Towards intuitive simultaneous control of a bionic limb
Doctoral thesis, 2025
Restoring arm function after limb loss with a prosthesis remains a major challenge. Recent advances in surgical techniques and engineering approaches are now enabling substantial restoration of functionality after amputation. This doctoral thesis investigates cutting-edge surgical and engineering strategies and their integration, aiming to achieve intuitive, simultaneous control over multiple bionic joints in myoelectric prostheses, thereby surpassing current clinical solutions.
A key focus was to understand how residual biological pathways after amputation, which naturally encode volitional movement, can be harnessed. We demonstrated that severed nerves can be redirected to innervate denervated native muscles and free muscle grafts, creating new, long-term stable myoelectric sources. These enabled simultaneous, proportional control of up to three degrees of freedom using a conventional one-to-one mapping strategy, improving functionality and reducing disability during extended home use. To further enhance motion-intent decoding and increase the number of controllable boinic joints, we explored deep learning methods and biologically inspired data-collection techniques for training neural networks. Our results show that deep learning architectures outperform shallow networks, facilitating intuitive simultaneous control. We further demonstrated that artificial training data can greatly reduce the burden of lengthy fitting sessions. These methods enabled intuitive, simultaneous, proportional control over 4.5 degrees of freedom in tasks representative of daily life.
Integrating these elements, we demonstrated for the first time that an individual with an above-elbow amputation could intuitively control all five fingers
of a bionic hand as if it were their own.
A key focus was to understand how residual biological pathways after amputation, which naturally encode volitional movement, can be harnessed. We demonstrated that severed nerves can be redirected to innervate denervated native muscles and free muscle grafts, creating new, long-term stable myoelectric sources. These enabled simultaneous, proportional control of up to three degrees of freedom using a conventional one-to-one mapping strategy, improving functionality and reducing disability during extended home use. To further enhance motion-intent decoding and increase the number of controllable boinic joints, we explored deep learning methods and biologically inspired data-collection techniques for training neural networks. Our results show that deep learning architectures outperform shallow networks, facilitating intuitive simultaneous control. We further demonstrated that artificial training data can greatly reduce the burden of lengthy fitting sessions. These methods enabled intuitive, simultaneous, proportional control over 4.5 degrees of freedom in tasks representative of daily life.
Integrating these elements, we demonstrated for the first time that an individual with an above-elbow amputation could intuitively control all five fingers
of a bionic hand as if it were their own.
Electro-neuromuscular constructs
Bionics
Prosthetic control
Myoelectric control
Prosthetics
Neuro-musculoskeletal interface
EA-salen, Hörsalsvägen 11
Opponent: Professor Strahinja Dosen, University of Aalborg, Denmark.
Author
Jan Zbinden
Chalmers, Electrical Engineering, Signal Processing and Biomedical Engineering
Improved control of a prosthetic limb by surgically creating electro-neuromuscular constructs with implanted electrodes
Science Translational Medicine,;Vol. 15(2023)p. eabq3665-
Journal article
Intuitive control of additional prosthetic joints via electro-neuromuscular constructs improves functional and disability outcomes during home use—a case study
Journal of Neural Engineering,;Vol. 21(2024)
Journal article
Deep Learning for Enhanced Prosthetic Control: Real-Time Motor Intent Decoding for Simultaneous Control of Artificial Limbs
IEEE Transactions on Neural Systems and Rehabilitation Engineering,;Vol. 32(2024)p. 1177-1186
Journal article
From sequential to simultaneous prosthetic control: Decoding simultaneous finger movements from individual ground truth EMG patterns
Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS,;(2024)
Paper in proceeding
Reconnecting the body: Restoring natural control and functionality after limb loss
Restoring arm function after limb loss has long been a challenge, but new advances in surgery and technology are changing what’s possible. This research focuses on making bionic prostheses more natural to use, allowing people that lost an arm to control a bionic arm and even individual fingers with their thoughts.
The key was tapping into the body’s remaining nerve pathways after amputation. By rerouting these severed nerves to other muscles, either existing ones or transplanted muscle tissue, we can create new, stable sources of electrical signals. These signals naturally carry information about the person’s intended movements. Using machine learning to interpret these signals, we can enable control over multiple joints of a prosthetic arm, restoring much of the lost functionality.
The combination of these surgical and technological innovations led to a groundbreaking achievement: for the first time, a person who lost their arm above the elbow was able to naturally control all five fingers of a bionic hand, as if it were their own. This represents a significant step toward making prosthetic limbs that are more functional and lifelike, greatly improving the quality of life for people who have lost limbs.
Restoring arm function after limb loss has long been a challenge, but new advances in surgery and technology are changing what’s possible. This research focuses on making bionic prostheses more natural to use, allowing people that lost an arm to control a bionic arm and even individual fingers with their thoughts.
The key was tapping into the body’s remaining nerve pathways after amputation. By rerouting these severed nerves to other muscles, either existing ones or transplanted muscle tissue, we can create new, stable sources of electrical signals. These signals naturally carry information about the person’s intended movements. Using machine learning to interpret these signals, we can enable control over multiple joints of a prosthetic arm, restoring much of the lost functionality.
The combination of these surgical and technological innovations led to a groundbreaking achievement: for the first time, a person who lost their arm above the elbow was able to naturally control all five fingers of a bionic hand, as if it were their own. This represents a significant step toward making prosthetic limbs that are more functional and lifelike, greatly improving the quality of life for people who have lost limbs.
Subject Categories (SSIF 2025)
Orthopaedics
Other Medical Engineering
Signal Processing
Areas of Advance
Health Engineering
DOI
10.63959/chalmers.dt/5804
ISBN
978-91-8103-347-2
Doktorsavhandlingar vid Chalmers tekniska högskola. Ny serie: 5804
Publisher
Chalmers
EA-salen, Hörsalsvägen 11
Opponent: Professor Strahinja Dosen, University of Aalborg, Denmark.