AI Co-Pilot Reshapes Control Of Bionic Hands

(MENAFN- The Arabian Post)

Artificial intelligence is beginning to change how people with upper-limb loss interact with prosthetic technology, with researchers at Newcastle University developing an AI“co-pilot” system designed to make bionic hands respond in a more natural and intuitive way. The approach, which blends human intention with machine assistance, aims to reduce the mental and physical strain many amputees face when using advanced prostheses, while improving precision in everyday tasks such as holding delicate objects or adjusting grip strength on the fly.

The system works by combining data from sensors placed on the residual limb with machine-learning models that learn how an individual user intends to move. Rather than forcing the wearer to consciously command every motion, the AI continuously interprets muscle signals and contextual cues, subtly assisting the movement in real time. Researchers describe this as shared control, where the human remains in charge but the machine helps smooth and refine actions that would otherwise require intense concentration.

This AI-guided shared control for prosthetic hands addresses a long-standing problem in bionics. Even the most advanced prosthetic hands often demand sustained mental effort, as users must translate intention into electrical signals that the device can interpret. Many amputees report fatigue, frustration and a sense of disconnection from the prosthesis, particularly during complex or prolonged tasks. By anticipating intended movements and correcting small errors, the AI co-pilot is intended to narrow the gap between thought and action.

Laboratory trials at Newcastle University have focused on common daily activities that typically expose the limitations of conventional prosthetic control. Tasks such as picking up fragile items, rotating objects, or switching smoothly between different grip types showed measurable improvements when the co-pilot system was active. Participants required fewer corrective movements and reported that the prosthetic felt more responsive, as if it were working with them rather than waiting for explicit commands.

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The research builds on a broader trend in prosthetics, where AI is increasingly used to personalise devices to individual users. Machine-learning models can adapt over time, refining their responses as they observe patterns in muscle signals and movement preferences. This adaptability is particularly important because no two amputees have identical residual limbs, muscle distributions or usage habits. Traditional one-size-fits-all control schemes often fail to account for this diversity, limiting comfort and long-term adoption.

Beyond physical performance, researchers are paying close attention to psychological factors. A recurring challenge in prosthetic use is alienation, the feeling that the artificial limb is an external tool rather than an integrated part of the body. By reducing the cognitive load required to operate the hand, the AI co-pilot may help users feel a stronger sense of ownership and embodiment. Early feedback from trial participants suggests that smoother, more predictable movements contribute to greater confidence in social and professional settings.

Despite its promise, the technology faces hurdles before it can be widely deployed. Cost remains a major barrier in advanced prosthetics, particularly when sophisticated sensors and on-device computing are involved. Regulatory approval also presents challenges, as AI-driven systems that adapt over time raise questions about safety, accountability and consistency of performance. Developers must demonstrate that learning algorithms remain reliable under varied conditions and do not introduce unexpected behaviours.

There are also practical considerations around training and support. While the goal is to make prosthetic use more intuitive, users still need time to familiarise themselves with shared-control systems. Clinicians and prosthetists will require new tools and guidelines to calibrate AI-assisted devices and monitor how they evolve with use. Ensuring transparency in how decisions are made by the AI is likely to be crucial for building trust among users and regulators alike.

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