Hand gesture recognition for post-stroke rehabilitation using leap motion

Li, Wen-Jeng; Hsieh, Chia-Yeh; Lin, Li-Fong; Chu, Woei-Chyn

doi:10.1109/icasi.2017.7988433

Cited by 61 publications

(39 citation statements)

References 4 publications

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“…HGR models are human-computer systems that determine what gesture was performed and when a person performed the gesture. Currently, these systems are used, for example, in several applications, such as intelligent prostheses [1][2][3], sign language recognition [4,5], rehabilitation devices [6,7], and device control [8].…”

Section: Introductionmentioning

confidence: 99%

Real-Time Hand Gesture Recognition Using Surface Electromyography and Machine Learning: A Systematic Literature Review

Jaramillo-Yánez

Benalcázar

Mena-Maldonado

2020

Sensors

170

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Today, daily life is composed of many computing systems, therefore interacting with them in a natural way makes the communication process more comfortable. Human–Computer Interaction (HCI) has been developed to overcome the communication barriers between humans and computers. One form of HCI is Hand Gesture Recognition (HGR), which predicts the class and the instant of execution of a given movement of the hand. One possible input for these models is surface electromyography (EMG), which records the electrical activity of skeletal muscles. EMG signals contain information about the intention of movement generated by the human brain. This systematic literature review analyses the state-of-the-art of real-time hand gesture recognition models using EMG data and machine learning. We selected and assessed 65 primary studies following the Kitchenham methodology. Based on a common structure of machine learning-based systems, we analyzed the structure of the proposed models and standardized concepts in regard to the types of models, data acquisition, segmentation, preprocessing, feature extraction, classification, postprocessing, real-time processing, types of gestures, and evaluation metrics. Finally, we also identified trends and gaps that could open new directions of work for future research in the area of gesture recognition using EMG.

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Section: Introductionmentioning

confidence: 99%

Real-Time Hand Gesture Recognition Using Surface Electromyography and Machine Learning: A Systematic Literature Review

Jaramillo-Yánez

Benalcázar

Mena-Maldonado

2020

Sensors

170

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“…The results were assessed using k-fold cross validation method. Results shows that multi-class SVM and k-NN classifier achieved an accuracy of 97.29% and 97.71%, respectively [8].…”

Section: Methodsmentioning

confidence: 98%

“…This process of tracking is continuously repeated, and gestures are recognised until the hand leaves camera range. The training data set reached recognition rate of 99.90%, and the test data set got recognition rate of 95.61% [8].…”

Section: Methodsmentioning

confidence: 99%

Hand Gesture Recognition Using Convolutional Neural Network for People Who Have Experienced A Stroke

Alnaim

Abbod

Albar

2019

2019 3rd International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT)

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A human gesture is a non-verbal form of communication and is critical in human-robot interactions. Vision-based gesture recognition methods play a key role to detect hand motion and support such interactions. Hand gesture recognition allows a convenient and usable interface between devices and users. Hand gestures can be used for various fields which makes it be able to be implemented for communication and further. Hand gesture recognition is not only useful for people who are hearingimpaired or disabled but also for the people who experienced a stroke, as they need to communicate with other people using different common essential gestures such as the sign of eating, drink, family and, more. In this paper, an approach for recognizing hand gesture based on Convolutional Neural Network (CNN) is proposed. The developed method is evaluated and compared between training and testing modes based on several metrics such as execution time, accuracy, sensitivity, specificity, positive and negative predictive value, likelihood and root mean square. Results show that testing accuracy is 99% using CNN and is an effective technique in extracting distinct features and classifying data.

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“…Logically, in the same way as other technologies explained above, the use of Leap Motion has extended to other areas such as robotics [ 119 ], medical rehabilitation [ 120 ], home automation [ 121 ], identification and authentication [ 122 ], music [ 123 ] or education [ 124 ].…”

Section: Evolution Of Gesture Recognition Devicesmentioning

confidence: 99%

Perspective and Evolution of Gesture Recognition for Sign Language: A Review

Galván-Ruiz

González

Tejera-Fettmilch

et al. 2020

Sensors

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This review analyses the different gesture recognition systems through a timeline, showing the different types of technology, and specifying which are the most important features and their achieved recognition rates. At the end of the review, Leap Motion sensor possibilities are described in detail, in order to consider its application on the field of sign language. This device has many positive characteristics that make it a good option for sign language. One of the most important conclusions is the ability of the Leap Motion sensor to provide 3D information from the hands for due identification.

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Hand gesture recognition for post-stroke rehabilitation using leap motion

Cited by 61 publications

References 4 publications

Real-Time Hand Gesture Recognition Using Surface Electromyography and Machine Learning: A Systematic Literature Review

Real-Time Hand Gesture Recognition Using Surface Electromyography and Machine Learning: A Systematic Literature Review

Hand Gesture Recognition Using Convolutional Neural Network for People Who Have Experienced A Stroke

Perspective and Evolution of Gesture Recognition for Sign Language: A Review

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