Bionic Hand Movements Recognition: A Unified Framework with Attention-Guided ROI Identification and the Bionic Fusion Net Approach

In prosthetics, bionic hand movement recognition is crucial to developing sophisticated systems that can effectively understand and react to human motions. Recent advances in image processing, feature extraction, and deep learning have improved bionic hand movement detection systems' accuracy and flexibility. This study proposes a unified framework called using attention-guided ROI detection and a unique Bionic Fusion-Net architecture to overcome these difficulties which contributes towards Sustainable Development Goal (SDG) Good Health and Well Being. Initially pre-processing undergoes dataset augmentation and image enhancement. The ROI Identification approach uses an attention-guided U-Net with sophisticated convolutional components. Spatial Features, BionicNet-1, and BionicNet-2 learn spatial and temporal features together during feature extraction. Optimized Red Fox Falcon Algorithm (O-RFF) which is a hybrid of Red Fox and Falcon Optimization Algorithms improves the feature selection. The Bionic Fusion-Net Architecture combines Xception, Squeeze-Net, Shuffle-Net, optimized Bi-LSTM, and Huber Loss function application. The recommended technique improves bionic hand movement recognition flexibility that attained an accuracy of about 99% which outperformed other approaches in use for well-being and future health policy.

@article{S2024,
  title     = {Bionic Hand Movements Recognition: A Unified Framework with Attention-Guided ROI Identification and the Bionic Fusion Net Approach},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {15},
  number    = {6},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {Prakash. S and Josephine H. H and Priya. S and M. Batumalay},
  doi       = {10.14569/IJACSA.2024.01506102},
  url       = {https://doi.org/10.14569/IJACSA.2024.01506102}
}