The crux of their research centers on how kinesthetic feedback—our intrinsic ability to sense muscle movements—is not processed by the brain as discrete signals but rather as integrated patterns of hand movements, or “synergies.” This insight emerged from a unique comparison between two of the world’s only neural-machine interfaces aimed at reinstating kinesthetic sensation in prosthetic users. By merging data from these distinct technologies, the team discerned that the brain interprets vibrations generated to simulate muscle movement as coordinated hand grasps rather than isolated stimuli, reshaping our understanding of sensory processing in motor control.

Kinesthesia itself, the perception of muscle action and position, plays an indispensable role in seamlessly orchestrating voluntary movement. Amputation severs this natural feedback, rendering prosthetic control a less intuitive and more cognitively demanding task. Traditional approaches to prosthetic feedback have involved muscle vibration to simulate movement; however, these methods often inadvertently stimulate skin and muscular receptors simultaneously. Such dual activation may create conflicting inputs, confusing the sensorimotor integration pathways and impairing the user’s control fidelity.

Responding to these challenges, the Sant’Anna research team engineered the myokinetic kinesthetic interface (MKkI), a pioneering bidirectional system that employs minimally invasive implanted magnets within residual forearm muscles. These magnets produce precisely controlled vibrations that exclusively target muscular structures, circumventing the skin’s sensory receptors. When coupled with the robotic Mia Hand, a product of the Sant’Anna spin-off Prensilia, MKkI enables users to receive authentic feedback correlating directly with natural hand movements, promising a leap forward in prosthetic sensation.

Over a rigorous six-week clinical trial, this interface was evaluated with a 34-year-old patient, who reported perceiving hand opening and closing as fluid, coordinated patterns mirroring those experienced prior to amputation. The significance of such perceptual fidelity cannot be overstated; it reflects the potential to bridge the sensory gap that has long hindered prosthetic integration, making mechanical hands feel more like true extensions of the body rather than artificial tools.

The uniqueness of this innovation lies in its approach to stimulation. Unlike existing paradigms that rely on skin interaction to evoke sensations, MKkI’s targeted muscle vibration—facilitated by implantable magnets—offers a purer form of kinesthetic input. As Dr. Federico Masiero, lead author and current researcher at the Munich Institute of Robotics and Machine Intelligence, articulates, this strategy may unlock more nuanced insights into human motor control and pave the way for restoring movement sensations lost through limb loss.

Intriguingly, parallel research at the Cleveland Clinic features an alternative kinesthetic feedback system based on surgical nerve redirection partnered with robotics. Despite fundamental differences in their mechanisms—magnetic muscle vibration in MKkI versus nerve stimulation in the Cleveland model—both systems elicited remarkably similar perceptual outcomes. Users in both studies experienced induced sensations as cohesive finger movements rather than fragmented feedback, highlighting an intrinsic neurological preference for integrated movement patterns.

Moreover, both teams observed instances where sensations generated by these interfaces were detected subconsciously by users, suggesting that some aspects of kinesthetic feedback operate beneath the threshold of conscious awareness. This discovery adds layers of complexity to how sensory information is processed and integrated within the central nervous system and could influence future designs of prosthetic feedback systems that harmonize with natural neural processing.

Taken together, these findings challenge earlier assumptions that movement sensations in prosthetics must be artificial reconstructions of discrete sensor signals. Instead, the brain’s apparent integration of these cues as holistic synergies opens new pathways for developing devices that users control more intuitively and naturally, thereby enhancing their everyday functionality and user experience.

Looking ahead, the research team aims to refine this technology by incorporating prior advancements that enable reading the real-time position of implanted magnets to drive prosthetic movement—while concurrently using superimposed vibrations for sensory feedback. This closed-loop control system, marrying motor output with sensory input, underpins the vision of a fully integrated prosthetic limb that both moves and feels like a natural extension of the user’s body.

The long-term objective extends beyond temporary demonstration implants to the development of durable, permanent implants capable of maintaining function over prolonged periods. According to Professor Christian Cipriani, who spearheaded the MKkI design and study coordination, the initial six-week implant served as a proof-of-concept exhibiting promising efficacy. With plans underway to iterate on implant longevity and safety, future trials hope to enroll larger participant cohorts, bolstering the generalizability and clinical translation of these technologies.

This interdisciplinary project, orchestrated by The BioRobotics Institute of Sant’Anna with vital collaborations including Pisa University Hospital and the Cleveland Clinic, has been fortified through diverse funding pathways—ranging from European Research Council grants to U.S. NIH and DARPA backing. Such support underscores the global and cross-institutional recognition of the project’s transformative potential for healthcare and rehabilitation technologies.

By illuminating the sophisticated ways in which the brain organizes and perceives movement via implanted prostheses, this research lays a robust foundation for the next generation of neuroprosthetics. The combination of naturalistic grasp sensation with intuitive motor control could herald a paradigm shift—enabling amputees to reclaim not only function but also the profound sensory experience of hand use, thereby restoring agency and quality of life.

Subject of Research: Neural-machine interfaces for restoring kinesthetic sensation in upper limb prosthetics

Article Title: Coordinated hand movement sensation revealed through an implanted magnetic prosthetic kinesthetic interface

News Publication Date: 24-Jun-2026

Web References:

• DOI: 10.1126/sciadv.adx5046
• Cleveland Clinic kinesthetic feedback system overview: https://newsroom.clevelandclinic.org/2018/03/14/cleveland-clinic-researchers-uncover-new-way-to-restore-movement-sensation-in-patients-with-upper-limb-amputations/
• Stroke rehabilitation related applications: https://www.frontiersin.org/journals/neurorobotics/articles/10.3389/fnbot.2021.610673/full
• Prior work on position reading of implanted magnets: https://www.science.org/doi/10.1126/scirobotics.adp3260
• Collaborative studies groundwork: https://iopscience.iop.org/article/10.1088/1741-2552/ac6537

Image Credits: Sant’Anna School of Advanced Studies

Keywords

Prosthetics, Neural Interfaces, Kinesthetic Sensation, Hand Prosthesis, Myokinetic Interface, Motor Control, Sensory Feedback, Brain-Machine Interface, Rehabilitation Robotics, Amputation, Neuroprosthetics, Implanted Magnets

Human hands are sophisticated machines finely tuned by the brain to adjust grip force dynamically in response to environmental feedback. This sensorimotor integration allows humans to rarely drop tools during rigorous or rapid tasks—a feature that current prosthetic hands lack. The TKE-BGC emulates this biological feedback loop by incorporating three critical data streams: electromyographic (EMG) signals from residual limb muscles, tactile contact forces from the prosthetic fingertips, and kinesthetic information derived from joint angles. This multidimensional sensory fusion informs a biomimetic control strategy that dynamically adapts grip posture and force during task execution.

To generate the robust dataset necessary for training the TKE-BGC, researchers enlisted able-bodied participants who donned a data glove equipped with embedded tactile sensors and bend sensors, alongside EMG electrodes placed to capture residual muscle signals. Participants engaged in two primary tasks—hammering nails and sawing wooden strips—each embodying complex interaction dynamics characterized by rigid impacts and varying load distributions. These recordings offered a comprehensive portrait of how natural hands negotiate sudden perturbations, providing executable templates for the prosthetic control model.

At the heart of this system lies a sophisticated neural architecture. A Transformer encoder processes the multimodal inputs, leveraging cross-attention mechanisms where tactile and joint-angle data serve as queries that modulate the EMG feature representations. This design mirrors the biological sensorimotor loop, allowing tactile and kinesthetic feedback to dynamically influence motor commands. Subsequently, a multilayer perceptron predicts the necessary joint angle adjustments, ensuring real-time adaptability of the prosthetic hand during manipulation tasks.

The TKE-BGC’s performance was benchmarked against two established prosthetic grip control methods: fixed force (FiF) and force follows (FoF). FiF applies a constant preload without adaptation, while FoF modulates grip force based on lagging load measurements, often resulting in delayed or excessive responses. During offline analysis using the demonstration datasets, TKE-BGC outperformed both, achieving significantly lower root-mean-square error in predicting joint movements, particularly under the impact-intensive hammering conditions. This evidences its superior ability to anticipate and adapt to dynamic interaction forces.

Subsequent online studies involved six able-bodied individuals fitted with an extended limb apparatus and three transradial amputees. Each participant performed four tasks: the two “seen” tasks from training (hammering and sawing) and two unseen tasks involving different tools (peeling and desktop item organization). All control strategies were tested in randomized sequences. Remarkably, TKE-BGC consistently minimized tool drops and reduced task completion times across all conditions. In contrast, FiF and FoF frequently failed to maintain stable grasps, resulting in multiple drops and longer durations.

Beyond performance metrics, the TKE-BGC demonstrated physiological and user experience advantages. EMG amplitude analyses revealed markedly lower muscle activation during TKE-BGC operation (average 0.0023) compared to FoF (average 0.0124), implying reduced muscular effort and potentially less fatigue. Integrated EMG measurements aligned with these findings. Subjective feedback via the USE questionnaire highlighted higher user satisfaction, ease of use, and natural control sensations when employing TKE-BGC. Conversely, some users described FoF as reactive and resisting external forces, undermining intuitiveness.

A key triumph of the research is the system’s ability to generalize effectively. Despite training on data from a single able-bodied individual, TKE-BGC delivered consistent performance across diverse users, including amputees, and extended seamlessly to novel tasks without further training. This adaptability underscores the robustness of the multimodal fusion and the transformative potential of integrating tactile feedback—a modality whose removal in ablation studies caused the most significant degradation in predictive accuracy.

Current tactile feedback in the system is relatively sparse, with only nine contact force measurement points on the prosthetic contact surface. The researchers acknowledge that human skin provides dense, multidimensional sensory input that conveys nuanced information critical for manipulation. Plans are underway to incorporate high-density tactile sensor arrays coupled with advanced neural network architectures capable of interpreting local tactile imagery, promising richer sensory representations and finer control.

Moreover, the team aims to expand their dataset to include diverse manipulation styles from multiple participants, thereby capturing personalized strategies and enhancing adaptive prosthetic behavior. They also anticipate introducing optimization-based mapping techniques that can dynamically align joint postures for individualized anatomical and task requirements, further personalizing control and broadening applicability.

This pioneering research marks a significant paradigm shift in prosthetic hand technology, moving beyond static grasping to dynamic, context-aware manipulation. By harnessing the synergy of multimodal sensory integration and advanced computational models inspired by human neurophysiology, the TKE-BGC controller offers an empowering tool for amputees. Its implications extend from improving everyday living functionality to enabling vocational rehabilitation, ultimately fostering greater independence and quality of life.

The study was spearheaded by Professors Bin Fang and colleagues including Boao Li, Shuhui Wu, Ting You, among others, at the School of Artificial Intelligence, Beijing University of Posts and Telecommunications. The research received support from the Brain Science and Brain-like Intelligence Technology–National Science and Technology Major Project (grant no. 2025ZD0215600) and the National Natural Science Foundation of China (grant nos. 62573063, 62536001). Their findings, published in the May 2026 issue of Cyborg and Bionic Systems, showcase the promising trajectory of prosthetic hand research and its potential to reshape human-machine interactions.

Subject of Research: Prosthetic hand control and dynamic tool manipulation using multimodal sensory integration

Article Title: Dynamic Manipulation Skill Learning for Tactile Myoelectric Prosthetic Hands in Tool Handling

News Publication Date: May 13, 2026

Web References: DOI: 10.34133/cbsystems.0572

Image Credits: Bin Fang, School of Artificial Intelligence, Beijing University of Posts and Telecommunications

Keywords: Prosthetic hands, multimodal sensory integration, electromyography, tactile sensing, kinesthetic feedback, transformer encoder, real-time control, biomimetic gripping, tool manipulation, muscle fatigue reduction, dynamic grasp stability, sensorimotor fusion