Static Hand Gesture Recognition Using a Low-Cost Data Glove and Bayesian Neural Network
Main Article Content
Abstract
Gesture recognition has become an important focus in human-machine interaction (HMI). Static hand gesture recognition is particularly useful for detecting the intuitive intentions of individuals who are deaf or mute. In recent years, various data gloves have been developed to capture static hand gestures. These gloves, worn like regular gloves, serve as input devices for HMI systems. A low-cost data glove can be built using flex sensors to detect finger bending, enabling the collection of data on finger positions for different static gestures. This data can then be interpreted by a computer program. While many commercial data gloves are bundled with software, they are often prohibitively expensive. This research develops a low-cost data glove using flex sensors and an Arduino Nano. For accurate static gesture recognition, a Bayesian neural network (BNN) is employed to classify different gestures. To optimize training, the scaled-conjugate gradient method, an efficient, automated algorithm, is used to update the network’s weights and biases.
Keywords
Data glove, static hand gesture recognition, Bayesian neural network.
Article Details
References
[2] Mingzhang Pan, Yingzhe Tang, Hongqi Li, State-of-the-art in data gloves: A review of hardware, algorithms, and applications, IEEE Transactions on Instrumentation and Measurement, vol. 72, Feb. 2023,
https://doi.org/ 10.1109/TIM.2023.3243614.
[3] Jun Ma, Xunhuan Ren, Hao Li, Wenzu Li, Viktar Yurevich Tsviatkou, Anatoliy Antonovich Boriskevich, Noise-against skeleton extraction framework and application on hand gesture recognition, IEEE Access, vol. 11, pp. 9547-9559, Jan. 2023,
https://doi.org/ 10.1109/ACCESS.2023.3240313.
[4] Abdirahman Osman Hashi, Siti Zaiton Mohd Hashim, Azurah Bte Asamah, A systematic review of hand gesture recognition: An Update From 2018 to 2024, IEEE Access, vol. 12, pp. 143599-143626, Jul. 2024, https://doi.org/ 10.1109/ACCESS.2024.3421992.
[5] Lin Guo, Zongxing Lu, Ligang Yao, Human-Machine interaction sensing technology based on hand Gesture recognition: A Review, IEEE Transactions on Human-Machine Systems, vol. 51, no. 4, pp. 300-309, Aug. 2021,
https://doi.org/ 10.1109/THMS.2021.3086003.
[6] Yongfeng Dong, Jielong Liu, Wenjie Yan, Dynamic hand gesture recognition based on signals from specialized data glove and deep learning algorithms, IEEE Transactions on Instrumentation and Measurement, vol. 70, pp. 2509014-2509014, May, 2021,
https://doi.org/ 10.1109/TIM.2021.3077967.
[7] Youngmo Han, A low-cost visual motion data glove as an input device to interpret human hand gestures, IEEE Transactions on Consumer Electronics, vol. 56, no. 2, pp. 501-509, May, 2010,
https://doi.org/ 10.1109/TCE.2010.5505962.
[8] Minhyuk Lee, Joonbum Bae, Deep learning based real-time recognition of dynamic finger gestures using a data glove, IEEE Access, vol. 8, pp. 219923-219933, Nov. 2020,
https://doi.org/ 10.1109/ACCESS.2020.3039401.
[9] Yuichiro Mori, Masahiko Toyonaga, Data-glove for Japanese sign language training system with gyro-sensor, 2018 Joint 10th SCIS and 19th ISIS, Toyama, Japan, 2018.
[10] Yeongyu Park, Jeongsoo Lee, Joonbum Bae, Development of a wearable sensing glove for measuring the motion of fingers using linear potentiometers and flexible wires, IEEE Transactions on Industrial Informatics, vol. 11, no. 11, pp. 198-206, Dec. 2014,
https://doi.org/ 10.1109/TII.2014.2381932.
[11] C. Jose L. Flores, A. E. Gladys Cutipa, R. Lauro Enciso, Application of convolutional neural networks for static hand gestures recognition under different invariant features, In 2017 IEEE XXIV (INTERCON), pp. 1-4.
[12] Amrutnarayan Panigrahi, Jaganath Prasad Mohanty, Ayas Kanta Swain, Kamalakanta Mahapatra, Real-time efficient detection in vision based static hand gesture recognition, 2018 IEEE iSES, Hyderabad, India,
pp. 265-268, 2018.
[13] Raj Patel, Jash Dhakad, Kashish Desai, Tanay Gupta, Stevina Correia, Hand gesture recognition system using convolutional neural networks, 2018 4th ICCCA, Greater Noida, India. pp. 1-6, 2018
[14] Paulo Trigueiros, Fernando Ribeiro, Luís Paulo Reis, A comparison of machine learning algorithms applied to hand gesture recognition, 7th Iberian CISTI, Madrid, Spain, pp. 1-6. IEEE, 2012.
[15] Christopher M. Bishop, Neural Networks for Pattern Recognition, Oxford University Press, 1995.
[16] Ian Nabney, NETLAB: Algorithms for Pattern Recognition, Springer, 2002.