Leap Motion Hand Gesture Recognition Based on Deep Neural Network

Yang, Qinglian; Ding, Weikang; Zhou, Xiaofang; Zhao, Dongdong; Shi, Yan

doi:10.1109/ccdc49329.2020.9164723

Cited by 16 publications

(2 citation statements)

References 18 publications

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“…Distal Phalanges Unit Vectors (DPUV) [ 62 ]: For each finger, the distal phalanges vector is defined as the vector from the distal interphalangeal joint to the fingertip, as presented in Figure 6 . This feature was normalized by dividing by its norm.…”

Section: Methodsmentioning

confidence: 99%

Classification of Activities of Daily Living Based on Grasp Dynamics Obtained from a Leap Motion Controller

Sharif

Ahmadreza

Chembrammel

et al. 2022

Sensors

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Stroke is one of the leading causes of mortality and disability worldwide. Several evaluation methods have been used to assess the effects of stroke on the performance of activities of daily living (ADL). However, these methods are qualitative. A first step toward developing a quantitative evaluation method is to classify different ADL tasks based on the hand grasp. In this paper, a dataset is presented that includes data collected by a leap motion controller on the hand grasps of healthy adults performing eight common ADL tasks. Then, a set of features with time and frequency domains is combined with two well-known classifiers, i.e., the support vector machine and convolutional neural network, to classify the tasks, and a classification accuracy of over 99% is achieved.

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Section: Methodsmentioning

confidence: 99%

Classification of Activities of Daily Living Based on Grasp Dynamics Obtained from a Leap Motion Controller

Sharif

Ahmadreza

Chembrammel

et al. 2022

Sensors

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show abstract

“…The design of the data acquisition device was developed as a result of a review of work in the field of obtaining and processing data from sensors installed on a person and showing the movement of body parts in space [23][24][25][26][27][28][29][30][31][32][33][34][35][36][37][38][39][40].…”

Section: No Punch (Np)mentioning

confidence: 99%

Recognition Punches in Karate Using Acceleration Sensors and Convolution Neural Networks

et al. 2021

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Coaches and athletes need to understand the kinematics and dynamics of karate kicks to improve the training process and results. The research was aimed at studying the automatic recognition of punches in karate using only linear acceleration sensors. Accelerometers were part of the Inertial Measurement Units (IMUs), which were attached to the left and right wrist of the athlete. To develop a model of punches, highly qualified athletes with 3-7 years of karate experience participated in the research. We analyzed the acceleration fields of various karate punches: Yun Tsuki, Mawashi Tsuki, Age of Tsuki, Uraken. A simpler approach to extracting features without calculating their statistical characteristics is proposed. To solve the classification problem, various architectures of convolutional neural networks are used: multilayer perceptron, 1-and 2-dimension Convolution Networks. Since the recognition of punches was carried out in the conditions of a shadow fight, in addition to the recognition of punches, another output parameter was introducedmovement without punches. Studies have shown a high level of punch recognition based on the developed models. The multi-class accuracy value is 0.96, and the average F1 value is 0.97 for five different punch classes. Thus, the proposed approach is more suitable for practical implementation in automatic learning systems.

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Hand Gesture Recognition Using Leap Motion Controller, Infrared Information, and Deep Learning Framework

Toalumbo

Nogales

2022

Smart Technologies, Systems and Applications

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Leap Motion Hand Gesture Recognition Based on Deep Neural Network

Cited by 16 publications

References 18 publications

Classification of Activities of Daily Living Based on Grasp Dynamics Obtained from a Leap Motion Controller

Classification of Activities of Daily Living Based on Grasp Dynamics Obtained from a Leap Motion Controller

Recognition Punches in Karate Using Acceleration Sensors and Convolution Neural Networks

Hand Gesture Recognition Using Leap Motion Controller, Infrared Information, and Deep Learning Framework

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