SenseBack: Enabling Technologies for Sensory Feedback in Next-Generation Assistive Devices
Underpinning research related to SenseBack in research topics:
Completed Project (2015-2018)
Research Team: Ian Williams, Adrien Rapeaux, Timothy Constandinou
Collaborators: Kianoush Nazarpour (Newcastle), Francisco Sepulveda (Essex), Luidi Jiang (Southampton), Ed Chadwick (Keele), Paul Steenson and Dr Rory J O'Conner (Leeds)
Funding: Engineering and Physical Sciences Research Council (EPSRC) EP/M025977/1
An artificial arm, or prosthesis, is an example of technology that can be used to help somebody perform essential activities of daily living after a serious injury that results in the loss of their arm. Such activities might include eating, washing, opening doors, or shaking hands with a friend. Many artificial arms on the market these days are highly sophisticated, offering individual finger movement, and even movement of segments within a finger, that resemble the natural arm and hand.
These prosthetic arms are often controlled by sensing the contractions in the muscles of the remaining arm to which the prosthesis is attached, allowing the user to operate the arm by flexing their muscles. However, one key aspect of artificial arms that is currently missing is the sense of feedback. In other words, the user does not know where the arm is or how wide open the hand is without looking at it, and if a delicate object is picked up, there is no sense of how hard it is being gripped. This leads to slow and awkward use of the artificial arm and prevents its use from becoming truly natural.
The goal of this project is to develop technologies that will enable the next generation of assistive devices to provide truly natural control through enhanced sensory feedback. Our long-term vision is for artificial arms that provide the user with a sense of feedback that recreates the natural feedback associated with a real arm.
To enable this level of feedback, we must meet two clear objectives: to generate artificial signals that mimic those of the natural arm and hand, and to provide a means of delivering those signals to the nervous system of a prosthesis user.
These objectives will be achieved by: building new fingertip sensors to give the prosthesis a realistic sense of touch, including pressure, shear and temperature; developing a 'virtual hand' that mimics the nerve impulses that would be produced by a real hand, giving the user a sense of position of an artificial hand; and designing electrodes and a stimulation system that can deliver the simulated nerve impulses directly to the individual's nervous system.
Our role within SenseBack was to develop the microelectronics -- specifically, a multichannel bidirectional neural interface that combines recording and stimulation capability, and integrate this within an implantable platform for chronic experiments.
Our work on the SenseBack project has resulted in a number of key outcomes. These include:
- An integrated circuit for bidirectional neural interfacing (combining recording and stimulation) allowing for dynamic reconfigurability;
- An implantable platform that allows for bidirectional neural interfacing in chronic experiments;
- New advanced methods for safe and selective stimulation of the peripheral nervous system;
- Intellectual property through a relevant patent application;
- An industry internship (Adrien Rapeaux in Galvani Bioelectronics) and subsequent collaboration.
- Williams I, Leene L, Constandinou TG, 2018, Next Generation Neural Interface Electronics, Circuit Design Considerations for Implantable Devices, Editors: Cong, Publisher: River Publishers, Pages: 141-178, ISBN: 978-87-93519-86-2
- Rapeaux A, Brunton E, Nazarpour K, Constandinou TG, 2018, Preliminary study of time to recovery of rat sciatic nerve from high frequency alternating current nerve block, 40th International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Publisher: IEEE
- Liu Y, Luan S, Williams I, Rapeaux A, Constandinou TG, 2017, A 64-Channel Versatile Neural Recording SoC with Activity Dependant Data Throughput, IEEE Transactions on Biomedical Circuits and Systems, Vol: 11, Pages: 1344-1355, ISSN: 1932-4545
- Williams I, Rapeaux A, Liu Y, Luan S, Constandinou TG, 2016, A 32-Ch. Bidirectional Neural/EMG Interface with on-Chip Spike Detection for Sensorimotor Feedback, 12th IEEE Biomedical Circuits and Systems Conference (BioCAS), Publisher: IEEE, Pages: 528-531, ISSN: 2163-4025
- Luan S, Liu Y, Williams I, Constandinou TG, 2016, An Event-Driven SoC for Neural Recording, 12th IEEE Biomedical Circuits and Systems Conference (BioCAS), Publisher: IEEE, Pages: 404-407, ISSN: 2163-4025
- Rapeaux A, Nikolic K, Williams I, Eftekhar A, Constandinou TG, 2015, Fiber Size-Selective Stimulation using Action Potential Filtering for a Peripheral Nerve Interface: A Simulation Study, 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Publisher: IEEE, Pages: 3411-3414, ISSN: 1557-170X