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Ding K, Rakhshan M, Paredes-Acuña N, Cheng G, Thakor NV. Sensory integration for neuroprostheses: from functional benefits to neural correlates. Med Biol Eng Comput 2024; 62:2939-2960. [PMID: 38760597 DOI: 10.1007/s11517-024-03118-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2023] [Accepted: 04/19/2024] [Indexed: 05/19/2024]
Abstract
In the field of sensory neuroprostheses, one ultimate goal is for individuals to perceive artificial somatosensory information and use the prosthesis with high complexity that resembles an intact system. To this end, research has shown that stimulation-elicited somatosensory information improves prosthesis perception and task performance. While studies strive to achieve sensory integration, a crucial phenomenon that entails naturalistic interaction with the environment, this topic has not been commensurately reviewed. Therefore, here we present a perspective for understanding sensory integration in neuroprostheses. First, we review the engineering aspects and functional outcomes in sensory neuroprosthesis studies. In this context, we summarize studies that have suggested sensory integration. We focus on how they have used stimulation-elicited percepts to maximize and improve the reliability of somatosensory information. Next, we review studies that have suggested multisensory integration. These works have demonstrated that congruent and simultaneous multisensory inputs provided cognitive benefits such that an individual experiences a greater sense of authority over prosthesis movements (i.e., agency) and perceives the prosthesis as part of their own (i.e., ownership). Thereafter, we present the theoretical and neuroscience framework of sensory integration. We investigate how behavioral models and neural recordings have been applied in the context of sensory integration. Sensory integration models developed from intact-limb individuals have led the way to sensory neuroprosthesis studies to demonstrate multisensory integration. Neural recordings have been used to show how multisensory inputs are processed across cortical areas. Lastly, we discuss some ongoing research and challenges in achieving and understanding sensory integration in sensory neuroprostheses. Resolving these challenges would help to develop future strategies to improve the sensory feedback of a neuroprosthetic system.
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Affiliation(s)
- Keqin Ding
- Department of Biomedical Engineering, Johns Hopkins School of Medicine, Baltimore, MD, 21205, USA.
| | - Mohsen Rakhshan
- Department of Electrical and Computer Engineering, University of Central Florida, Orlando, FL, 32816, USA
- Disability, Aging, and Technology Cluster, University of Central Florida, Orlando, FL, 32816, USA
| | - Natalia Paredes-Acuña
- Institute for Cognitive Systems, School of Computation, Information and Technology, Technical University of Munich, 80333, Munich, Germany
| | - Gordon Cheng
- Institute for Cognitive Systems, School of Computation, Information and Technology, Technical University of Munich, 80333, Munich, Germany
| | - Nitish V Thakor
- Department of Biomedical Engineering, Johns Hopkins School of Medicine, Baltimore, MD, 21205, USA
- Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD, 21205, USA
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2
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Ackerley R. Perceptual constancy of texture: how touch of textures on the foot feels equivalent to the hand and over different movements. J Neurophysiol 2024; 132:1238-1240. [PMID: 39319785 DOI: 10.1152/jn.00420.2024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2024] [Accepted: 09/20/2024] [Indexed: 09/26/2024] Open
Affiliation(s)
- Rochelle Ackerley
- Aix Marseille Univ, CNRS, CRPN (Centre de Recherche en Psychologie et Neurosciences - UMR 7077), Marseille, France
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3
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Valero-Cuevas FJ, Finley J, Orsborn A, Fung N, Hicks JL, Huang HH, Reinkensmeyer D, Schweighofer N, Weber D, Steele KM. NSF DARE-Transforming modeling in neurorehabilitation: Four threads for catalyzing progress. J Neuroeng Rehabil 2024; 21:46. [PMID: 38570842 PMCID: PMC10988973 DOI: 10.1186/s12984-024-01324-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2023] [Accepted: 02/09/2024] [Indexed: 04/05/2024] Open
Abstract
We present an overview of the Conference on Transformative Opportunities for Modeling in Neurorehabilitation held in March 2023. It was supported by the Disability and Rehabilitation Engineering (DARE) program from the National Science Foundation's Engineering Biology and Health Cluster. The conference brought together experts and trainees from around the world to discuss critical questions, challenges, and opportunities at the intersection of computational modeling and neurorehabilitation to understand, optimize, and improve clinical translation of neurorehabilitation. We organized the conference around four key, relevant, and promising Focus Areas for modeling: Adaptation & Plasticity, Personalization, Human-Device Interactions, and Modeling 'In-the-Wild'. We identified four common threads across the Focus Areas that, if addressed, can catalyze progress in the short, medium, and long terms. These were: (i) the need to capture and curate appropriate and useful data necessary to develop, validate, and deploy useful computational models (ii) the need to create multi-scale models that span the personalization spectrum from individuals to populations, and from cellular to behavioral levels (iii) the need for algorithms that extract as much information from available data, while requiring as little data as possible from each client (iv) the insistence on leveraging readily available sensors and data systems to push model-driven treatments from the lab, and into the clinic, home, workplace, and community. The conference archive can be found at (dare2023.usc.edu). These topics are also extended by three perspective papers prepared by trainees and junior faculty, clinician researchers, and federal funding agency representatives who attended the conference.
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Affiliation(s)
- Francisco J Valero-Cuevas
- Alfred E. Mann Department of Biomedical Engineering, University of Southern California, 1042 Downey Way, Los Angeles, 90089, CA, USA.
- Division of Biokinesiology and Physical Therapy, University of Southern California, 1540 Alcazar St 155, Los Angeles, 90033, CA, USA.
- Thomas Lord Department of Computer Science, University of Southern California, 941 Bloom Walk, Los Angeles, 90089, CA, USA.
| | - James Finley
- Division of Biokinesiology and Physical Therapy, University of Southern California, 1540 Alcazar St 155, Los Angeles, 90033, CA, USA
| | - Amy Orsborn
- Department of Electrical and Computer Engineering, University of Washington, 185 W Stevens Way NE, Box 352500, Seattle, 98195, WA, USA
- Department of Bioengineering, University of Washington, 3720 15th Ave NE, Box 355061, Seattle, 98195, WA, USA
- Washington National Primate Research Center, University of Washington, 3018 Western Ave, Seattle, 98121, WA, USA
| | - Natalie Fung
- Thomas Lord Department of Computer Science, University of Southern California, 941 Bloom Walk, Los Angeles, 90089, CA, USA
| | - Jennifer L Hicks
- Department of Bioengineering, Stanford University, 443 Via Ortega, Stanford, 94305, CA, USA
| | - He Helen Huang
- Joint Department of Biomedical Engineering, North Carolina State University, 1840 Entrepreneur Dr Suite 4130, Raleigh, 27606, NC, USA
- Joint Department of Biomedical Engineering, University of North Carolina at Chapel Hill, 333 S Columbia St, Chapel Hill, 27514, NC, USA
| | - David Reinkensmeyer
- Department of Mechanical and Aerospace Engineering, UCI Samueli School of Engineering, 3225 Engineering Gateway, Irvine, 92697, CA, USA
| | - Nicolas Schweighofer
- Alfred E. Mann Department of Biomedical Engineering, University of Southern California, 1042 Downey Way, Los Angeles, 90089, CA, USA
- Division of Biokinesiology and Physical Therapy, University of Southern California, 1540 Alcazar St 155, Los Angeles, 90033, CA, USA
| | - Douglas Weber
- Department of Mechanical Engineering and the Neuroscience Institute, Carnegie Mellon University, 5000 Forbes Avenue, B12 Scaife Hall, Pittsburgh, 15213, PA, USA
| | - Katherine M Steele
- Department of Mechanical Engineering, University of Washington, 3900 E Stevens Way NE, Box 352600, Seattle, 98195, WA, USA
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4
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Katic Secerovic N, Balaguer JM, Gorskii O, Pavlova N, Liang L, Ho J, Grigsby E, Gerszten PC, Karal-Ogly D, Bulgin D, Orlov S, Pirondini E, Musienko P, Raspopovic S, Capogrosso M. Neural population dynamics reveals disruption of spinal circuits' responses to proprioceptive input during electrical stimulation of sensory afferents. Cell Rep 2024; 43:113695. [PMID: 38245870 PMCID: PMC10962447 DOI: 10.1016/j.celrep.2024.113695] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2023] [Revised: 11/08/2023] [Accepted: 01/06/2024] [Indexed: 01/23/2024] Open
Abstract
While neurostimulation technologies are rapidly approaching clinical applications for sensorimotor disorders, the impact of electrical stimulation on network dynamics is still unknown. Given the high degree of shared processing in neural structures, it is critical to understand if neurostimulation affects functions that are related to, but not targeted by, the intervention. Here, we approach this question by studying the effects of electrical stimulation of cutaneous afferents on unrelated processing of proprioceptive inputs. We recorded intraspinal neural activity in four monkeys while generating proprioceptive inputs from the radial nerve. We then applied continuous stimulation to the radial nerve cutaneous branch and quantified the impact of the stimulation on spinal processing of proprioceptive inputs via neural population dynamics. Proprioceptive pulses consistently produce neural trajectories that are disrupted by concurrent cutaneous stimulation. This disruption propagates to the somatosensory cortex, suggesting that electrical stimulation can perturb natural information processing across the neural axis.
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Affiliation(s)
- Natalija Katic Secerovic
- School of Electrical Engineering, University of Belgrade, 11000 Belgrade, Serbia; The Mihajlo Pupin Institute, University of Belgrade, 11060 Belgrade, Serbia; Laboratory for Neuroengineering, Institute for Robotics and Intelligent Systems, ETH Zürich, 8092 Zürich, Switzerland
| | - Josep-Maria Balaguer
- Rehab and Neural Engineering Labs, University of Pittsburgh, Pittsburgh, PA 15213, USA; Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA 15213, USA; Center for Neural Basis of Cognition, Pittsburgh, PA, USA
| | - Oleg Gorskii
- Institute of Translational Biomedicine, Saint-Petersburg State University, 199034 Saint-Petersburg, Russia; Pavlov Institute of Physiology, Russian Academy of Sciences, 199034 Saint-Petersburg, Russia; National University of Science and Technology "MISIS," 4 Leninskiy Pr., 119049 Moscow, Russia
| | - Natalia Pavlova
- Institute of Translational Biomedicine, Saint-Petersburg State University, 199034 Saint-Petersburg, Russia
| | - Lucy Liang
- Rehab and Neural Engineering Labs, University of Pittsburgh, Pittsburgh, PA 15213, USA; Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA 15213, USA; Center for Neural Basis of Cognition, Pittsburgh, PA, USA
| | - Jonathan Ho
- Rehab and Neural Engineering Labs, University of Pittsburgh, Pittsburgh, PA 15213, USA; Department of Physical Medicine and Rehabilitation, University of Pittsburgh, Pittsburgh, PA 15213, USA
| | - Erinn Grigsby
- Rehab and Neural Engineering Labs, University of Pittsburgh, Pittsburgh, PA 15213, USA; Department of Physical Medicine and Rehabilitation, University of Pittsburgh, Pittsburgh, PA 15213, USA
| | - Peter C Gerszten
- Department of Neurological Surgery, University of Pittsburgh, Pittsburgh, PA 15213, USA
| | - Dzhina Karal-Ogly
- National Research Centre "Kurchatov Institute," 123098 Moscow, Russia
| | - Dmitry Bulgin
- National Research Centre "Kurchatov Institute," 123098 Moscow, Russia; Sirius University of Science and Technology, 354340 Sochi, Russia
| | - Sergei Orlov
- National Research Centre "Kurchatov Institute," 123098 Moscow, Russia
| | - Elvira Pirondini
- Rehab and Neural Engineering Labs, University of Pittsburgh, Pittsburgh, PA 15213, USA; Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA 15213, USA; Center for Neural Basis of Cognition, Pittsburgh, PA, USA; Department of Physical Medicine and Rehabilitation, University of Pittsburgh, Pittsburgh, PA 15213, USA; Department of Neurological Surgery, University of Pittsburgh, Pittsburgh, PA 15213, USA; Department of Neurobiology, University of Pittsburgh, Pittsburgh, PA 15213, USA
| | - Pavel Musienko
- Institute of Translational Biomedicine, Saint-Petersburg State University, 199034 Saint-Petersburg, Russia; Sirius University of Science and Technology, 354340 Sochi, Russia; Life Improvement by Future Technologies Center "LIFT," 143025 Moscow, Russia
| | - Stanisa Raspopovic
- Laboratory for Neuroengineering, Institute for Robotics and Intelligent Systems, ETH Zürich, 8092 Zürich, Switzerland.
| | - Marco Capogrosso
- Rehab and Neural Engineering Labs, University of Pittsburgh, Pittsburgh, PA 15213, USA; Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA 15213, USA; Center for Neural Basis of Cognition, Pittsburgh, PA, USA; Department of Physical Medicine and Rehabilitation, University of Pittsburgh, Pittsburgh, PA 15213, USA; Department of Neurological Surgery, University of Pittsburgh, Pittsburgh, PA 15213, USA.
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5
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Valle G, Katic Secerovic N, Eggemann D, Gorskii O, Pavlova N, Petrini FM, Cvancara P, Stieglitz T, Musienko P, Bumbasirevic M, Raspopovic S. Biomimetic computer-to-brain communication enhancing naturalistic touch sensations via peripheral nerve stimulation. Nat Commun 2024; 15:1151. [PMID: 38378671 PMCID: PMC10879152 DOI: 10.1038/s41467-024-45190-6] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2023] [Accepted: 01/17/2024] [Indexed: 02/22/2024] Open
Abstract
Artificial communication with the brain through peripheral nerve stimulation shows promising results in individuals with sensorimotor deficits. However, these efforts lack an intuitive and natural sensory experience. In this study, we design and test a biomimetic neurostimulation framework inspired by nature, capable of "writing" physiologically plausible information back into the peripheral nervous system. Starting from an in-silico model of mechanoreceptors, we develop biomimetic stimulation policies. We then experimentally assess them alongside mechanical touch and common linear neuromodulations. Neural responses resulting from biomimetic neuromodulation are consistently transmitted towards dorsal root ganglion and spinal cord of cats, and their spatio-temporal neural dynamics resemble those naturally induced. We implement these paradigms within the bionic device and test it with patients (ClinicalTrials.gov identifier NCT03350061). He we report that biomimetic neurostimulation improves mobility (primary outcome) and reduces mental effort (secondary outcome) compared to traditional approaches. The outcomes of this neuroscience-driven technology, inspired by the human body, may serve as a model for advancing assistive neurotechnologies.
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Affiliation(s)
- Giacomo Valle
- Laboratory for Neuroengineering, Department of Health Sciences and Technology, Institute for Robotics and Intelligent Systems, ETH Zürich, Zürich, Switzerland
| | - Natalija Katic Secerovic
- Laboratory for Neuroengineering, Department of Health Sciences and Technology, Institute for Robotics and Intelligent Systems, ETH Zürich, Zürich, Switzerland
- School of Electrical Engineering, University of Belgrade, 11000, Belgrade, Serbia
- The Mihajlo Pupin Institute, University of Belgrade, 11000, Belgrade, Serbia
| | - Dominic Eggemann
- Laboratory for Neuroengineering, Department of Health Sciences and Technology, Institute for Robotics and Intelligent Systems, ETH Zürich, Zürich, Switzerland
| | - Oleg Gorskii
- Laboratory for Neuroprosthetics, Institute of Translational Biomedicine, Saint-Petersburg State University, Saint-Petersburg, Russia
- Laboratory for Neuromodulation, Pavlov Institute of Physiology, Russian Academy of Sciences, Saint Petersburg, 199034, Russia
- Center for Biomedical Engineering, National University of Science and Technology "MISIS", 119049, Moscow, Russia
| | - Natalia Pavlova
- Laboratory for Neuroprosthetics, Institute of Translational Biomedicine, Saint-Petersburg State University, Saint-Petersburg, Russia
| | | | - Paul Cvancara
- Laboratory for Biomedical Microtechnology, Department of Microsystems Engineering-IMTEK, Bernstein Center, BrainLinks-BrainTools Center of Excellence, University of Freiburg, D-79110, Freiburg, Germany
| | - Thomas Stieglitz
- Laboratory for Biomedical Microtechnology, Department of Microsystems Engineering-IMTEK, Bernstein Center, BrainLinks-BrainTools Center of Excellence, University of Freiburg, D-79110, Freiburg, Germany
| | - Pavel Musienko
- Laboratory for Neuroprosthetics, Institute of Translational Biomedicine, Saint-Petersburg State University, Saint-Petersburg, Russia
- Sirius University of Science and Technology, Neuroscience Program, Sirius, Russia
- Laboratory for Neurorehabilitation Technologies, Life Improvement by Future Technologies Center "LIFT", Moscow, Russia
| | - Marko Bumbasirevic
- Orthopaedic Surgery Department, School of Medicine, University of Belgrade, 11000, Belgrade, Serbia
| | - Stanisa Raspopovic
- Laboratory for Neuroengineering, Department of Health Sciences and Technology, Institute for Robotics and Intelligent Systems, ETH Zürich, Zürich, Switzerland.
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6
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Cashaback JGA, Allen JL, Chou AHY, Lin DJ, Price MA, Secerovic NK, Song S, Zhang H, Miller HL. NSF DARE-transforming modeling in neurorehabilitation: a patient-in-the-loop framework. J Neuroeng Rehabil 2024; 21:23. [PMID: 38347597 PMCID: PMC10863253 DOI: 10.1186/s12984-024-01318-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2023] [Accepted: 01/25/2024] [Indexed: 02/15/2024] Open
Abstract
In 2023, the National Science Foundation (NSF) and the National Institute of Health (NIH) brought together engineers, scientists, and clinicians by sponsoring a conference on computational modelling in neurorehabiilitation. To facilitate multidisciplinary collaborations and improve patient care, in this perspective piece we identify where and how computational modelling can support neurorehabilitation. To address the where, we developed a patient-in-the-loop framework that uses multiple and/or continual measurements to update diagnostic and treatment model parameters, treatment type, and treatment prescription, with the goal of maximizing clinically-relevant functional outcomes. This patient-in-the-loop framework has several key features: (i) it includes diagnostic and treatment models, (ii) it is clinically-grounded with the International Classification of Functioning, Disability and Health (ICF) and patient involvement, (iii) it uses multiple or continual data measurements over time, and (iv) it is applicable to a range of neurological and neurodevelopmental conditions. To address the how, we identify state-of-the-art and highlight promising avenues of future research across the realms of sensorimotor adaptation, neuroplasticity, musculoskeletal, and sensory & pain computational modelling. We also discuss both the importance of and how to perform model validation, as well as challenges to overcome when implementing computational models within a clinical setting. The patient-in-the-loop approach offers a unifying framework to guide multidisciplinary collaboration between computational and clinical stakeholders in the field of neurorehabilitation.
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Affiliation(s)
- Joshua G A Cashaback
- Biomedical Engineering, Mechanical Engineering, Kinesiology and Applied Physiology, Biome chanics and Movement Science Program, Interdisciplinary Neuroscience Graduate Program, University of Delaware, 540 S College Ave, Newark, DE, 19711, USA.
| | - Jessica L Allen
- Department of Mechanical Engineering, University of Florida, Gainesville, USA
| | | | - David J Lin
- Division of Neurocritical Care and Stroke Service, Department of Neurology, Center for Neurotechnology and Neurorecovery, Massachusetts General Hospital, Harvard Medical School, Boston, USA
- Department of Veterans Affairs, Center for Neurorestoration and Neurotechnology, Rehabilitation Research and Development Service, Providence, USA
| | - Mark A Price
- Department of Mechanical and Industrial Engineering, Department of Kinesiology, University of Massachusetts Amherst, Amherst, USA
| | - Natalija K Secerovic
- School of Electrical Engineering, The Mihajlo Pupin Institute, University of Belgrade, Belgrade, Serbia
- Laboratory for Neuroengineering, Institute for Robotics and Intelligent Systems ETH Zürich, Zurich, Switzerland
| | - Seungmoon Song
- Mechanical and Industrial Engineering, Northeastern University, Boston, USA
| | - Haohan Zhang
- Department of Mechanical Engineering, University of Utah, Salt Lake City, USA
| | - Haylie L Miller
- School of Kinesiology, University of Michigan, 830 N University Ave, Ann Arbor, MI, 48109, USA.
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7
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Donati E, Valle G. Neuromorphic hardware for somatosensory neuroprostheses. Nat Commun 2024; 15:556. [PMID: 38228580 PMCID: PMC10791662 DOI: 10.1038/s41467-024-44723-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2022] [Accepted: 01/03/2024] [Indexed: 01/18/2024] Open
Abstract
In individuals with sensory-motor impairments, missing limb functions can be restored using neuroprosthetic devices that directly interface with the nervous system. However, restoring the natural tactile experience through electrical neural stimulation requires complex encoding strategies. Indeed, they are presently limited in effectively conveying or restoring tactile sensations by bandwidth constraints. Neuromorphic technology, which mimics the natural behavior of neurons and synapses, holds promise for replicating the encoding of natural touch, potentially informing neurostimulation design. In this perspective, we propose that incorporating neuromorphic technologies into neuroprostheses could be an effective approach for developing more natural human-machine interfaces, potentially leading to advancements in device performance, acceptability, and embeddability. We also highlight ongoing challenges and the required actions to facilitate the future integration of these advanced technologies.
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Affiliation(s)
- Elisa Donati
- Institute of Neuroinformatics, University of Zurich and ETH Zurich, Zurich, Switzerland.
| | - Giacomo Valle
- Department of Organismal Biology and Anatomy, University of Chicago, Chicago, IL, USA.
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8
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Cleland LD, Rowland HM, Mazzà C, Saal HP. Complexity of spatio-temporal plantar pressure patterns during everyday behaviours. J R Soc Interface 2023; 20:20230052. [PMID: 37376872 DOI: 10.1098/rsif.2023.0052] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Accepted: 06/09/2023] [Indexed: 06/29/2023] Open
Abstract
The human foot sole is the primary interface with the external world during balance and walking, and also provides important tactile information on the state of contact. However, prior studies on plantar pressure have focused mostly on summary metrics such as overall force or centre of pressure under limited conditions. Here, we recorded spatio-temporal plantar pressure patterns with high spatial resolution while participants completed a wide range of daily activities, including balancing, locomotion and jumping tasks. Contact area differed across task categories, but was only moderately correlated with the overall force experienced by the foot sole. The centre of pressure was often located outside the contact area or in locations experiencing relatively low pressure, and therefore a result of disparate contact regions spread widely across the foot. Non-negative matrix factorization revealed low-dimensional spatial complexity that increased during interaction with unstable surfaces. Additionally, pressure patterns at the heel and metatarsals decomposed into separately located and robustly identifiable components, jointly capturing most variance in the signal. These results suggest optimal sensor placements to capture task-relevant spatial information and provide insight into how pressure varies spatially on the foot sole during a wide variety of natural behaviours.
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Affiliation(s)
- Luke D Cleland
- Active Touch Laboratory, Department of Psychology, University of Sheffield, Sheffield, UK
- Insigneo Institute for in silico Medicine, University of Sheffield, Sheffield, UK
| | - Holly M Rowland
- Active Touch Laboratory, Department of Psychology, University of Sheffield, Sheffield, UK
- Insigneo Institute for in silico Medicine, University of Sheffield, Sheffield, UK
| | - Claudia Mazzà
- Insigneo Institute for in silico Medicine, University of Sheffield, Sheffield, UK
- Department of Mechanical Engineering, University of Sheffield, Sheffield, UK
| | - Hannes P Saal
- Active Touch Laboratory, Department of Psychology, University of Sheffield, Sheffield, UK
- Insigneo Institute for in silico Medicine, University of Sheffield, Sheffield, UK
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Cimolato A, Ciotti F, Kljajić J, Valle G, Raspopovic S. Symbiotic electroneural and musculoskeletal framework to encode proprioception via neurostimulation: ProprioStim. iScience 2023; 26:106248. [PMID: 36923003 PMCID: PMC10009292 DOI: 10.1016/j.isci.2023.106248] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2022] [Revised: 11/23/2022] [Accepted: 02/16/2023] [Indexed: 02/23/2023] Open
Abstract
Peripheral nerve stimulation in amputees achieved the restoration of touch, but not proprioception, which is critical in locomotion. A plausible reason is the lack of means to artificially replicate the complex activity of proprioceptors. To uncover this, we coupled neuromuscular models from ten subjects and nerve histologies from two implanted amputees to develop ProprioStim: a framework to encode proprioception by electrical evoking neural activity in close agreement with natural proprioceptive activity. We demonstrated its feasibility through non-invasive stimulation on seven healthy subjects comparing it with standard linear charge encoding. Results showed that ProprioStim multichannel stimulation was felt more natural, and hold promises for increasing accuracy in knee angle tracking, especially in future implantable solutions. Additionally, we quantified the importance of realistic 3D-nerve models against extruded models previously adopted for further design and validation of novel neurostimulation encoding strategies. ProprioStim provides clear guidelines for the development of neurostimulation policies restoring natural proprioception.
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Affiliation(s)
- Andrea Cimolato
- Neuroengineering Lab, Department of Health Sciences and Technology, Institute for Robotics and Intelligent Systems, ETH Zürich, 8092 Zürich, Switzerland
- Rehab Technologies Lab, Fondazione Istituto Italiano di Tecnologia, 16163 Genova, Italy
- Neuroengineering and Medical Robotics Laboratory, Department of Electronics, Information and Bioengineering, Politecnico di Milano, 20133 Milan, Italy
| | - Federico Ciotti
- Neuroengineering Lab, Department of Health Sciences and Technology, Institute for Robotics and Intelligent Systems, ETH Zürich, 8092 Zürich, Switzerland
| | - Jelena Kljajić
- Institute Mihajlo Pupin, Belgrade, 11060, Serbia
- School of Electrical Engineering, University of Belgrade, Belgrade, 11120, Serbia
| | - Giacomo Valle
- Neuroengineering Lab, Department of Health Sciences and Technology, Institute for Robotics and Intelligent Systems, ETH Zürich, 8092 Zürich, Switzerland
| | - Stanisa Raspopovic
- Neuroengineering Lab, Department of Health Sciences and Technology, Institute for Robotics and Intelligent Systems, ETH Zürich, 8092 Zürich, Switzerland
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