ArticleSensors (Basel, Switzerland)2020
British Sign Language Recognition via Late Fusion of Computer Vision and Leap Motion with Transfer Learning to American Sign Language.
Article in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- A comparative analysis of video vision transformers on word-level sign language datasets.PloS one · 2026Article
- SignVLM: a pre-trained large video model for sign language recognition.PeerJ. Computer science · 2025Article
- A Model of Multi-Finger Coordination in Keystroke Movement.Sensors (Basel, Switzerland) · 2024Article
- Automated sign language detection and classification using reptile search algorithm with hybrid deep learning.Heliyon · 2024Article
- Continuous Sign Language Recognition and Its Translation into Intonation-Colored Speech.Sensors (Basel, Switzerland) · 2023Article
- A multimodal human-robot sign language interaction framework applied in social robots.Frontiers in neuroscience · 2023Article
- A Sign Language Recognition System Applied to Deaf-Mute Medical Consultation.Sensors (Basel, Switzerland) · 2022Article
- Backhand-Approach-Based American Sign Language Words Recognition Using Spatial-Temporal Body Parts and Hand Relationship Patterns.Sensors (Basel, Switzerland) · 2022Article
- American Sign Language Words Recognition of Skeletal Videos Using Processed Video Driven Multi-Stacked Deep LSTM.Sensors (Basel, Switzerland) · 2022Article
- American Sign Language Alphabet Recognition by Extracting Feature from Hand Pose Estimation.Sensors (Basel, Switzerland) · 2021Article
- Research on Discrete Semantics in Continuous Hand Joint Movement Based on Perception and Expression.Sensors (Basel, Switzerland) · 2021Article
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Authors and funding
3 authors.
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Abstract
In this work, we show that a late fusion approach to multimodality in sign language recognition improves the overall ability of the model in comparison to the singular approaches of image classification (88.14%) and Leap Motion data classification (72.73%). With a large synchronous dataset of 18 BSL gestures collected from multiple subjects, two deep neural networks are benchmarked and compared to derive a best topology for each. The Vision model is implemented by a Convolutional Neural Network and optimised Artificial Neural Network, and the Leap Motion model is implemented by an evolutionary search of Artificial Neural Network topology. Next, the two best networks are fused for synchronised processing, which results in a better overall result (94.44%) as complementary features are learnt in addition to the original task. The hypothesis is further supported by application of the three models to a set of completely unseen data where a multimodality approach achieves the best results relative to the single sensor method. When transfer learning with the weights trained via British Sign Language, all three models outperform standard random weight distribution when classifying American Sign Language (ASL), and the best model overall for ASL classification was the transfer learning multimodality approach, which scored 82.55% accuracy.
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