@article {LIU2023108767,
	title = {A wearable system for sign language recognition enabled by a convolutional neural network},
	journal = {Nano Energy},
	volume = {116},
	year = {2023},
	pages = {108767},
	abstract = {Sign language recognition is of great significance to connect the hearing/speech impaired and non-sign language communities. Compared to isolated word recognition, sentence recognition is more practical in real-world scenarios, but is also more complicated because continuous, high-quality sign data with distinct features must be collected and isolated signs must be identified with high accuracy. Here, we propose a wearable sign language recognition system enabled by a convolutional neural network (CNN) that integrates stretchable strain sensors and inertial measurement units attached to the body to perceive hand postures and movement trajectories. Forty-eight Chinese sign language words commonly used in daily life were collected and used to train the CNN model, and an isolated sign language word recognition accuracy of 95.85\% was achieved. For sentence-level sign language recognition, we proposed a method that combines multiple sliding windows and uses correlation analysis to improve the CNN recognition performance, achieving a correct rate of 84\% for 50 sign language sentence samples, showing good extendibility.},
	keywords = {Convolutional neural networks, Sign language recognition, Stretchable strain sensor, Wearable device},
	issn = {2211-2855},
	doi = {https://doi.org/10.1016/j.nanoen.2023.108767},
	url = {https://www.sciencedirect.com/science/article/pii/S2211285523006043},
	author = {Liu, Yuxuan and Jiang, Xijun and Xingge Yu and Huaidong Ye and Chao Ma and Wanyi Wang and Hu, Youfan}
}
