Exploring Hand Gesture Recognition Techniques for Enhanced Control of Bionic Hands

Bionic hand prostheses are being developed to help patients who have lost the use of their hands due to an amputation. For bionic hands to be used naturally and intuitively, it is crucial to be able to reliably recognize and categorize hand motions. This study provides an overview of the current state of the art and future directions in bionic hand gesture recognition. The techniques of hand gesture detection discussed in this overview range from rule-based to template-based to hidden Markov models to neural networks to hybrid approaches. The difficulties of creating reliable and effective hand gesture recognition systems for bionic hands are also discussed in the research. These difficulties include dealing with noise, unpredictability, and individual variances. Future research topics in hand gesture identification for bionic hands are discussed to round up the article review. These include the use of multi-modal sensors, enhanced feature extraction techniques, and cutting-edge machine learning algorithms. The advancement of bionic hands and the betterment of the quality of life for those who have lost an upper limb to amputation depend on the creation of reliable and effective hand gesture recognition systems.[3]