Researchers at Florida Atlantic University have developed a novel, personalized prosthetic control system that addresses the longstanding mismatch between standardized devices and individual anatomy. Led by Dr. Erik Engeberg, the team engineered a lightweight, two-piece wearable sleeve fabricated through three-dimensional printing based on precise digital scans of each user’s residual limb. Embedded within the custom-fit garment are soft, flexible magnetic sensors that detect subtle shifts in muscle shape and pressure, capturing biomechanical intent with unprecedented clarity. Unlike conventional systems that rely on generalized signal interpretation, this device pairs its sensor array with a personalized artificial intelligence model. The machine learning framework learns each user’s unique muscular patterns rather than applying a universal dataset, dynamically translating muscle activity into precise commands. Depending on limb dimensions and anatomical variations, the sleeve accommodates either eighteen or twenty-four sensor modules, with configurations optimized individually to maximize accuracy and comfort. In clinical trials involving ten participants, including three upper-limb amputees, the system successfully classified nineteen distinct hand and wrist gestures in real time, directly controlling a dexterous robotic prosthesis. The AI achieved accuracy rates exceeding ninety percent across multiple gestures when sensor placement matched individual residual muscle function. To validate long-term reliability, researchers subjected the sensors to over seven thousand five hundred robotic force cycles. Results published in IEEE Transactions on Neural Systems and Rehabilitation Engineering demonstrated stable signal output, clear noise separation, and negligible performance drift, confirming the hardware’s durability for daily wear. The findings underscore a critical shift in rehabilitative engineering: prosthetic control cannot be standardized. Signal stability and classification accuracy depend heavily on anatomical customization, suggesting that future clinical workflows will treat sensor configuration similarly to a medical prescription. The research team also compiled a comprehensive, shared dataset encompassing both amputee and non-amputee participants, providing a valuable resource for broader neuromuscular research. With an estimated two point one million Americans and over fifty million individuals worldwide living with limb loss, the demand for intuitive, reliable prosthetic interfaces continues to rise. Upper-limb restoration remains particularly complex due to the intricacy of natural hand kinematics. By closing the gap between engineering capability and biological reality, this personalized approach restores functional autonomy, reduces cognitive load during device operation, and establishes a scalable pathway for next-generation adaptive rehabilitation technology.