introductionmentioning

confidence: 99%

“…Conventionally, radar sensor-based gesture recognition uses a frequency-modulated continuous-wave (FMCW) radar to obtain a range-Doppler map (RDM) by applying a 2-dimensional (2D) fast Fourier transform (FFT). Subsequently, the feature is extracted from the RDM, and the command is recognized using a machine-learning algorithm [ 9 , 10 , 11 ]. Command classification using the FMCW radar showed high accuracy.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Command Recognition Using Binarized Convolutional Neural Network with Voice and Radar Sensors for Human-Vehicle Interaction

Bae

Cho

et al. 2021

Sensors

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Recently, as technology has advanced, the use of in-vehicle infotainment systems has increased, providing many functions. However, if the driver’s attention is diverted to control these systems, it can cause a fatal accident, and thus human–vehicle interaction is becoming more important. Therefore, in this paper, we propose a human–vehicle interaction system to reduce driver distraction during driving. We used voice and continuous-wave radar sensors that require low complexity for application to vehicle environments as resource-constrained platforms. The proposed system applies sensor fusion techniques to improve the limit of single-sensor monitoring. In addition, we used a binarized convolutional neural network algorithm, which significantly reduces the computational workload of the convolutional neural network in command classification. As a result of performance evaluation in noisy and cluttered environments, the proposed system showed a recognition accuracy of 96.4%, an improvement of 7.6% compared to a single voice sensor-based system, and 9.0% compared to a single radar sensor-based system.

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“…Wang et al [ 10 ] proposed a method for continuous hand gesture detection and recognition based on the FMCW radar. RDM was acquired from raw data using a 2D FFT.…”

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Command Recognition Using Binarized Convolutional Neural Network with Voice and Radar Sensors for Human-Vehicle Interaction

Bae

Cho

et al. 2021

Sensors

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“…This distance calculation is based on the Manhattan distance or Euclidean distance between the observed fingerprint and the fingerprint recorded in the database. The second algorithm is K nearest neighbor method (KNN) [19,20]. The basic principle of the algorithm is that the fingerprint r i , i 1, 2, ..., L in the fingerprint library and the fingerprint r collected online are both the mean value of each FM broadcast signal sample.…”

Section: Online Phasementioning

confidence: 99%

A Frequency Modulation Fingerprint-Based Positioning Algorithm for Indoor Mobile Localization of Photoelectric Modules

et al. 2021

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Optoelectronic modules have a wide range of applications in the field of wireless communication. However, the function of mobile localization has not been realized in optoelectronic modules. In this paper, an indoor positioning algorithm, which was based on frequency modulation (FM) signals, was realized in optoelectronic modules. Firstly, FM monitoring receiver DB4004 was used to collect FM signals; Secondly, FM signals were preprocessed and analyzed to build a FM dataset. Finally, weighted centroid k-nearest neighbors (WC-KNN) precise positioning algorithm was proposed to obtain the position information of the photoelectric module. Experimental results showed that the median location error of the WC-KNN algorithm can reach 0.8 m and additional hardware equipment was not required. The research results provided the feasibility for the practical application of equipment based on optoelectronic devices in various fields.

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“…In [ 27 ], the authors used principal component analysis (PCA) and t-distributed stochastic neighbour embedding (t-SNE) algorithm, combined with machine learning techniques, to implement people’s way of walking based on radar spectrograms and range-Doppler maps. Wang et al [ 28 ] constructed a range-time map (RTM), Doppler-time map (DTM) and angle-time map (ATM) based on FMCW radar. The k-means algorithm was used to cluster the central time-frequency trajectory of each gesture spectrum, and the Fusion Dynamic Time Warping (FDTW) algorithm was used for gesture classification.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Hand Gesture Recognition in In-Vehicle Environment Based on FMCW Radar and Transformer

Zheng

Bai

Zhu

et al. 2021

Sensors

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Hand gesture recognition technology plays an important role in human-computer interaction and in-vehicle entertainment. Under in-vehicle conditions, it is a great challenge to design gesture recognition systems due to variable driving conditions, complex backgrounds, and diversified gestures. In this paper, we propose a gesture recognition system based on frequency-modulated continuous-wave (FMCW) radar and transformer for an in-vehicle environment. Firstly, the original range-Doppler maps (RDMs), range-azimuth maps (RAMs), and range-elevation maps (REMs) of the time sequence of each gesture are obtained by radar signal processing. Then we preprocess the obtained data frames by region of interest (ROI) extraction, vibration removal algorithm, background removal algorithm, and standardization. We propose a transformer-based radar gesture recognition network named RGTNet. It fully extracts and fuses the spatial-temporal information of radar feature maps to complete the classification of various gestures. The experimental results show that our method can better complete the eight gesture classification tasks in the in-vehicle environment. The recognition accuracy is 97.56%.

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A Novel Detection and Recognition Method for Continuous Hand Gesture Using FMCW Radar

Cited by 64 publications

References 41 publications

Command Recognition Using Binarized Convolutional Neural Network with Voice and Radar Sensors for Human-Vehicle Interaction

Command Recognition Using Binarized Convolutional Neural Network with Voice and Radar Sensors for Human-Vehicle Interaction

A Frequency Modulation Fingerprint-Based Positioning Algorithm for Indoor Mobile Localization of Photoelectric Modules

Dynamic Hand Gesture Recognition in In-Vehicle Environment Based on FMCW Radar and Transformer

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