Tiny hand gesture recognition without localization via a deep convolutional network

Bao, Peijun; Maqueda, Ana I.; del-Blanco, Carlos R.; García, Narciso

doi:10.1109/tce.2017.014971

Cited by 96 publications

(41 citation statements)

References 16 publications

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“…HGR can be categorized into vision-based gesture recognition (VGR) and sensor-based gesture recognition (SGR) [3]. VGR is a method of recognizing gestures using camera images, and various technologies have been proposed [4,5,6]. However, VGR accuracy degrades in light-sensitive application scenarios because camera images are affected by lighting conditions [7].…”

Section: Introductionmentioning

confidence: 99%

IMU Sensor-Based Hand Gesture Recognition for Human-Machine Interfaces

Kim

Cho

Lee

et al. 2019

Sensors

120

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We propose an efficient hand gesture recognition (HGR) algorithm, which can cope with time-dependent data from an inertial measurement unit (IMU) sensor and support real-time learning for various human-machine interface (HMI) applications. Although the data extracted from IMU sensors are time-dependent, most existing HGR algorithms do not consider this characteristic, which results in the degradation of recognition performance. Because the dynamic time warping (DTW) technique considers the time-dependent characteristic of IMU sensor data, the recognition performance of DTW-based algorithms is better than that of others. However, the DTW technique requires a very complex learning algorithm, which makes it difficult to support real-time learning. To solve this issue, the proposed HGR algorithm is based on a restricted column energy (RCE) neural network, which has a very simple learning scheme in which neurons are activated when necessary. By replacing the metric calculation of the RCE neural network with DTW distance, the proposed algorithm exhibits superior recognition performance for time-dependent sensor data while supporting real-time learning. Our verification results on a field-programmable gate array (FPGA)-based test platform show that the proposed HGR algorithm can achieve a recognition accuracy of 98.6% and supports real-time learning and recognition at an operating frequency of 150 MHz.

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Section: Introductionmentioning

confidence: 99%

IMU Sensor-Based Hand Gesture Recognition for Human-Machine Interfaces

Kim

Cho

Lee

et al. 2019

Sensors

120

View full text Add to dashboard Cite

show abstract

“…Oyedotun and Khashman [ 14 ] apply a convolutional neural network (CNN) and stacked denoising autoencoder (SDAE) to recognize 24 American Sign Language (ASL) hand gestures obtained from a public database, which achieves the recognition rates of 91.33 and 92.83%. Bao et al [ 15 ] propose a deep CNN that can classify hand gestures from the whole image without any segmentation or detection stage information. The method can organize seven sorts of hand gestures in a user-independent manner and achieve an accuracy of 97.1% in the dataset with simple backgrounds and 85.3% in the dataset with complex backgrounds.…”

Section: Introductionmentioning

confidence: 99%

A Fusion Recognition Method Based on Multifeature Hidden Markov Model for Dynamic Hand Gesture

Chen

Kaikai

2020

Computational Intelligence and Neuroscience

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In this paper, a fusion method based on multiple features and hidden Markov model (HMM) is proposed for recognizing dynamic hand gestures corresponding to an operator’s instructions in robot teleoperation. In the first place, a valid dynamic hand gesture from continuously obtained data according to the velocity of the moving hand needs to be separated. Secondly, a feature set is introduced for dynamic hand gesture expression, which includes four sorts of features: palm posture, bending angle, the opening angle of the fingers, and gesture trajectory. Finally, HMM classifiers based on these features are built, and a weighted calculation model fusing the probabilities of four sorts of features is presented. The proposed method is evaluated by recognizing dynamic hand gestures acquired by leap motion (LM), and it reaches recognition rates of about 90.63% for LM-Gesture3D dataset created by the paper and 93.3% for Letter-gesture dataset, respectively.

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“…Hand gesture-based interaction is one common approach being considered as HMI inputs [3]. Hand gestures are recognized by two major methods: Vision image processing [4] or wearable electronics [5]. Vision sensors are popularly used, especially in specific applications, such as smart televisions [6] or multimedia applications [7].…”

Section: Introductionmentioning

confidence: 99%

Gyroscope-Based Continuous Human Hand Gesture Recognition for Multi-Modal Wearable Input Device for Human Machine Interaction

Han

Yoon

2019

Sensors

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Human hand gestures are a widely accepted form of real-time input for devices providing a human-machine interface. However, hand gestures have limitations in terms of effectively conveying the complexity and diversity of human intentions. This study attempted to address these limitations by proposing a multi-modal input device, based on the observation that each application program requires different user intentions (and demanding functions) and the machine already acknowledges the running application. When the running application changes, the same gesture now offers a new function required in the new application, and thus, we can greatly reduce the number and complexity of required hand gestures. As a simple wearable sensor, we employ one miniature wireless three-axis gyroscope, the data of which are processed by correlation analysis with normalized covariance for continuous gesture recognition. Recognition accuracy is improved by considering both gesture patterns and signal strength and by incorporating a learning mode. In our system, six unit hand gestures successfully provide most functions offered by multiple input devices. The characteristics of our approach are automatically adjusted by acknowledging the application programs or learning user preferences. In three application programs, the approach shows good accuracy (90–96%), which is very promising in terms of designing a unified solution. Furthermore, the accuracy reaches 100% as the users become more familiar with the system.

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Tiny hand gesture recognition without localization via a deep convolutional network

Cited by 96 publications

References 16 publications

IMU Sensor-Based Hand Gesture Recognition for Human-Machine Interfaces

IMU Sensor-Based Hand Gesture Recognition for Human-Machine Interfaces

A Fusion Recognition Method Based on Multifeature Hidden Markov Model for Dynamic Hand Gesture

Gyroscope-Based Continuous Human Hand Gesture Recognition for Multi-Modal Wearable Input Device for Human Machine Interaction

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