摘要：

2023 Little Lion Scientific.People affected with Neuro diseases and lost hands in accidents unable to perform their activities by their own, the technology aided supportive devices which accelerate the activities in day today activitiesmay be the primary requirement for this type of semi-paralyzed people. This research paper presents a novel approach to predicting prosthetic hand gestures using machine learning and deep learning techniques. Surface electromyography (sEMG) signals are collected from the user's forearm muscles, which are then processed to identify the intended hand gesture. The proposed model contributes the people affected semi- paralyzed stage to achieve their intended activities through Deep learning based object detection model. Thedataset consists of seven hand gestures commonly used in daily activities. To establish a baseline performance, the K-Nearest Neighbor (KNN) algorithm is employed and achieves an accuracy of 96%. To improve the prediction accuracy further, a Convolutional Neural Network (CNN) model is developed and trained on the same dataset. The CNN model achieves an accuracy of 86%, which is lower than the KNN model but still demonstrates promising results. In addition to the hand gesture prediction model, an object detection model is also developed. The dataset for this model is created from scratch and consists of images of everyday objects. The model uses a combination of deep learning techniques to identify the object in theimage and assigns a corresponding gesture that can be performed with the object using the prosthetic hand. The proposed models have several potential applications in the field of prosthetics. They can be used to develop prosthetic devices that are more intuitive and responsive to the user's intended gestures, improving their overall functionality and user experience. Moreover, the object detection model can be extended to identify more complex objects and gestures, expanding the range of activities that can be performed using the prosthetic hand. This study shows that it is possible to correctly predict prosthetic hand gestures using machine learning and deep learning techniques. The proposed models are a significant contribution to the field's research because they exhibit encouraging findings and have a number of possible applications in theprosthetics industry. The findings have implications for the creation of prosthetic hand control systems thatare more dependable and precise and that can be used in everyday life. Overall, this study shows how machine learning and deep learning techniques could advance in the field of prosthetics and, eventually, enhance the quality of life for people who have lost limbs.

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