ArticleSensors (Basel, Switzerland)2022
American Sign Language Words Recognition of Skeletal Videos Using Processed Video Driven Multi-Stacked Deep LSTM.
Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- A Comprehensive Review of SLR Systems: Challenges, Datasets, and Unresolved Gaps.Journal of imaging · 2026Review
- A wavelet attention fusion and YOLO based approach for robust multi scale hand pose estimation.Scientific reports · 2026Article
- Dynamic Kannada Sign Language Recognition on Resource Constrained Devices.Scientific reports · 2026Article
- A Deep Neural Network Framework for Dynamic Two-Handed Indian Sign Language Recognition in Hearing and Speech-Impaired Communities.Sensors (Basel, Switzerland) · 2025Article
- Efficient spatio-temporal modeling for sign language recognition using CNN and RNN architectures.Frontiers in artificial intelligence · 2025Article
- Novel Wearable System to Recognize Sign Language in Real Time.Sensors (Basel, Switzerland) · 2024Article
- Intelligent real-life key-pixel image detection system for early Arabic sign language learners.PeerJ. Computer science · 2024Article
- Lie Recognition with Multi-Modal Spatial-Temporal State Transition Patterns Based on Hybrid Convolutional Neural Network-Bidirectional Long Short-Term Memory.Brain sciences · 2023Article
- Multi-cue temporal modeling for skeleton-based sign language recognition.Frontiers in neuroscience · 2023Article
- Backhand-Approach-Based American Sign Language Words Recognition Using Spatial-Temporal Body Parts and Hand Relationship Patterns.Sensors (Basel, Switzerland) · 2022Article
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2 authors.
Funding
Abstract
Complex hand gesture interactions among dynamic sign words may lead to misclassification, which affects the recognition accuracy of the ubiquitous sign language recognition system. This paper proposes to augment the feature vector of dynamic sign words with knowledge of hand dynamics as a proxy and classify dynamic sign words using motion patterns based on the extracted feature vector. In this method, some double-hand dynamic sign words have ambiguous or similar features across a hand motion trajectory, which leads to classification errors. Thus, the similar/ambiguous hand motion trajectory is determined based on the approximation of a probability density function over a time frame. Then, the extracted features are enhanced by transformation using maximal information correlation. These enhanced features of 3D skeletal videos captured by a leap motion controller are fed as a state transition pattern to a classifier for sign word classification. To evaluate the performance of the proposed method, an experiment is performed with 10 participants on 40 double hands dynamic ASL words, which reveals 97.98% accuracy. The method is further developed on challenging ASL, SHREC, and LMDHG data sets and outperforms conventional methods by 1.47%, 1.56%, and 0.37%, respectively.
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