A novel hybrid bidirectional unidirectional LSTM network for dynamic hand gesture recognition with Leap Motion

Ameur, Safa; Khalifa, Anouar Ben; Bouhlel, Med Salim

doi:10.1016/j.entcom.2020.100373

Cited by 78 publications

(24 citation statements)

References 38 publications

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“…As shown in the Table Ⅸ, WTS has higher segmentation rate and more stable segmentation effect. We compare the recognition rate of BPTS with that of other algorithms, such as Average Threshold Crossing (ATC) [77], CNN [78], Deep Forest algorithm (DF) [79], Deep Convolutional Network (DCN) [80], Dynamic Time Warping (DTW) [81], PCA [82] and SVM [83], WPT and unscented Kalman neural network (UKFNN) [84], Hybrid Bidirectional Unidirectional Long Short-Term Memory (HBU-LSTM) [85], and PCA and SVM [13]. Some parameters or structures of other algorithms are configured as shown in Table Ⅹ.…”

Section: Results Analysismentioning

confidence: 99%

Motion Recognition Based on Sum of the Squared Errors Distribution

Yang

Cui

et al. 2021

IEEE Access

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Human motion recognition is playing an increasingly important role in modern society. Direct recognition of complex motions has great limitations, so we usually study basic motions first. The key to the establishment of the basic motion set is how to determine the number of basic motions. Sum of the Squared Errors Distribution (SSED) is therefore proposed to determine the number of clustering classes of Self Organizing Maps (SOM). Secondly, the Weighted Tangent Segmentation (WTS) is also proposed to segment complex motions into simple ones, and the sequence with the time stamp is generated. Finally, Back Propagation with the time stamp (BPTS) is proposed to classify the simple motions according to the basic motion set, and the complex motion is recognized according to the time stamp. The motions of human upper limbs are used to verify the effectiveness of this method. SSED determines that the number of clusters of human upper limb motion is 10. The experimental results show that the correct segmentation rate (CSR) is 98.13%. The recognition rate of basic motions is 98.67% and 99.33% in user independent (UI) and user dependent (UD) experiments, respectively. The recognition rate of complex motions is 96.37%. Experiments on UI and UD recognitions verified the effectiveness of our algorithm. Compared with other recognition algorithms, the complex motion recognition method proposed in this paper has better recognition performance.

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Section: Results Analysismentioning

confidence: 99%

Motion Recognition Based on Sum of the Squared Errors Distribution

Yang

Cui

et al. 2021

IEEE Access

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“…For example, the process starts with Equation ( 16) where the input (independent variable) is set to the normalized distance measured from edge to center and the output (dependable variable) is set to Gesture 0 or 6. For the rest equations (17)(18)(19)(20), the DT mechanism is similar and the process stops when all inputs are recognized.…”

Section: Methodsmentioning

confidence: 99%

“…Calculating the distances for the sixth highest peak, we plug the result into Equation ( 19) that is set to determine gesture 4 or 5. If p 6 satisfies Equation (19) which is designed to detect any further significant peak, the gesture can be 4 or 5. Then, Equation ( 20) performs the peak count to determine the final gesture type.…”

Section: Methodsmentioning

confidence: 99%

Smart Living: An Interactive Control System for Household Appliances

Chen

Arifai

et al. 2021

IEEE Access

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Hand-gesture based control has enormous potential both theoretically and for practical applications due to its convenience and intuitiveness. This study presents a real-time interactive control system for household appliances. The interactive control system allowing wireless control of household appliances using a combination of 11 hand gestures and 2 waving motions is tested on hundreds of samples. It is implemented using a regular personal computer (PC) and existing digital single processing (DSP) platforms. The evaluation results show that the system performs efficiently reaching an accuracy recognition rate of 91% and spending around 30 seconds to complete the control operation for household appliances. The contributions of this work are both academic (1) successful demonstration of the integration of algorithms for solving image detection, processing, and pattern recognition, and practical (2) showing its feasibility and using commonly available hardware and software configurations for practical uses, and finally (3) establishing a mechanism for intuitively interactive control system that facilitates smart living.INDEX TERMS Smart home, human computer interaction, hand gesture recognition, household appliance control.

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“…The review research methods of visual gesture recognition will be grouped according to the following family: static, dynamic, based on the supports (Kinect, Leap…etc), works that focus on the application of gesture recognition on robots and works on dealing with gesture recognition at the browser level Ameur, Safa, et al [14], authors suggest a dynamic hand gesture recognition approach over a Leap Motion system in this paper, which uses touchless hand movements. To begin, they use recurrent neural networks with Long Short-Term Memory (LSTM) to evaluate the sequential time series data collected from Leap Motion for recognition purposes.…”

Section: Related Workmentioning

confidence: 99%

Approach for Improving User Interface Based on Gesture Recognition

et al. 2021

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Gesture recognition technology based on visual detection to acquire gestures information is obtained in a non-contact manner. There are two types of gesture recognition: independent and continuous gesture recognition. The former aims to classify videos or other types of gesture sequences that only contain one isolated gesture instance in each sequence (e.g., RGB-D or skeleton data). In this study, we review existing research methods of visual gesture recognition and will be grouped according to the following family: static, dynamic, based on the supports (Kinect, Leap…etc), works that focus on the application of gesture recognition on robots and works on dealing with gesture recognition at the browser level. Following that, we take a look at the most common JavaScript-based deep learning frameworks. Then we present the idea of defining a process for improving user interface control based on gesture recognition to streamline the implementation of this mechanism.

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A novel hybrid bidirectional unidirectional LSTM network for dynamic hand gesture recognition with Leap Motion

Cited by 78 publications

References 38 publications

Motion Recognition Based on Sum of the Squared Errors Distribution

Motion Recognition Based on Sum of the Squared Errors Distribution

Smart Living: An Interactive Control System for Household Appliances

Approach for Improving User Interface Based on Gesture Recognition

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