Dynamic Hand Gesture to Text using Leap Motion

Jamaludin, Nur Aliah Nadzirah; Fang, Ong Huey

doi:10.14569/ijacsa.2019.0101127

Cited by 7 publications

(2 citation statements)

References 16 publications

(20 reference statements)

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“…However, the hand's rotation is similar in the case of SRM words because these words have similar hand shapes and movements. The third group is the group including both single-and double-hand signs [43][44][45][46][47][48][49], in which both single-and double-hands are used. This group proposed a method to extract features from the hand, such as joint angles, fingertip position, finger direction, and finger velocity.…”

Section: Related Workmentioning

confidence: 99%

Backhand-Approach-Based American Sign Language Words Recognition Using Spatial-Temporal Body Parts and Hand Relationship Patterns

Chophuk

Chamnongthai

Chinnasarn

2022

Sensors

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Most of the existing methods focus mainly on the extraction of shape-based, rotation-based, and motion-based features, usually neglecting the relationship between hands and body parts, which can provide significant information to address the problem of similar sign words based on the backhand approach. Therefore, this paper proposes four feature-based models. The spatial–temporal body parts and hand relationship patterns are the main feature. The second model consists of the spatial–temporal finger joint angle patterns. The third model consists of the spatial–temporal 3D hand motion trajectory patterns. The fourth model consists of the spatial–temporal double-hand relationship patterns. Then, a two-layer bidirectional long short-term memory method is used to deal with time-independent data as a classifier. The performance of the method was evaluated and compared with the existing works using 26 ASL letters, with an accuracy and F1-score of 97.34% and 97.36%, respectively. The method was further evaluated using 40 double-hand ASL words and achieved an accuracy and F1-score of 98.52% and 98.54%, respectively. The results demonstrated that the proposed method outperformed the existing works under consideration. However, in the analysis of 72 new ASL words, including single- and double-hand words from 10 participants, the accuracy and F1-score were approximately 96.99% and 97.00%, respectively.

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Section: Related Workmentioning

confidence: 99%

Backhand-Approach-Based American Sign Language Words Recognition Using Spatial-Temporal Body Parts and Hand Relationship Patterns

Chophuk

Chamnongthai

Chinnasarn

2022

Sensors

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“…The reference background was stored from each frame which was compared with the next frame for reliable hand gesture recognition. [28] They developed a hand gesture recognition system using Leap motion sensors. The system was customized to recognize multiple motion-based hand gestures for controlling smart home appliances.…”

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confidence: 99%

Dynamic Hand Gesture Recognition for Smart Lifecare Routines via K-Ary Tree Hashing Classifier

et al. 2022

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In the past few years, home appliances have been influenced by the latest technologies and changes in consumer trends. One of the most desired gadgets of this time is a universal remote control for gestures. Hand gestures are the best way to control home appliances. This paper presents a novel method of recognizing hand gestures for smart home appliances using imaging sensors. The proposed model is divided into six steps. First, preprocessing is done to de-noise the video frames and resize each frame to a specific dimension. Second, the hand is detected using a single shot detector-based convolution neural network (SSD-CNN) model. Third, landmarks are localized on the hand using the skeleton method. Fourth, features are extracted based on point-based trajectories, frame differencing, orientation histograms, and 3D point clouds. Fifth, features are optimized using fuzzy logic, and last, the H-Hash classifier is used for the classification of hand gestures. The system is tested on two benchmark datasets, namely, the IPN hand dataset and Jester dataset. The recognition accuracy on the IPN hand dataset is 88.46% and on Jester datasets is 87.69%. Users can control their smart home appliances, such as television, radio, air conditioner, and vacuum cleaner, using the proposed system.

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Demands on User Interfaces for People with Intellectual Disabilities, Their Requirements, and Adjustments

Braun

Wölfel

2023

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Dynamic Hand Gesture to Text using Leap Motion

Cited by 7 publications

References 16 publications

Backhand-Approach-Based American Sign Language Words Recognition Using Spatial-Temporal Body Parts and Hand Relationship Patterns

Backhand-Approach-Based American Sign Language Words Recognition Using Spatial-Temporal Body Parts and Hand Relationship Patterns

Dynamic Hand Gesture Recognition for Smart Lifecare Routines via K-Ary Tree Hashing Classifier

Demands on User Interfaces for People with Intellectual Disabilities, Their Requirements, and Adjustments

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