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Sagastegui Alva PG, Boesendorfer A, Aszmann OC, Ibáñez J, Farina D. Excitation of natural spinal reflex loops in the sensory-motor control of hand prostheses. Sci Robot 2024; 9:eadl0085. [PMID: 38809994 DOI: 10.1126/scirobotics.adl0085] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2023] [Accepted: 04/30/2024] [Indexed: 05/31/2024]
Abstract
Sensory feedback for prosthesis control is typically based on encoding sensory information in specific types of sensory stimuli that the users interpret to adjust the control of the prosthesis. However, in physiological conditions, the afferent feedback received from peripheral nerves is not only processed consciously but also modulates spinal reflex loops that contribute to the neural information driving muscles. Spinal pathways are relevant for sensory-motor integration, but they are commonly not leveraged for prosthesis control. We propose an approach to improve sensory-motor integration for prosthesis control based on modulating the excitability of spinal circuits through the vibration of tendons in a closed loop with muscle activity. We measured muscle signals in healthy participants and amputees during different motor tasks, and we closed the loop by applying vibration on tendons connected to the muscles, which modulated the excitability of motor neurons. The control signals to the prosthesis were thus the combination of voluntary control and additional spinal reflex inputs induced by tendon vibration. Results showed that closed-loop tendon vibration was able to modulate the neural drive to the muscles. When closed-loop tendon vibration was used, participants could achieve similar or better control performance in interfaces using muscle activation than without stimulation. Stimulation could even improve prosthetic grasping in amputees. Overall, our results indicate that closed-loop tendon vibration can integrate spinal reflex pathways in the myocontrol system and open the possibility of incorporating natural feedback loops in prosthesis control.
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Affiliation(s)
| | - Anna Boesendorfer
- Clinical Laboratory for Bionic Extremity Reconstruction, Department of Plastic, Reconstructive and Aesthetic Surgery, Medical University of Vienna, Vienna, Austria
| | - Oskar C Aszmann
- Clinical Laboratory for Bionic Extremity Reconstruction, Department of Plastic, Reconstructive and Aesthetic Surgery, Medical University of Vienna, Vienna, Austria
- Department of Plastic, Reconstructive and Aesthetic Surgery, Medical University of Vienna, Vienna, Austria
| | - Jaime Ibáñez
- Department of Bioengineering, Imperial College London, London, UK
- BSICoS group, I3A Institute, University of Zaragoza, IIS Aragón, Zaragoza, Spain
| | - Dario Farina
- Department of Bioengineering, Imperial College London, London, UK
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2
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Bensmaia SJ, Tyler DJ, Micera S. Restoration of sensory information via bionic hands. Nat Biomed Eng 2023; 7:443-455. [PMID: 33230305 PMCID: PMC10233657 DOI: 10.1038/s41551-020-00630-8] [Citation(s) in RCA: 80] [Impact Index Per Article: 80.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2019] [Accepted: 09/13/2020] [Indexed: 12/19/2022]
Abstract
Individuals who have lost the use of their hands because of amputation or spinal cord injury can use prosthetic hands to restore their independence. A dexterous prosthesis requires the acquisition of control signals that drive the movements of the robotic hand, and the transmission of sensory signals to convey information to the user about the consequences of these movements. In this Review, we describe non-invasive and invasive technologies for conveying artificial sensory feedback through bionic hands, and evaluate the technologies' long-term prospects.
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Affiliation(s)
- Sliman J Bensmaia
- Department of Organismal Biology and Anatomy, University of Chicago, Chicago, IL, USA.
- Committee on Computational Neuroscience, University of Chicago, Chicago, IL, USA.
- Grossman Institute for Neuroscience, Quantitative Biology, and Human Behavior, University of Chicago, Chicago, IL, USA.
| | - Dustin J Tyler
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA
- Louis Stokes Cleveland Veterans Affairs Medical Center, Cleveland, OH, USA
| | - Silvestro Micera
- The BioRobotics Institute, Scuola Superiore Sant'Anna, Pisa, Italy.
- Translational Neural Engineering Laboratory, Center for Neuroprosthetics and Institute of Bioengineering, School of Engineering, École Polytechnique Federale de Lausanne, Lausanne, Switzerland.
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Defi IR, Iskandar S, Charismawati S, Turnip A, Novita D. Healthcare Workers’ Point of View on Medical Robotics During COVID-19 Pandemic – A Scoping Review. Int J Gen Med 2022; 15:3767-3777. [PMID: 35418776 PMCID: PMC8995177 DOI: 10.2147/ijgm.s355734] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2022] [Accepted: 03/25/2022] [Indexed: 11/26/2022] Open
Abstract
COVID-19 affected how healthcare workers interact with patients. Medical technology and robotics are developed in hospital settings to limit human contact. The aim of this review is to elucidate what kind of medical robotics is required for healthcare workers during COVID-19 pandemic. This review was obtained from electronic databases such as Google Scholar, PubMed, EBSCO, and Cochrane reviews were searched for articles using keywords such as “healthcare professional” OR “health worker” AND “COVID-19” AND “robot application” OR “robotics” OR “health technology” AND “needs assessment” OR “expectation” OR “perception” published during 2020 to 2021. Inclusion criteria were full-text articles related to assessment of healthcare workers’ need for medical robotics during COVID-19 pandemics. Exclusion criteria included abstracts, duplicate articles, blogs, news articles, promotional brochures, and conference proceedings. A total of 13,692 articles were identified through the search engines (PubMed 179, Cochrane Library 1300, EBSCO 13, Google Scholar 12,200). Five full-text articles fulfilled the inclusion criteria. Determining robotic functions is important to healthcare workers who will be user of such medical technology. This review divided robotic functions into medical, operational, movement, and social functions. Healthcare workers’ demands for robotics were also influenced by the types of robots, such as examination robots, robot-based sample test and medicine production, surgery and rehabilitation robots, disinfection and cleaning robots, delivery and logistic robot, telemedicine, and telepresence robots. Medical robotics is required for healthcare workers during the COVID-19 pandemic. The highest demands for medical robotics functions include cardiac measurements and oxygen saturation monitoring (medical functions); examination record delivery, video and image play, and medical information delivery (operational functions); and the ability to recognize and avoid obstacles (movement functions). Disinfection and cleaning robots were the type of robots with the highest demand among healthcare workers.
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Affiliation(s)
- Irma Ruslina Defi
- Department of Physical Medicine and Rehabilitation, Hasan Sadikin General Hospital/Faculty of Medicine, Universitas Padjadjaran, Bandung, Jawa Barat, Indonesia
- Correspondence: Irma Ruslina Defi, Department of Physical Medicine and Rehabilitation, Hasan Sadikin General Hospital/Faculty of Medicine, Universitas Padjadjaran, Jl. Pasteur No. 38, Bandung, Jawa Barat, 40161, Indonesia, Tel +62 (22) 203 4989, Email
| | - Shelly Iskandar
- Department of Psychiatry, Hasan Sadikin General Hospital/Faculty of Medicine, Universitas Padjadjaran, Bandung, Jawa Barat, Indonesia
| | - Septiana Charismawati
- Department of Physical Medicine and Rehabilitation, Hasan Sadikin General Hospital/Faculty of Medicine, Universitas Padjadjaran, Bandung, Jawa Barat, Indonesia
| | - Arjon Turnip
- Department of Electrical Engineering, Faculty of Mathematics and Natural Sciences, Universitas Padjadjaran, Sumedang, Jawa Barat, Indonesia
| | - Dessy Novita
- Department of Electrical Engineering, Faculty of Mathematics and Natural Sciences, Universitas Padjadjaran, Sumedang, Jawa Barat, Indonesia
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Pandarinath C, Bensmaia SJ. The science and engineering behind sensitized brain-controlled bionic hands. Physiol Rev 2022; 102:551-604. [PMID: 34541898 PMCID: PMC8742729 DOI: 10.1152/physrev.00034.2020] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2020] [Revised: 09/07/2021] [Accepted: 09/13/2021] [Indexed: 12/13/2022] Open
Abstract
Advances in our understanding of brain function, along with the development of neural interfaces that allow for the monitoring and activation of neurons, have paved the way for brain-machine interfaces (BMIs), which harness neural signals to reanimate the limbs via electrical activation of the muscles or to control extracorporeal devices, thereby bypassing the muscles and senses altogether. BMIs consist of reading out motor intent from the neuronal responses monitored in motor regions of the brain and executing intended movements with bionic limbs, reanimated limbs, or exoskeletons. BMIs also allow for the restoration of the sense of touch by electrically activating neurons in somatosensory regions of the brain, thereby evoking vivid tactile sensations and conveying feedback about object interactions. In this review, we discuss the neural mechanisms of motor control and somatosensation in able-bodied individuals and describe approaches to use neuronal responses as control signals for movement restoration and to activate residual sensory pathways to restore touch. Although the focus of the review is on intracortical approaches, we also describe alternative signal sources for control and noninvasive strategies for sensory restoration.
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Affiliation(s)
- Chethan Pandarinath
- Department of Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, Georgia
- Department of Neurosurgery, Emory University, Atlanta, Georgia
| | - Sliman J Bensmaia
- Department of Organismal Biology and Anatomy, University of Chicago, Chicago, Illinois
- Committee on Computational Neuroscience, University of Chicago, Chicago, Illinois
- Grossman Institute for Neuroscience, Quantitative Biology, and Human Behavior, University of Chicago, Chicago, Illinois
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Vargas L, Huang H, Zhu Y, Hu X. Object Recognition via Evoked Sensory Feedback during Control of a Prosthetic Hand. IEEE Robot Autom Lett 2022; 7:207-214. [PMID: 35784093 PMCID: PMC9248871 DOI: 10.1109/lra.2021.3122897] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Abstract
Haptic and proprioceptive feedback is critical for sensorimotor integration when we use our hand to perform daily tasks. Here, we evaluated how externally evoked haptic and proprioceptive feedback and myoelectric control strategies affected the recognition of object properties when participants controlled a prosthetic hand. Fingertip haptic sensation was elicited using a transcutaneous nerve stimulation grid to encode the prosthetic's fingertip forces. An array of tactors elicited patterned vibratory stimuli to encode tactile-proprioceptive kinematic information of the prosthetic finger joint. Myoelectric signals of the finger flexor and extensor were used to control the position or velocity of joint angles of the prosthesis. Participants were asked to perform object property (stiffness and size) recognition, by controlling the prosthetic hand with concurrent haptic and tactile-proprioceptive feedback. With the evoked feedback, intact and amputee participants recognized the object stiffness and size at success rates ranging from 50% to 100% in both position and velocity control with no significant difference across control schemes. Our findings show that evoked somatosensory feedback in a non-invasive manner can facilitate closed-loop control of the prosthetic hand and allowed for simultaneous recognition of different object properties. The outcomes can facilitate our understanding on the role of sensory feedback during bidirectional human-machine interactions, which can potentially promote user experience in object interactions using prosthetic hands.
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Affiliation(s)
- Luis Vargas
- Joint Department of Biomedical Engineering at University of North Carolina-Chapel Hill and NC State University
| | - He Huang
- Joint Department of Biomedical Engineering at University of North Carolina-Chapel Hill and NC State University
| | - Yong Zhu
- Mechanical and Aerospace Engineering Department at NC State University
| | - Xiaogang Hu
- Joint Department of Biomedical Engineering at University of North Carolina-Chapel Hill and NC State University
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Atashzar SF, Carriere J, Tavakoli M. Review: How Can Intelligent Robots and Smart Mechatronic Modules Facilitate Remote Assessment, Assistance, and Rehabilitation for Isolated Adults With Neuro-Musculoskeletal Conditions? Front Robot AI 2021; 8:610529. [PMID: 33912593 PMCID: PMC8072151 DOI: 10.3389/frobt.2021.610529] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2020] [Accepted: 02/08/2021] [Indexed: 12/12/2022] Open
Abstract
Worldwide, at the time this article was written, there are over 127 million cases of patients with a confirmed link to COVID-19 and about 2.78 million deaths reported. With limited access to vaccine or strong antiviral treatment for the novel coronavirus, actions in terms of prevention and containment of the virus transmission rely mostly on social distancing among susceptible and high-risk populations. Aside from the direct challenges posed by the novel coronavirus pandemic, there are serious and growing secondary consequences caused by the physical distancing and isolation guidelines, among vulnerable populations. Moreover, the healthcare system's resources and capacity have been focused on addressing the COVID-19 pandemic, causing less urgent care, such as physical neurorehabilitation and assessment, to be paused, canceled, or delayed. Overall, this has left elderly adults, in particular those with neuromusculoskeletal (NMSK) conditions, without the required service support. However, in many cases, such as stroke, the available time window of recovery through rehabilitation is limited since neural plasticity decays quickly with time. Given that future waves of the outbreak are expected in the coming months worldwide, it is important to discuss the possibility of using available technologies to address this issue, as societies have a duty to protect the most vulnerable populations. In this perspective review article, we argue that intelligent robotics and wearable technologies can help with remote delivery of assessment, assistance, and rehabilitation services while physical distancing and isolation measures are in place to curtail the spread of the virus. By supporting patients and medical professionals during this pandemic, robots, and smart digital mechatronic systems can reduce the non-COVID-19 burden on healthcare systems. Digital health and cloud telehealth solutions that can complement remote delivery of assessment and physical rehabilitation services will be the subject of discussion in this article due to their potential in enabling more effective and safer NMSDK rehabilitation, assistance, and assessment service delivery. This article will hopefully lead to an interdisciplinary dialogue between the medical and engineering sectors, stake holders, and policy makers for a better delivery of care for those with NMSK conditions during a global health crisis including future pandemics.
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Affiliation(s)
- S. Farokh Atashzar
- Department of Electrical and Computer Engineering, Department of Mechanical and Aerospace Engineering, New York University, New York, NY, United States
| | - Jay Carriere
- Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB, Canada
| | - Mahdi Tavakoli
- Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB, Canada
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Cene VH, Balbinot A. Resilient EMG Classification to Enable Reliable Upper-Limb Movement Intent Detection. IEEE Trans Neural Syst Rehabil Eng 2020; 28:2507-2514. [PMID: 32956063 DOI: 10.1109/tnsre.2020.3024947] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
Reliable control of assistive devices using surface electromyography (sEMG) remains an unsolved task due to the signal's stochastic behavior that prevents robust pattern recognition for real-time control. Non-representative samples lead to inherent class overlaps that generate classification ripples for which the most common alternatives rely on post-processing and sample discard methods that insert additional delays and often do not offer substantial improvements. In this paper, a resilient classification pipeline based on Extreme Learning Machines (ELM) was used to classify 17 different upper-limb movements through sEMG signals from a total of 99 trials derived from three different databases. The method was compared to a baseline ELM and a sample discarding (DISC) method and proved to generate more stable and consistent classifications. The average accuracy boost of ≈ 10% in all databases lead to average weighted accuracy rates higher as 53,4% for amputees and 89,0% for non-amputee volunteers. The results match or outperform related works even without sample discards.
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