Summary of Wearable Sensor-based Few-shot Continual Learning on Hand Gestures For Motor-impaired Individuals Via Latent Embedding Exploitation, by Riyad Bin Rafiq et al.
Wearable Sensor-Based Few-Shot Continual Learning on Hand Gestures for Motor-Impaired Individuals via Latent Embedding Exploitation
by Riyad Bin Rafiq, Weishi Shi, Mark V. Albert
First submitted to arxiv on: 14 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper introduces a novel approach to hand gesture recognition, designed specifically for motor-impaired individuals who require tailored gestures. Existing methods rely on pre-defined gestures, but this new framework, called Latent Embedding Exploitation (LEE), uses Few-Shot Continual Learning (FSCL) to fine-tune models for out-of-distribution data. The LEE mechanism leverages gesture prior knowledge and intra-gesture divergence to capture latent statistical structure in highly variable gestures with limited samples. This results in improved performance on the SmartWatch Gesture and Motion Gesture datasets, with average test accuracy of 57.0%, 64.6%, and 69.3% using one, three, and five samples for six different gestures. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The new method helps motor-impaired persons leverage wearable devices, learning and applying their unique styles of movement in human-computer interaction and social communication. The proposed framework has the potential to revolutionize communication for individuals with disabilities, enabling them to efficiently interact with technology using their own gestures. |
Keywords
» Artificial intelligence » Continual learning » Embedding » Few shot » Gesture recognition
