The purpose of this research is to create an intelligent robotic hand for people who have lost a limb below their elbow. By using artificial intelligence to adaptively grasp different types of objects, this will improve both the accuracy and flexibility of robotic prosthetic control. In addition, the project will integrate mechanical design and artificial intelligence based controls in order to produce a more functional and user-friendly prosthetic solution.

This research develops a low-cost, AI-powered prosthetic system for individuals with transradial amputations. The process begins with a 3D scan of the participant's residual limb to design customized, 3D-printed sockets and robotic hands. The core of the system integrates Artificial Intelligence to classify surface Electromyography (EMG) signals captured from the limb's muscles. This AI-driven pattern recognition allows for adaptive grasping of various objects. The study's primary objective is to compare this AI control system against traditional rule-based EMG programming. Both systems will be evaluated based on their effectiveness, adaptability, and response efficiency while the participant performs real-world grasping activities.

Inclusion Criteria:

• Participants with unilateral transradial amputation.
• Age between 18 and 60 years.
• Sufficient muscle activity in the residual limb to generate detectable EMG signals.
• Stable physical and mental health condition to undergo the testing.

Exclusion Criteria:

• History of severe skin diseases or open wounds at the site of EMG electrode placement.
• Cognitive impairments that prevent the participant from understanding or following instructions.
• Participation in other clinical trials that might interfere with the current study outcomes.

Individual participant data will not be shared to protect the privacy and confidentiality of the single participant involved in this case study, as the data includes sensitive EMG signals and physical scanning information that could potentially identify the individual.