Development of ML model for triboelectric nanogenerator based sign language detection system
Sign language recognition (SLR) is vital for bridging communication gaps between deaf and hearing communities. Vision-based approaches suffer from occlusion, computational costs, and physical constraints. This work presents a comparison of machine learning (ML) and deep learning models for a custom triboelectric nanogenerator (TENG)-based sensor glove. Utilizing multivariate time-series data from five flex sensors, the study benchmarks traditional ML algorithms, feedforward neural networks, LSTM-based temporal models, and a multi-sensor MFCC CNN-LSTM architecture across 11 sign classes (digits 1-5, letters A-F). The proposed MFCC CNN-LSTM architecture processes frequency-domain features f...
| Authors | Meshv Patel, Bikash Baro, Sayan Bayan et al. |
|---|---|
| Primary category | eess.SP |
| Published | 26 March 2026 |
| Last updated at source | 26 March 2026 |
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