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Cardoso LRL, Melendez-Calderon A, Bochkezanian V, Forner-Cordero A, Bo APL. Towards Visual-Tactile Integration of Shoulder and Hand Using Immersive Virtual Reality. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2023; 2023:1-4. [PMID: 38083309 DOI: 10.1109/embc40787.2023.10340578] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
Abstract
Shoulder-controlled hand neuroprostheses are wearable devices designed to assist hand function in people with cervical spinal cord injury (SCI). They use preserved shoulder movements to control artificial actuators. Due to the concurrent afferent (i.e., shoulder proprioception) and visual (i.e., hand response) feedback, these wearables may affect the user's body somatosensory representation. To investigate this effect, we propose an experimental paradigm that uses immersive virtual reality (VR) environment to emulate the use of a shoulder-controlled hand neuroprostheses and an adapted version of a visual-tactile integration task (i.e., Crossmodal Congruency Task) as an assessment tool. Data from seven non-disabled participants validates the experimental setup, with preliminary statistical analysis revealing no significant difference across the means of VR and visual-tactile integration tasks. The results serve as a proof-of-concept for the proposed paradigm, paving the way for further research with improvements in the experimental design and a larger sample size.
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A survey of human shoulder functional kinematic representations. Med Biol Eng Comput 2018; 57:339-367. [PMID: 30367391 PMCID: PMC6347660 DOI: 10.1007/s11517-018-1903-3] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2017] [Accepted: 12/17/2017] [Indexed: 10/28/2022]
Abstract
In this survey, we review the field of human shoulder functional kinematic representations. The central question of this review is to evaluate whether the current approaches in shoulder kinematics can meet the high-reliability computational challenge. This challenge is posed by applications such as robot-assisted rehabilitation. Currently, the role of kinematic representations in such applications has been mostly overlooked. Therefore, we have systematically searched and summarised the existing literature on shoulder kinematics. The shoulder is an important functional joint, and its large range of motion (ROM) poses several mathematical and practical challenges. Frequently, in kinematic analysis, the role of the shoulder articulation is approximated to a ball-and-socket joint. Following the high-reliability computational challenge, our review challenges this inappropriate use of reductionism. Therefore, we propose that this challenge could be met by kinematic representations, that are redundant, that use an active interpretation and that emphasise on functional understanding.
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Chmura J, Rosing J, Collazos S, Goodwin SJ. Classification of Movement and Inhibition Using a Hybrid BCI. Front Neurorobot 2017; 11:38. [PMID: 28860986 PMCID: PMC5559436 DOI: 10.3389/fnbot.2017.00038] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2017] [Accepted: 07/25/2017] [Indexed: 01/22/2023] Open
Abstract
Brain-computer interfaces (BCIs) are an emerging technology that are capable of turning brain electrical activity into commands for an external device. Motor imagery (MI)—when a person imagines a motion without executing it—is widely employed in BCI devices for motor control because of the endogenous origin of its neural control mechanisms, and the similarity in brain activation to actual movements. Challenges with translating a MI-BCI into a practical device used outside laboratories include the extensive training required, often due to poor user engagement and visual feedback response delays; poor user flexibility/freedom to time the execution/inhibition of their movements, and to control the movement type (right arm vs. left leg) and characteristics (reaching vs. grabbing); and high false positive rates of motion control. Solutions to improve sensorimotor activation and user performance of MI-BCIs have been explored. Virtual reality (VR) motor-execution tasks have replaced simpler visual feedback (smiling faces, arrows) and have solved this problem to an extent. Hybrid BCIs (hBCIs) implementing an additional control signal to MI have improved user control capabilities to a limited extent. These hBCIs either fail to allow the patients to gain asynchronous control of their movements, or have a high false positive rate. We propose an immersive VR environment which provides visual feedback that is both engaging and immediate, but also uniquely engages a different cognitive process in the patient that generates event-related potentials (ERPs). These ERPs provide a key executive function for the users to execute/inhibit movements. Additionally, we propose signal processing strategies and machine learning algorithms to move BCIs toward developing long-term signal stability in patients with distinctive brain signals and capabilities to control motor signals. The hBCI itself and the VR environment we propose would help to move BCI technology outside laboratory environments for motor rehabilitation in hospitals, and potentially for controlling a prosthetic.
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Affiliation(s)
- Jennifer Chmura
- Department of Biomedical Engineering, University of MinnesotaMinneapolis, MN, United States.,Department of Neuroscience, University of MinnesotaMinneapolis, MN, United States.,Department of Integrative Biology and Physiology, University of MinnesotaMinneapolis, MN, United States
| | - Joshua Rosing
- Department of Biomedical Engineering, University of MinnesotaMinneapolis, MN, United States
| | - Steven Collazos
- School of Mathematics, University of MinnesotaMinneapolis, MN, United States
| | - Shikha J Goodwin
- Department of Biomedical Engineering, University of MinnesotaMinneapolis, MN, United States.,Department of Neurology, University of Minnesota Medical SchoolMinneapolis, MN, United States.,Brain Sciences Center, VA Medical CenterMinneapolis, MN, United States
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Seáñez-González I, Pierella C, Farshchiansadegh A, Thorp EB, Wang X, Parrish T, Mussa-Ivaldi FA. Body-Machine Interfaces after Spinal Cord Injury: Rehabilitation and Brain Plasticity. Brain Sci 2016; 6:E61. [PMID: 27999362 PMCID: PMC5187575 DOI: 10.3390/brainsci6040061] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2016] [Revised: 12/06/2016] [Accepted: 12/12/2016] [Indexed: 01/07/2023] Open
Abstract
The purpose of this study was to identify rehabilitative effects and changes in white matter microstructure in people with high-level spinal cord injury following bilateral upper-extremity motor skill training. Five subjects with high-level (C5-C6) spinal cord injury (SCI) performed five visuo-spatial motor training tasks over 12 sessions (2-3 sessions per week). Subjects controlled a two-dimensional cursor with bilateral simultaneous movements of the shoulders using a non-invasive inertial measurement unit-based body-machine interface. Subjects' upper-body ability was evaluated before the start, in the middle and a day after the completion of training. MR imaging data were acquired before the start and within two days of the completion of training. Subjects learned to use upper-body movements that survived the injury to control the body-machine interface and improved their performance with practice. Motor training increased Manual Muscle Test scores and the isometric force of subjects' shoulders and upper arms. Moreover, motor training increased fractional anisotropy (FA) values in the cingulum of the left hemisphere by 6.02% on average, indicating localized white matter microstructure changes induced by activity-dependent modulation of axon diameter, myelin thickness or axon number. This body-machine interface may serve as a platform to develop a new generation of assistive-rehabilitative devices that promote the use of, and that re-strengthen, the motor and sensory functions that survived the injury.
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Affiliation(s)
- Ismael Seáñez-González
- Department of Biomedical Engineering, Northwestern University, Evanston, IL 60208, USA.
- Sensory Motor Performance Program, Rehabilitation Institute of Chicago, Chicago, IL 60611, USA.
| | - Camilla Pierella
- Sensory Motor Performance Program, Rehabilitation Institute of Chicago, Chicago, IL 60611, USA.
- Department of Physiology, Physical Medicine and Rehabilitation, Northwestern University, Evanston, IL 60208, USA.
- Department of Informatics, Bioengineering, Robotics, and Systems Engineering at the University of Genoa, 16145 Genoa, Italy.
| | - Ali Farshchiansadegh
- Department of Biomedical Engineering, Northwestern University, Evanston, IL 60208, USA.
- Sensory Motor Performance Program, Rehabilitation Institute of Chicago, Chicago, IL 60611, USA.
| | - Elias B Thorp
- Department of Biomedical Engineering, Northwestern University, Evanston, IL 60208, USA.
- Sensory Motor Performance Program, Rehabilitation Institute of Chicago, Chicago, IL 60611, USA.
| | - Xue Wang
- Department of Radiology, Northwestern University, Evanston, IL 60208, USA.
| | - Todd Parrish
- Department of Biomedical Engineering, Northwestern University, Evanston, IL 60208, USA.
- Department of Radiology, Northwestern University, Evanston, IL 60208, USA.
| | - Ferdinando A Mussa-Ivaldi
- Department of Biomedical Engineering, Northwestern University, Evanston, IL 60208, USA.
- Sensory Motor Performance Program, Rehabilitation Institute of Chicago, Chicago, IL 60611, USA.
- Department of Physiology, Physical Medicine and Rehabilitation, Northwestern University, Evanston, IL 60208, USA.
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Seanez-Gonzalez I, Pierella C, Farshchiansadegh A, Thorp EB, Abdollahi F, Pedersen JP, Sandro Mussa-Ivaldi FA. Static Versus Dynamic Decoding Algorithms in a Non-Invasive Body-Machine Interface. IEEE Trans Neural Syst Rehabil Eng 2016; 25:893-905. [PMID: 28092564 DOI: 10.1109/tnsre.2016.2640360] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
In this study, we consider a non-invasive body-machine interface that captures body motions still available to people with spinal cord injury (SCI) and maps them into a set of signals for controlling a computer user interface while engaging in a sustained level of mobility and exercise. We compare the effectiveness of two decoding algorithms that transform a high-dimensional body-signal vector into a lower dimensional control vector on six subjects with high-level SCI and eight controls. One algorithm is based on a static map from current body signals to the current value of the control vector set through principal component analysis (PCA), the other on dynamic mapping a segment of body signals to the value and the temporal derivatives of the control vector set through a Kalman filter. SCI and control participants performed straighter and smoother cursor movements with the Kalman algorithm during center-out reaching, but their movements were faster and more precise when using PCA. All participants were able to use the BMI's continuous, two-dimensional control to type on a virtual keyboard and play pong, and performance with both algorithms was comparable. However, seven of eight control participants preferred PCA as their method of virtual wheelchair control. The unsupervised PCA algorithm was easier to train and seemed sufficient to achieve a higher degree of learnability and perceived ease of use.
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Thorp EB, Abdollahi F, Chen D, Farshchiansadegh A, Lee MH, Pedersen JP, Pierella C, Roth EJ, Seanez Gonzalez I, Mussa-Ivaldi FA. Upper Body-Based Power Wheelchair Control Interface for Individuals With Tetraplegia. IEEE Trans Neural Syst Rehabil Eng 2015; 24:249-60. [PMID: 26054071 DOI: 10.1109/tnsre.2015.2439240] [Citation(s) in RCA: 41] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Many power wheelchair control interfaces are not sufficient for individuals with severely limited upper limb mobility. The majority of controllers that do not rely on coordinated arm and hand movements provide users a limited vocabulary of commands and often do not take advantage of the user's residual motion. We developed a body-machine interface (BMI) that leverages the flexibility and customizability of redundant control by using high dimensional changes in shoulder kinematics to generate proportional control commands for a power wheelchair. In this study, three individuals with cervical spinal cord injuries were able to control a power wheelchair safely and accurately using only small shoulder movements. With the BMI, participants were able to achieve their desired trajectories and, after five sessions driving, were able to achieve smoothness that was similar to the smoothness with their current joystick. All participants were twice as slow using the BMI however improved with practice. Importantly, users were able to generalize training controlling a computer to driving a power wheelchair, and employed similar strategies when controlling both devices. Overall, this work suggests that the BMI can be an effective wheelchair control interface for individuals with high-level spinal cord injuries who have limited arm and hand control.
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