Research on Gesture Recognition Technology of Data Glove Based on Joint Algorithm
Shuran Yin, Jun Yang, Yukun Qu, Wenjun Liu, Yunfei Guo, Hongtao Liu, Dapeng Wei
Available Online March 2018.
- https://doi.org/10.2991/mecae-18.2018.8How to use a DOI?
- Gesture recognition, joint algorithm, data glove, bending sensor.
- A gesture recognition system were fabricated with distributed sensor networks as the core component, and could be wore in the hands. This data glove could detect the posture of fingers using the FLEX2.2 bending sensors and STM32 series single chip. Furthermore, we adopted a gesture recognition joint algorithm which combined template matching with BP neural network. First, the identify data from bending sensors could be obtained by template matching. Then, these data was inputted to the neural network, and got the final gesture recognition rate of joint algorithm. This joint algorithm enhanced minimal template recognition rate, and improved high training volume of BP neural network, showing the higher accuracy and adaptability of recognition. These research exhibit large potential in low cost, accurate, flexible, wearable sign language recognition systems.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
TY - CONF AU - Shuran Yin AU - Jun Yang AU - Yukun Qu AU - Wenjun Liu AU - Yunfei Guo AU - Hongtao Liu AU - Dapeng Wei PY - 2018/03 DA - 2018/03 TI - Research on Gesture Recognition Technology of Data Glove Based on Joint Algorithm BT - Proceedings of the 2018 International Conference on Mechanical, Electronic, Control and Automation Engineering (MECAE 2018) PB - Atlantis Press SP - 41 EP - 50 SN - 2352-5401 UR - https://doi.org/10.2991/mecae-18.2018.8 DO - https://doi.org/10.2991/mecae-18.2018.8 ID - Yin2018/03 ER -