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Felix Brown D, Quan Xie S. Effectiveness of Intelligent Control Strategies in Robot-Assisted Rehabilitation-A Systematic Review. IEEE Trans Neural Syst Rehabil Eng 2024; 32:1828-1840. [PMID: 38696295 DOI: 10.1109/tnsre.2024.3396065] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/04/2024]
Abstract
This review aims to provide a systematic analysis of the literature focused on the use of intelligent control systems in robotics for physical rehabilitation, identifying trends in recent research and comparing the effectiveness of intelligence used in control, with the aim of determining important factors in robot-assisted rehabilitation and how intelligent controller design can improve them. Seven electronic research databases were searched for articles published in the years 2015 - 2022 with articles selected based on relevance to the subject area of intelligent control systems in rehabilitation robotics. It was found that the most common use of intelligent algorithms for control is improving traditional control strategies with optimization and learning techniques. Intelligent algorithms are also commonly used in sensor output mapping, model construction, and for various data learning purposes. Experimental results show that intelligent controllers consistently outperform non-intelligent controllers in terms of transparency, tracking accuracy, and adaptability. Active participation of the patients and lowered interaction forces are consistently mentioned as important factors in improving the rehabilitation outcome as well as the patient experience. However, there are limited examples of studies presenting experimental results with impaired participants suffering limited range of motion, so the effectiveness of therapy provided by these systems is often difficult to quantify. A lack of universal evaluation criteria also makes it difficult to compare control systems outside of articles which use their own comparison criteria.
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2
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Berger DJ, d’Avella A. Myoelectric control and virtual reality to enhance motor rehabilitation after stroke. Front Bioeng Biotechnol 2024; 12:1376000. [PMID: 38665814 PMCID: PMC11043476 DOI: 10.3389/fbioe.2024.1376000] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2024] [Accepted: 03/28/2024] [Indexed: 04/28/2024] Open
Abstract
Effective upper-limb rehabilitation for severely impaired stroke survivors is still missing. Recent studies endorse novel motor rehabilitation approaches such as robotic exoskeletons and virtual reality systems to restore the function of the paretic limb of stroke survivors. However, the optimal way to promote the functional reorganization of the central nervous system after a stroke has yet to be uncovered. Electromyographic (EMG) signals have been employed for prosthetic control, but their application to rehabilitation has been limited. Here we propose a novel approach to promote the reorganization of pathological muscle activation patterns and enhance upper-limb motor recovery in stroke survivors by using an EMG-controlled interface to provide personalized assistance while performing movements in virtual reality (VR). We suggest that altering the visual feedback to improve motor performance in VR, thereby reducing the effect of deviations of the actual, dysfunctional muscle patterns from the functional ones, will actively engage patients in motor learning and facilitate the restoration of functional muscle patterns. An EMG-controlled VR interface may facilitate effective rehabilitation by targeting specific changes in the structure of muscle synergies and in their activations that emerged after a stroke-offering the possibility to provide rehabilitation therapies addressing specific individual impairments.
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Affiliation(s)
- Denise Jennifer Berger
- Laboratory of Neuromotor Physiology, IRCCS Fondazione Santa Lucia, Rome, Italy
- Department of Systems Medicine, Centre of Space Bio-medicine, University of Rome Tor Vergata, Rome, Italy
| | - Andrea d’Avella
- Laboratory of Neuromotor Physiology, IRCCS Fondazione Santa Lucia, Rome, Italy
- Department of Biology, University of Rome Tor Vergata, Rome, Italy
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3
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Wei Z, Zhang ZQ, Xie SQ. Continuous Motion Intention Prediction Using sEMG for Upper-Limb Rehabilitation: A Systematic Review of Model-Based and Model-Free Approaches. IEEE Trans Neural Syst Rehabil Eng 2024; 32:1487-1504. [PMID: 38557618 DOI: 10.1109/tnsre.2024.3383857] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
Upper limb functional impairments persisting after stroke significantly affect patients' quality of life. Precise adjustment of robotic assistance levels based on patients' motion intentions using sEMG signals is crucial for active rehabilitation. This paper systematically reviews studies on continuous prediction of upper limb single joints and multi-joint combinations motion intention using Model-Based (MB) and Model-Free (MF) approaches over the past decade, based on 186 relevant studies screened from six major electronic databases. The findings indicate ongoing challenges in terms of subject composition, algorithm robustness and generalization, and algorithm feasibility for practical applications. Moreover, it suggests integrating the strengths of both MB and MF approaches to improve existing algorithms. Therefore, future research should further explore personalized MB-MF combination methods incorporating deep learning, attention mechanisms, muscle synergy features, motor unit features, and closed-loop feedback to achieve precise, real-time, and long-duration prediction of multi-joint complex movements, while further refining the transfer learning strategy for rapid algorithm deployment across days and subjects. Overall, this review summarizes the current research status, significant findings, and challenges, aiming to inspire future research on predicting upper limb motion intentions based on sEMG.
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Dennler N, Cain A, De Guzman E, Chiu C, Winstein CJ, Nikolaidis S, Matarić MJ. A metric for characterizing the arm nonuse workspace in poststroke individuals using a robot arm. Sci Robot 2023; 8:eadf7723. [PMID: 37967205 DOI: 10.1126/scirobotics.adf7723] [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: 11/11/2022] [Accepted: 10/19/2023] [Indexed: 11/17/2023]
Abstract
An overreliance on the less-affected limb for functional tasks at the expense of the paretic limb and in spite of recovered capacity is an often-observed phenomenon in survivors of hemispheric stroke. The difference between capacity for use and actual spontaneous use is referred to as arm nonuse. Obtaining an ecologically valid evaluation of arm nonuse is challenging because it requires the observation of spontaneous arm choice for different tasks, which can easily be influenced by instructions, presumed expectations, and awareness that one is being tested. To better quantify arm nonuse, we developed the bimanual arm reaching test with a robot (BARTR) for quantitatively assessing arm nonuse in chronic stroke survivors. The BARTR is an instrument that uses a robot arm as a means of remote and unbiased data collection of nuanced spatial data for clinical evaluations of arm nonuse. This approach shows promise for determining the efficacy of interventions designed to reduce paretic arm nonuse and enhance functional recovery after stroke. We show that the BARTR satisfies the criteria of an appropriate metric for neurorehabilitative contexts: It is valid, reliable, and simple to use.
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Affiliation(s)
- Nathaniel Dennler
- Department of Computer Science, University of Southern California, Los Angeles, CA, USA
| | - Amelia Cain
- Department of Biokinesiology and Physical Therapy, University of Southern California, Los Angeles, CA, USA
| | - Erica De Guzman
- Department of Computer Science, University of Southern California, Los Angeles, CA, USA
| | - Claudia Chiu
- Department of Computer Science, University of Southern California, Los Angeles, CA, USA
| | - Carolee J Winstein
- Department of Biokinesiology and Physical Therapy, University of Southern California, Los Angeles, CA, USA
| | - Stefanos Nikolaidis
- Department of Computer Science, University of Southern California, Los Angeles, CA, USA
| | - Maja J Matarić
- Department of Computer Science, University of Southern California, Los Angeles, CA, USA
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Vidaurre C, Irastorza-Landa N, Sarasola-Sanz A, Insausti-Delgado A, Ray AM, Bibián C, Helmhold F, Mahmoud WJ, Ortego-Isasa I, López-Larraz E, Lozano Peiteado H, Ramos-Murguialday A. Challenges of neural interfaces for stroke motor rehabilitation. Front Hum Neurosci 2023; 17:1070404. [PMID: 37789905 PMCID: PMC10543821 DOI: 10.3389/fnhum.2023.1070404] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2022] [Accepted: 08/28/2023] [Indexed: 10/05/2023] Open
Abstract
More than 85% of stroke survivors suffer from different degrees of disability for the rest of their lives. They will require support that can vary from occasional to full time assistance. These conditions are also associated to an enormous economic impact for their families and health care systems. Current rehabilitation treatments have limited efficacy and their long-term effect is controversial. Here we review different challenges related to the design and development of neural interfaces for rehabilitative purposes. We analyze current bibliographic evidence of the effect of neuro-feedback in functional motor rehabilitation of stroke patients. We highlight the potential of these systems to reconnect brain and muscles. We also describe all aspects that should be taken into account to restore motor control. Our aim with this work is to help researchers designing interfaces that demonstrate and validate neuromodulation strategies to enforce a contingent and functional neural linkage between the central and the peripheral nervous system. We thus give clues to design systems that can improve or/and re-activate neuroplastic mechanisms and open a new recovery window for stroke patients.
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Affiliation(s)
- Carmen Vidaurre
- TECNALIA, Basque Research and Technology Alliance (BRTA), San Sebastian, Spain
- Ikerbasque Science Foundation, Bilbao, Spain
| | | | | | | | - Andreas M. Ray
- Institute for Medical Psychology and Behavioral Neurobiology, University of Tübingen, Tübingen, Germany
| | - Carlos Bibián
- Institute for Medical Psychology and Behavioral Neurobiology, University of Tübingen, Tübingen, Germany
| | - Florian Helmhold
- Institute for Medical Psychology and Behavioral Neurobiology, University of Tübingen, Tübingen, Germany
| | - Wala J. Mahmoud
- Institute for Medical Psychology and Behavioral Neurobiology, University of Tübingen, Tübingen, Germany
| | - Iñaki Ortego-Isasa
- TECNALIA, Basque Research and Technology Alliance (BRTA), San Sebastian, Spain
| | - Eduardo López-Larraz
- Institute for Medical Psychology and Behavioral Neurobiology, University of Tübingen, Tübingen, Germany
- Bitbrain, Zaragoza, Spain
| | | | - Ander Ramos-Murguialday
- TECNALIA, Basque Research and Technology Alliance (BRTA), San Sebastian, Spain
- Institute for Medical Psychology and Behavioral Neurobiology, University of Tübingen, Tübingen, Germany
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Bitikofer CK, Wolbrecht ET, Maura RM, Perry JC. Comparison of Admittance Control Dynamic Models for Transparent Free-Motion Human-Robot Interaction. IEEE Int Conf Rehabil Robot 2023; 2023:1-6. [PMID: 37941184 DOI: 10.1109/icorr58425.2023.10304709] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2023]
Abstract
This paper presents an experimental comparison of multiple admittance control dynamic models implemented on a five-degree-of-freedom arm exoskeleton. The performance of each model is evaluated for transparency, stability, and impact on point-to-point reaching. Although ideally admittance control would render a completely transparent environment for physical human-robot interaction (pHRI), in practice, there are trade-offs between transparency and stability-both of which can detrimentally impact natural arm movements. Here we test four admittance modes: 1) Low-Mass: low inertia with zero damping; 2) High-Mass: high inertia with zero damping; 3) Velocity-Damping: low inertia with damping; and 4) a novel Velocity-Error-Damping: low inertia with damping based on velocity error. A single subject completed two experiments: 1) 20 repetitions of a single reach-and-return, and 2) two repetitions of reach-and-return to 13 different targets. The results suggest that the proposed novel Velocity-Error-Damping model improves transparency the most, achieving a 70% average reduction of vibration power vs. Low-Mass, while also reducing user effort, with less impact on spatial/temporal accuracy than alternate modes. Results also indicate that different models have unique situational advantages so selecting between them may depend on the goals of the specific task (i.e., assessment, therapy, etc.). Future work should investigate merging approaches or transitioning between them in real-time.
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Cazenave L, Yurkewich A, Hohler C, Keller T, Krewer C, Jahn K, Hirche S, Endo S, Burdet E. Hybrid Functional Electrical Stimulation and Robotic Assistance for Wrist Motion Training After Stroke: Preliminary Results. IEEE Int Conf Rehabil Robot 2023; 2023:1-6. [PMID: 37941261 DOI: 10.1109/icorr58425.2023.10304736] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2023]
Abstract
This work presents preliminary results of a clinical study with sub-acute stroke patients using a hybrid system for wrist rehabilitation. The patients trained their wrist flexion/extension motion through a target tracking task, where electrical stimulation and robotic torque assisted them proportionally to their tracking error. Five sub-acute stroke patients have completed the training for 3 sessions on separate days. The preliminary results show hybrid assistance improves tracking performance and motion smoothness in most participants. In each session, patients' tracking performances before and after training were evaluated in unassisted tracking trials, without assistance. Their unassisted performance was compared across sessions and the results suggest that moderately to severely impaired patients might benefit more from hybrid training with our system than mildly impaired patients. Subjective assessments from all sessions show that the patients found the use of the device very comfortable and the training enjoyable. More data is being collected and future work will aim at verifying these trends.
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Widanage KND, Sheng Z, Chen HL, Li Y. Multi-Objective Optimization-Based Assist-as-Needed Controller for Improved Quality of Assistance in Rehabilitation Robotics. IEEE Int Conf Rehabil Robot 2023; 2023:1-6. [PMID: 37941209 DOI: 10.1109/icorr58425.2023.10304734] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2023]
Abstract
Assist-as-needed (AAN) is a paradigm in rehabilitation robotics based on the fact that more active participation from human users promotes faster recovery of motor functions. Moreover, the patients and public engaged and involved in our research design stressed that in order to provide safe and patient-friendly assistance, rehabilitation robotics should be equipped with different constraints while giving minimal assistance where required. Most of the current constraint-based AAN methods are only capable of providing position or velocity constraints which limit the quality of assistance that the robotic systems could provide. In this paper, we propose a multi-objective optimization (MOO) based controller which can implement both linear and non-linear constraints to improve the quality of assistance. This MOO-based proposed controller includes not only position and velocity constraints but also a vibration constraint to subside the tremors common in rehabilitation patients. The performance of this controller is compared with a Barrier Lyapunov Function (BLF) based controller with task-space constraints in a simulation. The results indicate that the MOO-based controller behaves similarly to the BLF-based controller in terms of position constraints. It also shows that the MOO-based controller can improve the quality of assistance by constraining the velocity and subsiding the simulated tremors.
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Pană CF, Popescu D, Rădulescu VM. Patent Review of Lower Limb Rehabilitation Robotic Systems by Sensors and Actuation Systems Used. SENSORS (BASEL, SWITZERLAND) 2023; 23:6237. [PMID: 37448084 PMCID: PMC10346545 DOI: 10.3390/s23136237] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/07/2023] [Revised: 06/24/2023] [Accepted: 06/27/2023] [Indexed: 07/15/2023]
Abstract
Robotic systems for lower limb rehabilitation are essential for improving patients' physical conditions in lower limb rehabilitation and assisting patients with various locomotor dysfunctions. These robotic systems mainly integrate sensors, actuation, and control systems and combine features from bionics, robotics, control, medicine, and other interdisciplinary fields. Several lower limb robotic systems have been proposed in the patent literature; some are commercially available. This review is an in-depth study of the patents related to robotic rehabilitation systems for lower limbs from the point of view of the sensors and actuation systems used. The patents awarded and published between 2013 and 2023 were investigated, and the temporal distribution of these patents is presented. Our results were obtained by examining the analyzed information from the three public patent databases. The patents were selected so that there were no duplicates after several filters were used in this review. For each patent database, the patents were analyzed according to the category of sensors and the number of sensors used. Additionally, for the main categories of sensors, an analysis was conducted depending on the type of sensors used. Afterwards, the actuation solutions for robotic rehabilitation systems for upper limbs described in the patents were analyzed, highlighting the main trends in their use. The results are presented with a schematic approach so that any user can easily find patents that use a specific type of sensor or a particular type of actuation system, and the sensors or actuation systems recommended to be used in some instances are highlighted.
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Affiliation(s)
- Cristina Floriana Pană
- Department of Mechatronics and Robotics, University of Craiova, 200440 Craiova, Romania;
| | - Dorin Popescu
- Department of Mechatronics and Robotics, University of Craiova, 200440 Craiova, Romania;
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Samuel OW, Asogbon MG, Kulwa F, Zangene AR, Oyemakinde TT, Igbe T, McEwan AA, Li Y, Li G. Enhanced Deep Transfer Learning Model based on Spatial-Temporal driven Scalograms for Precise Decoding of Motor Intent in Stroke Survivors. 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: 38083417 DOI: 10.1109/embc40787.2023.10340683] [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
Intelligent rehabilitation robotics (RR) have been proposed in recent years to aid post-stroke survivors recover their lost limb functions. However, a large proportion of these robotic systems operate in a passive mode that restricts users to predefined trajectories that rarely align with their intended limb movements, precluding full functional recovery. To address this issue, an efficient Transfer Learning based Convolutional Neural Network (TL-CNN) model is proposed to decode post-stroke patients' motion intentions toward realizing dexterously active robotic training during rehabilitation. For the first time, we use Spatial-Temporal Descriptor based Continuous Wavelet Transform (STD-CWT) as input to TL-CNN to optimally decode limb movement intent patterns. We evaluated the STD-CWT method on three distinct wavelets including the Morse, Amor, and Bump, and compared their decoding outcomes with those of the commonly adopted CWT technique under similar experimental conditions. We then validated the method using electromyogram signals of five stroke survivors who performed twenty-one distinct motor tasks. The results showed that the proposed technique recorded a significantly higher (p<0.05) decoding accuracy and faster convergence compared to the common method. Our method equally recorded obvious class separability for individual motor tasks across subjects. The findings suggest that the STD-CWT Scalograms have the potential for robust decoding of motor intention and could facilitate intuitive and active motor training in stroke RR.Clinical Relevance- The study demonstrated the potential of Spatial Temporal based Scalograms in aiding precise and robust decoding of multi-class motor tasks, upon which dexterously active rehabilitation robotic training for full motor function restoration could be realized.
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Hasson CJ, Manczurowsky J, Collins EC, Yarossi M. Neurorehabilitation robotics: how much control should therapists have? Front Hum Neurosci 2023; 17:1179418. [PMID: 37250692 PMCID: PMC10213717 DOI: 10.3389/fnhum.2023.1179418] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2023] [Accepted: 04/18/2023] [Indexed: 05/31/2023] Open
Abstract
Robotic technologies for rehabilitating motor impairments from neurological injuries have been the focus of intensive research and capital investment for more than 30 years. However, these devices have failed to convincingly demonstrate greater restoration of patient function compared to conventional therapy. Nevertheless, robots have value in reducing the manual effort required for physical therapists to provide high-intensity, high-dose interventions. In most robotic systems, therapists remain outside the control loop to act as high-level supervisors, selecting and initiating robot control algorithms to achieve a therapeutic goal. The low-level physical interactions between the robot and the patient are handled by adaptive algorithms that can provide progressive therapy. In this perspective, we examine the physical therapist's role in the control of rehabilitation robotics and whether embedding therapists in lower-level robot control loops could enhance rehabilitation outcomes. We discuss how the features of many automated robotic systems, which can provide repeatable patterns of physical interaction, may work against the goal of driving neuroplastic changes that promote retention and generalization of sensorimotor learning in patients. We highlight the benefits and limitations of letting therapists physically interact with patients through online control of robotic rehabilitation systems, and explore the concept of trust in human-robot interaction as it applies to patient-robot-therapist relationships. We conclude by highlighting several open questions to guide the future of therapist-in-the-loop rehabilitation robotics, including how much control to give therapists and possible approaches for having the robotic system learn from therapist-patient interactions.
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Affiliation(s)
- Christopher J. Hasson
- Department of Physical Therapy, Movement and Rehabilitation Sciences, Northeastern University, Boston, MA, United States
- Department of Bioengineering, Northeastern University, Boston, MA, United States
- Institute for Experiential Robotics, Northeastern University, Boston, MA, United States
| | - Julia Manczurowsky
- Department of Physical Therapy, Movement and Rehabilitation Sciences, Northeastern University, Boston, MA, United States
| | - Emily C. Collins
- Institute for Experiential Robotics, Northeastern University, Boston, MA, United States
| | - Mathew Yarossi
- Department of Physical Therapy, Movement and Rehabilitation Sciences, Northeastern University, Boston, MA, United States
- Institute for Experiential Robotics, Northeastern University, Boston, MA, United States
- Department of Electrical and Computer Engineering, Northeastern University, Boston, MA, United States
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12
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Dunkelberger N, Berning J, Schearer EM, O'Malley MK. Hybrid FES-exoskeleton control: Using MPC to distribute actuation for elbow and wrist movements. Front Neurorobot 2023; 17:1127783. [PMID: 37091069 PMCID: PMC10118008 DOI: 10.3389/fnbot.2023.1127783] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Accepted: 03/06/2023] [Indexed: 04/08/2023] Open
Abstract
IntroductionIndividuals who have suffered a cervical spinal cord injury prioritize the recovery of upper limb function for completing activities of daily living. Hybrid FES-exoskeleton systems have the potential to assist this population by providing a portable, powered, and wearable device; however, realization of this combination of technologies has been challenging. In particular, it has been difficult to show generalizability across motions, and to define optimal distribution of actuation, given the complex nature of the combined dynamic system.MethodsIn this paper, we present a hybrid controller using a model predictive control (MPC) formulation that combines the actuation of both an exoskeleton and an FES system. The MPC cost function is designed to distribute actuation on a single degree of freedom to favor FES control effort, reducing exoskeleton power consumption, while ensuring smooth movements along different trajectories. Our controller was tested with nine able-bodied participants using FES surface stimulation paired with an upper limb powered exoskeleton. The hybrid controller was compared to an exoskeleton alone controller, and we measured trajectory error and torque while moving the participant through two elbow flexion/extension trajectories, and separately through two wrist flexion/extension trajectories.ResultsThe MPC-based hybrid controller showed a reduction in sum of squared torques by an average of 48.7 and 57.9% on the elbow flexion/extension and wrist flexion/extension joints respectively, with only small differences in tracking accuracy compared to the exoskeleton alone.DiscussionTo realize practical implementation of hybrid FES-exoskeleton systems, the control strategy requires translation to multi-DOF movements, achieving more consistent improvement across participants, and balancing control to more fully leverage the muscles' capabilities.
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Affiliation(s)
- Nathan Dunkelberger
- Department of Mechanical Engineering, Mechatronics and Haptics Interfaces Laboratory, Rice University, Houston, TX, United States
| | - Jeffrey Berning
- Department of Mechanical Engineering, Mechatronics and Haptics Interfaces Laboratory, Rice University, Houston, TX, United States
| | - Eric M. Schearer
- Center for Human Machine Systems, Department of Mechanical Engineering, Cleveland State University, Cleveland, OH, United States
| | - Marcia K. O'Malley
- Department of Mechanical Engineering, Mechatronics and Haptics Interfaces Laboratory, Rice University, Houston, TX, United States
- *Correspondence: Marcia K. O'Malley
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Laszlo C, Munari D, Maggioni S, Knechtle D, Wolf P, De Bon D. Feasibility of an Intelligent Algorithm Based on an Assist-as-Needed Controller for a Robot-Aided Gait Trainer (Lokomat) in Neurological Disorders: A Longitudinal Pilot Study. Brain Sci 2023; 13:brainsci13040612. [PMID: 37190576 DOI: 10.3390/brainsci13040612] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2023] [Revised: 03/29/2023] [Accepted: 03/31/2023] [Indexed: 04/07/2023] Open
Abstract
Most robotic gait assisted devices are designed to provide constant assistance during the training without taking into account each patient’s functional ability. The Lokomat offers an assist-as-needed control via the integrated exercise “Adaptive Gait Support” (AGS), which adapts the robotic support based on the patient’s abilities. The aims of this study were to examine the feasibility and characteristics of the AGS during long-term application. Ten patients suffering from neurological diseases underwent an 8-week Lokomat training with the AGS. They additionally performed conventional walking tests and a robotic force measurement. The difference between robotic support during adaptive and conventional training and the relationship between the robotic assessment and the conventional walking and force tests were examined. The results show that AGS is feasible during long-term application in a heterogeneous population. The support during AGS training in most of the gait phases was significantly lower than during conventional Lokomat training. A relationship between the robotic support level determined by the AGS and conventional walking tests was revealed. Moreover, combining the isometric force data and AGS data could divide patients into clusters, based on their ability to generate high forces and their level of motor control. AGS shows a high potential in assessing patients’ walking ability, as well as in providing challenging training, e.g., by automatically adjusting the robotic support throughout the whole gait cycle and enabling training at lower robotic support.
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Affiliation(s)
- Caroline Laszlo
- Sensory-Motor Systems (SMS) Lab, ETH Zurich, 8006 Zurich, Switzerland
| | | | | | - Deborah Knechtle
- Revigo, Rehaklinik Zihlschlacht AG, 8604 Volketswil, Switzerland
| | - Peter Wolf
- Sensory-Motor Systems (SMS) Lab, ETH Zurich, 8006 Zurich, Switzerland
| | - Dino De Bon
- Revigo, Rehaklinik Zihlschlacht AG, 8604 Volketswil, Switzerland
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14
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Xu W, Guo Y, Bravo C, Ben-Tzvi P. Design, Control, and Experimental Evaluation of A Novel Robotic Glove System for Patients with Brachial Plexus Injuries. IEEE T ROBOT 2023; 39:1637-1652. [PMID: 37035529 PMCID: PMC10079272 DOI: 10.1109/tro.2022.3220973] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
This paper presents the development of an exoskeleton glove system for people who suffer from brachial plexus injuries, aiming to assist their lost grasping functionality. The robotic system consists of a portable glove system and an embedded controller. The glove system consists of Linear Series Elastic Actuators (LSEA), Rotary Series Elastic Actuators (RSEA), and optimized finger linkages to provide imitated human motion to each finger and a coupled motion of the hand. The design principles and optimization strategies were investigated to balance functionality, portability, and stability. The model-based force control strategy compensated with a backlash model and model-free force control strategy are presented and compared. Results show that our proposed model-free control method achieves the goal of accurate force control. Finally, experiments were conducted with the prototype of the developed integrated exoskeleton glove system. Results from 3 subjects with 150 trials show that our proposed exoskeleton glove system has the potential to be used as a rehabilitation device for patients.
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Affiliation(s)
- Wenda Xu
- Mechanical Engineering department in Virginia Tech
| | - Yunfei Guo
- Electrical and Computer Engineering department in Virginia Tech
| | - Cesar Bravo
- Carilion Clinic Institute of Orthopaedics and Neurosciences, Virginia Tech Carilion School of Medicine
| | - Pinhas Ben-Tzvi
- Electrical and Mechanical Engineering departments in Virginia Tech
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15
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Zheng J, Ma Q, He W, Huang Y, Shi P, Li S, Yu H. Cognitive and motor cortex activation during robot-assisted multi-sensory interactive motor rehabilitation training: An fNIRS based pilot study. Front Hum Neurosci 2023; 17:1089276. [PMID: 36845877 PMCID: PMC9947243 DOI: 10.3389/fnhum.2023.1089276] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2022] [Accepted: 01/23/2023] [Indexed: 02/11/2023] Open
Abstract
Objective This study aimed to evaluate the effects of multiple virtual reality (VR) interaction modalities based on force-haptic feedback combined with visual or auditory feedback in different ways on cerebral cortical activation by functional near-infrared spectroscopy (fNIRS). Methods: A modular multi-sensory VR interaction system based on a planar upper-limb rehabilitation robot was developed. Twenty healthy participants completed active elbow flexion and extension training in four VR interaction patterns, including haptic (H), haptic + auditory (HA), haptic + visual (HV), and haptic + visual + auditory (HVA). Cortical activation changes in the sensorimotor cortex (SMC), premotor cortex (PMC), and prefrontal cortex (PFC) were measured. Results Four interaction patterns all had significant activation effects on the motor and cognitive regions of the cerebral cortex (p < 0.05). Among them, in the HVA interaction mode, the cortical activation of each ROI was the strongest, followed by HV, HA, and H. The connectivity between channels of SMC and bilateral PFC, as well as the connectivity between channels in PMC, was the strongest under HVA and HV conditions. Besides, the two-way ANOVA of visual and auditory feedback showed that it was difficult for auditory feedback to have a strong impact on activation without visual feedback. In addition, under the condition of visual feedback, the effect of fusion auditory feedback on the activation degree was significantly higher than that of no auditory feedback. Conclusions The interaction mode of visual, auditory, and haptic multi-sensory integration is conducive to stronger cortical activation and cognitive control. Besides, there is an interaction effect between visual and auditory feedback, thus improving the cortical activation level. This research enriches the research on activation and connectivity of cognitive and motor cortex in the process of modular multi-sensory interaction training of rehabilitation robots. These conclusions provide a theoretical basis for the optimal design of the interaction mode of the rehabilitation robot and the possible scheme of clinical VR rehabilitation.
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Affiliation(s)
- Jinyu Zheng
- Institute of Rehabilitation Engineering and Technology, University of Shanghai for Science and Technology, Shanghai, China
| | - Qiqi Ma
- Institute of Rehabilitation Engineering and Technology, University of Shanghai for Science and Technology, Shanghai, China
| | - Wanying He
- Institute of Rehabilitation Engineering and Technology, University of Shanghai for Science and Technology, Shanghai, China
| | - Yanping Huang
- Institute of Rehabilitation Engineering and Technology, University of Shanghai for Science and Technology, Shanghai, China
| | - Ping Shi
- Institute of Rehabilitation Engineering and Technology, University of Shanghai for Science and Technology, Shanghai, China
- Shanghai Engineering Research Center of Assistive Devices, Shanghai, China
- Key Laboratory of Neural-Functional Information and Rehabilitation Engineering of the Ministry of Civil Affairs, Shanghai, China
| | - Sujiao Li
- Institute of Rehabilitation Engineering and Technology, University of Shanghai for Science and Technology, Shanghai, China
- Shanghai Engineering Research Center of Assistive Devices, Shanghai, China
- Key Laboratory of Neural-Functional Information and Rehabilitation Engineering of the Ministry of Civil Affairs, Shanghai, China
| | - Hongliu Yu
- Institute of Rehabilitation Engineering and Technology, University of Shanghai for Science and Technology, Shanghai, China
- Shanghai Engineering Research Center of Assistive Devices, Shanghai, China
- Key Laboratory of Neural-Functional Information and Rehabilitation Engineering of the Ministry of Civil Affairs, Shanghai, China
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16
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Grosmaire AG, Pila O, Breuckmann P, Duret C. Robot-assisted therapy for upper limb paresis after stroke: Use of robotic algorithms in advanced practice. NeuroRehabilitation 2022; 51:577-593. [PMID: 36530096 DOI: 10.3233/nre-220025] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
BACKGROUND Rehabilitation of stroke-related upper limb paresis is a major public health issue. OBJECTIVE Robotic systems have been developed to facilitate neurorehabilitation by providing key elements required to stimulate brain plasticity and motor recovery, namely repetitive, intensive, adaptative training with feedback. Although the positive effect of robot-assisted therapy on motor impairments has been well demonstrated, the effect on functional capacity is less certain. METHOD This narrative review outlines the principles of robot-assisted therapy for the rehabilitation of post-stroke upper limb paresis. RESULTS A paradigm is proposed to promote not only recovery of impairment but also function. CONCLUSION Further studies that would integrate some principles of the paradigm described in this paper are needed.
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Affiliation(s)
- Anne-Gaëlle Grosmaire
- Unité de Neurorééducation, Médecine Physique et de Réadaptation, Centre de Rééducation Fonctionnelle Les Trois Soleils, Boissise-Le-Roi, France
| | - Ophélie Pila
- Unité de Neurorééducation, Médecine Physique et de Réadaptation, Centre de Rééducation Fonctionnelle Les Trois Soleils, Boissise-Le-Roi, France
| | - Petra Breuckmann
- Unité de Neurorééducation, Médecine Physique et de Réadaptation, Centre de Rééducation Fonctionnelle Les Trois Soleils, Boissise-Le-Roi, France
| | - Christophe Duret
- Unité de Neurorééducation, Médecine Physique et de Réadaptation, Centre de Rééducation Fonctionnelle Les Trois Soleils, Boissise-Le-Roi, France
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17
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Anastasiev A, Kadone H, Marushima A, Watanabe H, Zaboronok A, Watanabe S, Matsumura A, Suzuki K, Matsumaru Y, Ishikawa E. Supervised Myoelectrical Hand Gesture Recognition in Post-Acute Stroke Patients with Upper Limb Paresis on Affected and Non-Affected Sides. SENSORS (BASEL, SWITZERLAND) 2022; 22:8733. [PMID: 36433330 PMCID: PMC9692557 DOI: 10.3390/s22228733] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/16/2022] [Revised: 10/31/2022] [Accepted: 11/02/2022] [Indexed: 06/16/2023]
Abstract
In clinical practice, acute post-stroke paresis of the extremities fundamentally complicates timely rehabilitation of motor functions; however, recently, residual and distorted musculoskeletal signals have been used to initiate feedback-driven solutions for establishing motor rehabilitation. Here, we investigate the possibilities of basic hand gesture recognition in acute stroke patients with hand paresis using a novel, acute stroke, four-component multidomain feature set (ASF-4) with feature vector weight additions (ASF-14NP, ASF-24P) and supervised learning algorithms trained only by surface electromyography (sEMG). A total of 19 (65.9 ± 12.4 years old; 12 men, seven women) acute stroke survivors (12.4 ± 6.3 days since onset) with hand paresis (Brunnstrom stage 4 ± 1/4 ± 1, SIAS 3 ± 1/3 ± 2, FMA-UE 40 ± 20) performed 10 repetitive hand movements reflecting basic activities of daily living (ADLs): rest, fist, pinch, wrist flexion, wrist extension, finger spread, and thumb up. Signals were recorded using an eight-channel, portable sEMG device with electrode placement on the forearms and thenar areas of both limbs (four sensors on each extremity). Using data preprocessing, semi-automatic segmentation, and a set of extracted feature vectors, support vector machine (SVM), linear discriminant analysis (LDA), and k-nearest neighbors (k-NN) classifiers for statistical comparison and validity (paired t-tests, p-value < 0.05), we were able to discriminate myoelectrical patterns for each gesture on both paretic and non-paretic sides. Despite any post-stroke conditions, the evaluated total accuracy rate by the 10-fold cross-validation using SVM among four-, five-, six-, and seven-gesture models were 96.62%, 94.20%, 94.45%, and 95.57% for non-paretic and 90.37%, 88.48%, 88.60%, and 89.75% for paretic limbs, respectively. LDA had competitive results using PCA whereas k-NN was a less efficient classifier in gesture prediction. Thus, we demonstrate partial efficacy of the combination of sEMG and supervised learning for upper-limb rehabilitation procedures for early acute stroke motor recovery and various treatment applications.
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Affiliation(s)
- Alexey Anastasiev
- Department of Neurosurgery, Graduate School of Comprehensive Human Sciences, University of Tsukuba, 1-1-1 Tennodai, Tsukuba 305-8575, Ibaraki, Japan
| | - Hideki Kadone
- Center for Cybernics Research, Faculty of Medicine, University of Tsukuba, 1-1-1 Tennodai, Tsukuba 305-8573, Ibaraki, Japan
| | - Aiki Marushima
- Department of Neurosurgery, Faculty of Medicine, University of Tsukuba, 1-1-1 Tennodai, Tsukuba 305-8575, Ibaraki, Japan
| | - Hiroki Watanabe
- Department of Neurosurgery, Faculty of Medicine, University of Tsukuba, 1-1-1 Tennodai, Tsukuba 305-8575, Ibaraki, Japan
| | - Alexander Zaboronok
- Department of Neurosurgery, Faculty of Medicine, University of Tsukuba, 1-1-1 Tennodai, Tsukuba 305-8575, Ibaraki, Japan
| | - Shinya Watanabe
- Department of Neurosurgery, Faculty of Medicine, University of Tsukuba, 1-1-1 Tennodai, Tsukuba 305-8575, Ibaraki, Japan
| | - Akira Matsumura
- Ibaraki Prefectural University of Health Sciences, 4669-2 Amicho, Inashiki 300-0394, Ibaraki, Japan
| | - Kenji Suzuki
- Center for Cybernics Research, Artificial Intelligence Laboratory, Faculty of Engineering Information and Systems, University of Tsukuba, 1-1-1 Tennodai, Tsukuba 305-8573, Ibaraki, Japan
| | - Yuji Matsumaru
- Department of Neurosurgery, Faculty of Medicine, University of Tsukuba, 1-1-1 Tennodai, Tsukuba 305-8575, Ibaraki, Japan
| | - Eiichi Ishikawa
- Department of Neurosurgery, Faculty of Medicine, University of Tsukuba, 1-1-1 Tennodai, Tsukuba 305-8575, Ibaraki, Japan
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18
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Yurkewich A, Ortega S, Sanchez J, Wang RH, Burdet E. Integrating hand exoskeletons into goal-oriented clinic and home stroke and spinal cord injury rehabilitation. J Rehabil Assist Technol Eng 2022; 9:20556683221130970. [PMID: 36212185 PMCID: PMC9535266 DOI: 10.1177/20556683221130970] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2022] [Accepted: 09/20/2022] [Indexed: 11/07/2022] Open
Abstract
Introduction Robotic exoskeletons are emerging as rehabilitation and assistive
technologies that simultaneously restore function and enable independence
for people with disabilities. Aim We investigated the feasibility and orthotic and restorative effects of an
exoskeleton-supported goal-directed rehabilitation program for people with
hand impairments after stroke or Spinal Cord Injury (SCI). Method A single-arm case-series feasibility study was conducted using a wearable
untethered hand exoskeleton during goal-directed therapy programs with
in-clinic and at-home components. Therapists trained stroke and SCI patients
to use a hand exoskeleton during rehabilitation exercises, activities of
daily living and patient-selected goals. Each patient received a 1-hour
in-clinic training session on five consecutive days, then took the
exoskeleton home for two consecutive days to perform therapist-recommended
tasks. Goal Attainment Scaling (GAS) and the Box and Block Test (BBT) were
administered at baseline, after in-clinic
therapy and after home use, with and again without wearing
the exoskeleton. The System Usability Scale (SUS), Motor Activity Log, and
Fugl-Meyer Assessment were also administered to assess the intervention’s
acceptability, adherence, usability and effectiveness. Results Four stroke patients (Chedoke McMaster Stage of Hand 2–4) and one SCI patient
(ASIA C8 Motor Stage 1) 23 ± 19 months post-injury wore the hand exoskeleton
to perform 280 ± 23 exercise repetitions in the clinic and additional
goal-oriented tasks at home. The patients performed their own goals and the
dexterity task with higher performance following the 7-days therapy program
in comparison to baseline for both exoskeleton-assisted (ΔGAS: 18 ± 10,
ΔBBT: 1 ± 5) and unassisted (ΔGAS: 14 ± 14, ΔBBT: 3 ± 4) assessments.
Therapists and patients provided ‘good’ SUS ratings of 78 ± 6 and no harmful
events were reported. Conclusions The exoskeleton-supported stroke and SCI therapy program with in-clinic and
at-home training components was feasible.
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Affiliation(s)
- Aaron Yurkewich
- Bioengineering Department, Imperial College of Science
Technology and Medicine, London UK,Aaron Yurkewich, MESc, Institute of
Biomaterials and Biomedical Engineering, University of Toronto, Room 407,
Rosebrugh Bldg, 164 College St, Toronto, ON M5S 3G9, Canada.
| | - Sara Ortega
- Occupational Therapy, Centro Europeo de
Neurociencias, Madrid, Spain
| | - José Sanchez
- Occupational Therapy, Centro Europeo de
Neurociencias, Madrid, Spain
| | - Rosalie H Wang
- Occupational Science and
Occupational Therapy, University of Toronto, Toronto, ON, Canada
| | - Etienne Burdet
- Bioengineering Department, Imperial College of Science
Technology and Medicine, London UK
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19
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Hu X, Song A, Wang J, Zeng H, Wei W. Finger Movement Recognition via High-Density Electromyography of Intrinsic and Extrinsic Hand Muscles. Sci Data 2022; 9:373. [PMID: 35768439 PMCID: PMC9243097 DOI: 10.1038/s41597-022-01484-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2021] [Accepted: 06/15/2022] [Indexed: 11/09/2022] Open
Abstract
Surface electromyography (sEMG) is commonly used to observe the motor neuronal activity within muscle fibers. However, decoding dexterous body movements from sEMG signals is still quite challenging. In this paper, we present a high-density sEMG (HD-sEMG) signal database that comprises simultaneously recorded sEMG signals of intrinsic and extrinsic hand muscles. Specifically, twenty able-bodied participants performed 12 finger movements under two paces and three arm postures. HD-sEMG signals were recorded with a 64-channel high-density grid placed on the back of hand and an 8-channel armband around the forearm. Also, a data-glove was used to record the finger joint angles. Synchronisation and reproducibility of the data collection from the HD-sEMG and glove sensors were ensured. The collected data samples were further employed for automated recognition of dexterous finger movements. The introduced dataset offers a new perspective to study the synergy between the intrinsic and extrinsic hand muscles during dynamic finger movements. As this dataset was collected from multiple participants, it also provides a resource for exploring generalized models for finger movement decoding.
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Affiliation(s)
- Xuhui Hu
- State Key Laboratory of Bioelectronics, Nanjing, China.,Jiangsu Key Laboratory of Remote Measurement and Control, Nanjing, China.,School of Instrument Science and Engineering, Southeast University, Nanjing, China
| | - Aiguo Song
- State Key Laboratory of Bioelectronics, Nanjing, China. .,Jiangsu Key Laboratory of Remote Measurement and Control, Nanjing, China. .,School of Instrument Science and Engineering, Southeast University, Nanjing, China.
| | - Jianzhi Wang
- State Key Laboratory of Bioelectronics, Nanjing, China.,Jiangsu Key Laboratory of Remote Measurement and Control, Nanjing, China.,School of Instrument Science and Engineering, Southeast University, Nanjing, China
| | - Hong Zeng
- State Key Laboratory of Bioelectronics, Nanjing, China.,Jiangsu Key Laboratory of Remote Measurement and Control, Nanjing, China.,School of Instrument Science and Engineering, Southeast University, Nanjing, China
| | - Wentao Wei
- School of Design Arts and Media, Nanjing University of Science and Technology, Nanjing, China
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20
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Morone G, Giansanti D. Comment on Anwer et al. Rehabilitation of Upper Limb Motor Impairment in Stroke: A Narrative Review on the Prevalence, Risk Factors, and Economic Statistics of Stroke and State of the Art Therapies. Healthcare 2022, 10, 190. Healthcare (Basel) 2022; 10:healthcare10050846. [PMID: 35627983 PMCID: PMC9141591 DOI: 10.3390/healthcare10050846] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2022] [Revised: 04/14/2022] [Accepted: 04/18/2022] [Indexed: 02/01/2023] Open
Abstract
We are writing to you as the corresponding author of the interesting review study entitled “Rehabilitation of Upper Limb Motor Impairment in Stroke: A Narrative Review on the Prevalence, Risk Factors, and Economic Statistics of Stroke and State of the Art Therapies” [...]
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Affiliation(s)
- Giovanni Morone
- Department of Life, Health and Environmental Sciences, University of L’Aquila, 67100 L’Aquila, Italy;
| | - Daniele Giansanti
- Centre Tisp, The Italian National Institute of Health, 00161 Rome, Italy
- Correspondence: ; Tel.: +39-06-49902701
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21
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Tiboni M, Borboni A, Vérité F, Bregoli C, Amici C. Sensors and Actuation Technologies in Exoskeletons: A Review. SENSORS (BASEL, SWITZERLAND) 2022; 22:884. [PMID: 35161629 PMCID: PMC8839165 DOI: 10.3390/s22030884] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/31/2021] [Revised: 01/16/2022] [Accepted: 01/19/2022] [Indexed: 02/06/2023]
Abstract
Exoskeletons are robots that closely interact with humans and that are increasingly used for different purposes, such as rehabilitation, assistance in the activities of daily living (ADLs), performance augmentation or as haptic devices. In the last few decades, the research activity on these robots has grown exponentially, and sensors and actuation technologies are two fundamental research themes for their development. In this review, an in-depth study of the works related to exoskeletons and specifically to these two main aspects is carried out. A preliminary phase investigates the temporal distribution of scientific publications to capture the interest in studying and developing novel ideas, methods or solutions for exoskeleton design, actuation and sensors. The distribution of the works is also analyzed with respect to the device purpose, body part to which the device is dedicated, operation mode and design methods. Subsequently, actuation and sensing solutions for the exoskeletons described by the studies in literature are analyzed in detail, highlighting the main trends in their development and spread. The results are presented with a schematic approach, and cross analyses among taxonomies are also proposed to emphasize emerging peculiarities.
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Affiliation(s)
- Monica Tiboni
- Department of Mechanical and Industrial Engineering, University of Brescia, Via Branze, 38, 25123 Brescia, Italy; (M.T.); (C.A.)
| | - Alberto Borboni
- Department of Mechanical and Industrial Engineering, University of Brescia, Via Branze, 38, 25123 Brescia, Italy; (M.T.); (C.A.)
| | - Fabien Vérité
- Agathe Group INSERM U 1150, UMR 7222 CNRS, ISIR (Institute of Intelligent Systems and Robotics), Sorbonne Université, 75005 Paris, France;
| | - Chiara Bregoli
- Institute of Condensed Matter Chemistry and Technologies for Energy (ICMATE), National Research Council (CNR), Via Previati 1/E, 23900 Lecco, Italy;
| | - Cinzia Amici
- Department of Mechanical and Industrial Engineering, University of Brescia, Via Branze, 38, 25123 Brescia, Italy; (M.T.); (C.A.)
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22
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Anwer S, Waris A, Gilani SO, Iqbal J, Shaikh N, Pujari AN, Niazi IK. Rehabilitation of Upper Limb Motor Impairment in Stroke: A Narrative Review on the Prevalence, Risk Factors, and Economic Statistics of Stroke and State of the Art Therapies. Healthcare (Basel) 2022; 10:healthcare10020190. [PMID: 35206805 PMCID: PMC8872602 DOI: 10.3390/healthcare10020190] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2021] [Revised: 01/05/2022] [Accepted: 01/13/2022] [Indexed: 02/04/2023] Open
Abstract
Stroke has been one of the leading causes of disability worldwide and is still a social health issue. Keeping in view the importance of physical rehabilitation of stroke patients, an analytical review has been compiled in which different therapies have been reviewed for their effectiveness, such as functional electric stimulation (FES), noninvasive brain stimulation (NIBS) including transcranial direct current stimulation (t-DCS) and transcranial magnetic stimulation (t-MS), invasive epidural cortical stimulation, virtual reality (VR) rehabilitation, task-oriented therapy, robot-assisted training, tele rehabilitation, and cerebral plasticity for the rehabilitation of upper extremity motor impairment. New therapeutic rehabilitation techniques are also being investigated, such as VR. This literature review mainly focuses on the randomized controlled studies, reviews, and statistical meta-analyses associated with motor rehabilitation after stroke. Moreover, with the increasing prevalence rate and the adverse socio-economic consequences of stroke, a statistical analysis covering its economic factors such as treatment, medication and post-stroke care services, and risk factors (modifiable and non-modifiable) have also been discussed. This review suggests that if the prevalence rate of the disease remains persistent, a considerable increase in the stroke population is expected by 2025, causing a substantial economic burden on society, as the survival rate of stroke is high compared to other diseases. Compared to all the other therapies, VR has now emerged as the modern approach towards rehabilitation motor activity of impaired limbs. A range of randomized controlled studies and experimental trials were reviewed to analyse the effectiveness of VR as a rehabilitative treatment with considerable satisfactory results. However, more clinical controlled trials are required to establish a strong evidence base for VR to be widely accepted as a preferred rehabilitation therapy for stroke.
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Affiliation(s)
- Saba Anwer
- School of Mechanical & Manufacturing Engineering, National University of Sciences and Technology (NUST), Islamabad 45200, Pakistan; (S.A.); (A.W.); (S.O.G.); (J.I.)
| | - Asim Waris
- School of Mechanical & Manufacturing Engineering, National University of Sciences and Technology (NUST), Islamabad 45200, Pakistan; (S.A.); (A.W.); (S.O.G.); (J.I.)
| | - Syed Omer Gilani
- School of Mechanical & Manufacturing Engineering, National University of Sciences and Technology (NUST), Islamabad 45200, Pakistan; (S.A.); (A.W.); (S.O.G.); (J.I.)
| | - Javaid Iqbal
- School of Mechanical & Manufacturing Engineering, National University of Sciences and Technology (NUST), Islamabad 45200, Pakistan; (S.A.); (A.W.); (S.O.G.); (J.I.)
| | - Nusratnaaz Shaikh
- Faculty of Health & Environmental Sciences, Health & Rehabilitation Research Institute, AUT University, Auckland 0627, New Zealand;
| | - Amit N. Pujari
- School of Physics, Engineering and Computer Science, University of Hertfordshire, Hatfield AL10 9AB, UK;
- School of Engineering, University of Aberdeen, Aberdeen AB24 3FX, UK
| | - Imran Khan Niazi
- Faculty of Health & Environmental Sciences, Health & Rehabilitation Research Institute, AUT University, Auckland 0627, New Zealand;
- Center of Chiropractic Research, New Zealand College of Chiropractic, Auckland 1060, New Zealand
- Center for Sensory-Motor Interaction, Department of Health Science & Technology, Aalborg University, 9000 Alborg, Denmark
- Correspondence:
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23
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Preliminary Evaluation of an Adaptive Robotic Training Program of the Wrist for Persons with Multiple Sclerosis. APPLIED SCIENCES-BASEL 2021. [DOI: 10.3390/app11199239] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/23/2023]
Abstract
Robotics can be used to describe wrist kinematics and assess sensorimotor impairments, while the implementation of training algorithms can be aimed at improving neuromuscular control. The purpose of this study was to use a robotic device to develop an adaptive and individualized training program of the distal upper extremity for individuals with multiple sclerosis (MS). This approach included an online assessment of performance aimed at changing the level of assistance/resistance provided during the task. Participants (N = 7) completed a robotic training program that occurred 3 times weekly for 4 weeks. The training protocol consisted of tracking a target moving along a figure by grasping the end-effector of the robotic device and moving it along the trajectory. Outcome measures were assessed pre- and post-intervention. Improvements in performance were quantified by average tracking (p = 0.028) and figural error (p = 0.028), which was significantly reduced by 26% and 43%, respectively. Isometric wrist strength significantly improved post-intervention (flexion: p = 0.043, radial and ulnar deviation: p = 0.028). The results of this work demonstrate that 4-weeks of adaptive robotic training is a feasible rehabilitative program that has the potential to improve distal upper extremity motor accuracy and muscular strength in a MS population.
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24
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Sharifi M, Mehr JK, Mushahwar VK, Tavakoli M. Adaptive CPG-Based Gait Planning With Learning-Based Torque Estimation and Control for Exoskeletons. IEEE Robot Autom Lett 2021. [DOI: 10.1109/lra.2021.3105996] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
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25
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Hoppe-Ludwig S, Armitage J, Turner KL, O'Brien MK, Mummidisetty CK, Koch LM, Kocherginsky M, Jayaraman A. Usability, functionality, and efficacy of a custom myoelectric elbow-wrist-hand orthosis to assist elbow function in individuals with stroke. J Rehabil Assist Technol Eng 2021; 8:20556683211035057. [PMID: 34471545 PMCID: PMC8404626 DOI: 10.1177/20556683211035057] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2019] [Accepted: 07/08/2021] [Indexed: 11/17/2022] Open
Abstract
Introduction After stroke, upper limb impairment affects independent performance of activities of daily living. We evaluated the usability, functionality, and efficacy of a myoelectric elbow-wrist-hand orthosis to provide support, limit unsafe motion, and enhance the functional motion of paralyzed or weak upper limbs. Methods Individuals with stroke participated in a single-session study to evaluate the device. Ability to activate the device was tested in supported and unsupported shoulder position, as well as the elbow range of motion, ability to maintain elbow position, and ability to lift and hold a range of weights while using the device. Results No adverse events were reported. 71% of users were able to operate the device in all three active myoelectric activation modes (Biceps, Triceps, Dual) during testing. Users were able to hold a range of wrist weights (0.5–2 lbs) for 10–120 seconds, with the largest percentage of participants able to hold weights with the device in Biceps Mode. Conclusions The myoelectric elbow-wrist-hand orthosis improved range of motion during use and was efficacious at remediating upper extremity impairment after stroke. All users could operate the device in at least one mode, and most could lift and hold weights representative of some everyday objects using the device.
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Affiliation(s)
- Shenan Hoppe-Ludwig
- Max Nader Center for Rehabilitation Technologies & Outcomes Research, Shirley Ryan AbilityLab, Chicago, USA.,Center for Bionic Medicine, Shirley Ryan AbilityLab, Chicago, USA
| | - Jodi Armitage
- Max Nader Center for Rehabilitation Technologies & Outcomes Research, Shirley Ryan AbilityLab, Chicago, USA.,Northwestern Memorial Hospital, Chicago, USA
| | - Kristi L Turner
- Center for Bionic Medicine, Shirley Ryan AbilityLab, Chicago, USA
| | - Megan K O'Brien
- Max Nader Center for Rehabilitation Technologies & Outcomes Research, Shirley Ryan AbilityLab, Chicago, USA.,Center for Bionic Medicine, Shirley Ryan AbilityLab, Chicago, USA.,Department of Physical Medicine & Rehabilitation, Northwestern University, Chicago, USA
| | - Chaithanya K Mummidisetty
- Max Nader Center for Rehabilitation Technologies & Outcomes Research, Shirley Ryan AbilityLab, Chicago, USA.,Center for Bionic Medicine, Shirley Ryan AbilityLab, Chicago, USA
| | - Lori McGee Koch
- Max Nader Center for Rehabilitation Technologies & Outcomes Research, Shirley Ryan AbilityLab, Chicago, USA.,Center for Bionic Medicine, Shirley Ryan AbilityLab, Chicago, USA
| | - Masha Kocherginsky
- Department of Preventive Medicine (Biostatistics), Northwestern University, Chicago, USA
| | - Arun Jayaraman
- Max Nader Center for Rehabilitation Technologies & Outcomes Research, Shirley Ryan AbilityLab, Chicago, USA.,Center for Bionic Medicine, Shirley Ryan AbilityLab, Chicago, USA.,Department of Physical Medicine & Rehabilitation, Northwestern University, Chicago, USA
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26
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Kumar A, Pirogova E, Mahmoud SS, Fang Q. Classification of error-related potentials evoked during stroke rehabilitation training. J Neural Eng 2021; 18. [PMID: 34384052 DOI: 10.1088/1741-2552/ac1d32] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2020] [Accepted: 08/12/2021] [Indexed: 01/22/2023]
Abstract
Objective.Error-related potentials (ErrPs) are elicited in the human brain following an error's perception. Recently, ErrPs have been observed in a novel task situation, i.e. when stroke patients perform upper-limb rehabilitation exercises. These ErrPs can be used to developassist-as-needed(AAN) robotic stroke rehabilitation systems. However, to date, there is no reported research on assessing the feasibility of using the ErrPs to implement the AAN approach. Hence, in this study, we evaluated and compared the single-trial classification of novel ErrPs using various classical machine learning and deep learning approaches.Approach.Electroencephalogram data of 13 stroke patients recorded while performing an upper-limb physical rehabilitation exercise were used. Two classification approaches, one combining the xDAWN spatial filtering and support vector machines, and the other using a convolutional neural network-based double transfer learning, were utilized.Main results.Results showed that the ErrPs could be detected with a mean area under the receiver operating characteristics curve of 0.838, and a mean accuracy of 0.842, 0.257 above the chance level (p< 0.05), for a within-subject classification. The results indicated the feasibility of using ErrP signals in real-time AAN robot therapy with evidence from the conducted latency analysis, cross-subject classification, and three-class asynchronous classification.Significance.The findings presented support our proposed approach of using ErrPs as a measure to trigger and/or modulate as required the robotic assistance in a real-timehuman-in-the-looprobotic stroke rehabilitation system.
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Affiliation(s)
- Akshay Kumar
- Department of Biomedical Engineering, College of Engineering, Shantou University, Guangdong, People's Republic of China
| | - Elena Pirogova
- School of Engineering, Royal Melbourne Institute of Technology University, Melbourne, Australia
| | - Seedahmed S Mahmoud
- Department of Biomedical Engineering, College of Engineering, Shantou University, Guangdong, People's Republic of China
| | - Qiang Fang
- Department of Biomedical Engineering, College of Engineering, Shantou University, Guangdong, People's Republic of China
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Myoelectric control and neuromusculoskeletal modeling: Complementary technologies for rehabilitation robotics. CURRENT OPINION IN BIOMEDICAL ENGINEERING 2021. [DOI: 10.1016/j.cobme.2021.100313] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
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He C, Xiong CH, Chen ZJ, Fan W, Huang XL, Fu C. Preliminary Assessment of a Postural Synergy-Based Exoskeleton for Post-Stroke Upper Limb Rehabilitation. IEEE Trans Neural Syst Rehabil Eng 2021; 29:1795-1805. [PMID: 34428146 DOI: 10.1109/tnsre.2021.3107376] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Upper limb exoskeletons have drawn significant attention in neurorehabilitation because of the anthropomorphic mechanical structure analogous to human anatomy. Whereas, the training movements are typically unorganized because most exoskeletons ignore the natural movement characteristic of human upper limbs, particularly inter-joint postural synergy. This paper introduces a newly developed exoskeleton (Armule) for upper limb rehabilitation with a postural synergy design concept, which can reproduce activities of daily living (ADL) motion with the characteristics of human natural movements. The semitransparent active control strategy with the interactive force guidance and visual feedback ensured the active participation of users. Eight participants with hemiplegia due to a first-ever, unilateral stroke were recruited and included. They participated in exoskeleton therapy sessions for 4 weeks, with passive/active training under trajectories and postures with the characteristics of human natural movements. The primary outcome was the Fugl-Meyer Assessment for Upper Extremities (FMA-UE). The secondary outcomes were the Action Research Arm Test(ARAT), modified Barthel Index (mBI), and metric measured with the exoskeleton After the 4-weeks intervention, all subjects showed significant improvements in the following clinical measures: the FMA-UE (difference, 11.50 points, p = 0.002), the ARAT (difference, 7.75 points ), and the mBI (difference, 17.50 points, p = 0.003 ) score. Besides, all subjects showed significant improvements in kinematic and interaction force metrics measured with the exoskeleton. These preliminary results demonstrate that the Armule exoskeleton could improve individuals' motor control and ADL function after stroke, which might be associated with kinematic and interaction force optimization and postural synergy modification during functional tasks.
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Zhang Z, Prilutsky BI, Butler AJ, Shinohara M, Ghovanloo M. Design and Preliminary Evaluation of a Tongue-Operated Exoskeleton System for Upper Limb Rehabilitation. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:8708. [PMID: 34444456 PMCID: PMC8393282 DOI: 10.3390/ijerph18168708] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/08/2021] [Revised: 08/10/2021] [Accepted: 08/12/2021] [Indexed: 01/17/2023]
Abstract
Stroke is a devastating condition that may cause upper limb paralysis. Robotic rehabilitation with self-initiated and assisted movements is a promising technology that could help restore upper limb function. Previous studies have established that the tongue motion can be used to communicate human intent and control a rehabilitation robot/assistive device. The goal of this study was to evaluate a tongue-operated exoskeleton system (TDS-KA), which we have developed for upper limb rehabilitation. We adopted a tongue-operated assistive technology, called the tongue drive system (TDS), and interfaced it with the exoskeleton KINARM. We also developed arm reaching and tracking tasks, controlled by different tongue operation modes, for training and evaluation of arm motor function. Arm reaching and tracking tasks were tested in 10 healthy participants (seven males and three females, 23-60 years) and two female stroke survivors with upper extremity impairment (32 and 58 years). All healthy and two stroke participants successfully performed the tasks. One stroke subject demonstrated a clinically significant improvement in Fugl-Meyer upper extremity score after practicing the tasks in six 3-h sessions. We conclude that the TDS-KA system can accurately translate tongue commands to exoskeleton arm movements, quantify the function of the arm, and perform rehabilitation training.
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Affiliation(s)
- Zhenxuan Zhang
- School of Electrical & Computer Engineering, Georgia Institute of Technology, Atlanta, GA 30308, USA;
| | - Boris I. Prilutsky
- School of Biological Sciences, Georgia Institute of Technology, Atlanta, GA 30332, USA;
| | - Andrew J. Butler
- School of Health Professions, The University of Alabama at Birmingham, Birmingham, AL 35294, USA;
| | - Minoru Shinohara
- School of Biological Sciences, Georgia Institute of Technology, Atlanta, GA 30332, USA;
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Rong J, Ding L, Xiong L, Zhang W, Wang W, Deng M, Wang Y, Chen Z, Jia J. Mirror Visual Feedback Prior to Robot-Assisted Training Facilitates Rehabilitation After Stroke: A Randomized Controlled Study. Front Neurol 2021; 12:683703. [PMID: 34305792 PMCID: PMC8297738 DOI: 10.3389/fneur.2021.683703] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2021] [Accepted: 06/07/2021] [Indexed: 11/13/2022] Open
Abstract
Purpose: Robot-assisted training has been widely used in neurorehabilitation, but its effect on facilitating recovery after stroke remains controversial. One possible reason might be lacking consideration of the role of embodiment in robotic systems. Mirror visual feedback is an ideal method to approach embodiment. Thus, we hypothesized that mirror visual feedback priming with subsequent robot-assisted training might provide additional treatment benefits in rehabilitation. Method: This is a prospective, assessor-blinded, randomized, controlled study. Forty subacute stroke patients were randomly assigned into an experimental group (N = 20) or a control group (N = 20). They received either mirror visual feedback or sham-mirror visual feedback prior to robot-assisted training for 1.5 h/day, 5 days/week for 4 weeks. Before and after intervention, the Fugl-Meyer Assessment Upper Limb subscale, the Functional Independence Measure, the modified Barthel Index, and grip strength were measured. Scores of four specified games were recorded pre and post one-time mirror visual feedback priming before intervention in the experimental group. Results: All measurements improved significantly in both groups following interventions. Moreover, the Fugl-Meyer Assessment Upper Limb subscale, self-care subscale of the Functional Independence Measure, and the grip strength were improved significantly in the experimental group after a 4-week intervention, compared with the control group. Significantly higher scores of two games were revealed after one-time priming. Conclusions: Mirror visual feedback prior to robot-assisted training could prompt motor recovery, increase ability of self-care, and potentially enhance grip strength in stroke patients, compared to control treatment. Moreover, mirror visual feedback priming might have the capability to improve the patient's performance and engagement during robot-assisted training, which could prompt the design and development of robotic systems. Clinical Trial Registration: www.ClinicalTrials.gov, identifier: ChiCTR1900023356.
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Affiliation(s)
- Jifeng Rong
- The Center of Rehabilitation Therapy, The First Rehabilitation Hospital of Shanghai, Shanghai, China
| | - Li Ding
- The Department of Rehabilitation Medicine, Huashan Hospital, Fudan University, Shanghai, China
| | - Li Xiong
- The Center of Rehabilitation Therapy, The First Rehabilitation Hospital of Shanghai, Shanghai, China
| | - Wen Zhang
- The Center of Rehabilitation Therapy, The First Rehabilitation Hospital of Shanghai, Shanghai, China
| | - Weining Wang
- The Center of Rehabilitation Therapy, The First Rehabilitation Hospital of Shanghai, Shanghai, China
| | - Meikui Deng
- The Center of Rehabilitation Therapy, The First Rehabilitation Hospital of Shanghai, Shanghai, China
| | - Yana Wang
- The Center of Rehabilitation Therapy, The First Rehabilitation Hospital of Shanghai, Shanghai, China
| | - Zhen Chen
- The Neurorehabilitation Centre, The First Rehabilitation Hospital of Shanghai, Shanghai, China
| | - Jie Jia
- The Department of Rehabilitation Medicine, Huashan Hospital, Fudan University, Shanghai, China.,The National Clinical Research Center for Aging and Medicine, Huashan Hospital, Fudan University, Shanghai, China
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Manjunatha H, Pareek S, Jujjavarapu SS, Ghobadi M, Kesavadas T, Esfahani ET. Upper Limb Home-Based Robotic Rehabilitation During COVID-19 Outbreak. Front Robot AI 2021; 8:612834. [PMID: 34109220 PMCID: PMC8181124 DOI: 10.3389/frobt.2021.612834] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2020] [Accepted: 03/03/2021] [Indexed: 12/23/2022] Open
Abstract
The coronavirus disease (COVID-19) outbreak requires rapid reshaping of rehabilitation services to include patients recovering from severe COVID-19 with post-intensive care syndromes, which results in physical deconditioning and cognitive impairments, patients with comorbid conditions, and other patients requiring physical therapy during the outbreak with no or limited access to hospital and rehabilitation centers. Considering the access barriers to quality rehabilitation settings and services imposed by social distancing and stay-at-home orders, these patients can be benefited from providing access to affordable and good quality care through home-based rehabilitation. The success of such treatment will depend highly on the intensity of the therapy and effort invested by the patient. Monitoring patients' compliance and designing a home-based rehabilitation that can mentally engage them are the critical elements in home-based therapy's success. Hence, we study the state-of-the-art telerehabilitation frameworks and robotic devices, and comment about a hybrid model that can use existing telerehabilitation framework and home-based robotic devices for treatment and simultaneously assess patient's progress remotely. Second, we comment on the patients' social support and engagement, which is critical for the success of telerehabilitation service. As the therapists are not physically present to guide the patients, we also discuss the adaptability requirement of home-based telerehabilitation. Finally, we suggest that the reformed rehabilitation services should consider both home-based solutions for enhancing the activities of daily living and an on-demand ambulatory rehabilitation unit for extensive training where we can monitor both cognitive and motor performance of the patients remotely.
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Affiliation(s)
- Hemanth Manjunatha
- Human in the Loop Systems Laboratory, Department of Mechanical and Aerospace Engineering, University at Buffalo, Buffalo, NY, United States
| | - Shrey Pareek
- Health Care Engineering Systems Center, University of Illinois Urbana-Champaign, Champaign, IL, United States
| | - Sri Sadhan Jujjavarapu
- Human in the Loop Systems Laboratory, Department of Mechanical and Aerospace Engineering, University at Buffalo, Buffalo, NY, United States
| | - Mostafa Ghobadi
- Human in the Loop Systems Laboratory, Department of Mechanical and Aerospace Engineering, University at Buffalo, Buffalo, NY, United States
| | - Thenkurussi Kesavadas
- Health Care Engineering Systems Center, University of Illinois Urbana-Champaign, Champaign, IL, United States
| | - Ehsan T Esfahani
- Human in the Loop Systems Laboratory, Department of Mechanical and Aerospace Engineering, University at Buffalo, Buffalo, NY, United States
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陈 盛, 严 以, 徐 国, 高 翔, 黄 康, 邰 春. [Mirror-type rehabilitation training with dynamic adjustment and assistance for shoulder joint]. SHENG WU YI XUE GONG CHENG XUE ZA ZHI = JOURNAL OF BIOMEDICAL ENGINEERING = SHENGWU YIXUE GONGCHENGXUE ZAZHI 2021; 38:351-360. [PMID: 33913296 PMCID: PMC9927699 DOI: 10.7507/1001-5515.202001053] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Received: 01/21/2020] [Revised: 11/04/2020] [Indexed: 11/03/2022]
Abstract
The real physical image of the affected limb, which is difficult to move in the traditional mirror training, can be realized easily by the rehabilitation robots. During this training, the affected limb is often in a passive state. However, with the gradual recovery of the movement ability, active mirror training becomes a better choice. Consequently, this paper took the self-developed shoulder joint rehabilitation robot with an adjustable structure as an experimental platform, and proposed a mirror training system completed by next four parts. First, the motion trajectory of the healthy limb was obtained by the Inertial Measurement Units (IMU). Then the variable universe fuzzy adaptive proportion differentiation (PD) control was adopted for inner loop, meanwhile, the muscle strength of the affected limb was estimated by the surface electromyography (sEMG). The compensation force for an assisted limb of outer loop was calculated. According to the experimental results, the control system can provide real-time assistance compensation according to the recovery of the affected limb, fully exert the training initiative of the affected limb, and make the affected limb achieve better rehabilitation training effect.
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Affiliation(s)
- 盛 陈
- 南京邮电大学 机器人信息感知与控制研究所(南京 210023)Institute of Robot Information Perception and Control, Nanjing University of Posts and Telecommunications, Nanjing 210023, P.R.China
- 南京航空航天大学 精密与微细制造技术省重点实验室(南京 210016)Provincial Key Laboratory of Precision and Micro Manufacturing Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, P.R.China
| | - 以哲 严
- 南京邮电大学 机器人信息感知与控制研究所(南京 210023)Institute of Robot Information Perception and Control, Nanjing University of Posts and Telecommunications, Nanjing 210023, P.R.China
| | - 国政 徐
- 南京邮电大学 机器人信息感知与控制研究所(南京 210023)Institute of Robot Information Perception and Control, Nanjing University of Posts and Telecommunications, Nanjing 210023, P.R.China
| | - 翔 高
- 南京邮电大学 机器人信息感知与控制研究所(南京 210023)Institute of Robot Information Perception and Control, Nanjing University of Posts and Telecommunications, Nanjing 210023, P.R.China
| | - 康金 黄
- 南京邮电大学 机器人信息感知与控制研究所(南京 210023)Institute of Robot Information Perception and Control, Nanjing University of Posts and Telecommunications, Nanjing 210023, P.R.China
| | - 春 邰
- 南京邮电大学 机器人信息感知与控制研究所(南京 210023)Institute of Robot Information Perception and Control, Nanjing University of Posts and Telecommunications, Nanjing 210023, P.R.China
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Lee T, Kim I, Lee SH. Estimation of the Continuous Walking Angle of Knee and Ankle (Talocrural Joint, Subtalar Joint) of a Lower-Limb Exoskeleton Robot Using a Neural Network. SENSORS 2021; 21:s21082807. [PMID: 33923587 PMCID: PMC8072591 DOI: 10.3390/s21082807] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/17/2021] [Revised: 04/09/2021] [Accepted: 04/15/2021] [Indexed: 11/16/2022]
Abstract
A lower-limb exoskeleton robot identifies the wearer's walking intention and assists the walking movement through mechanical force; thus, it is important to be able to identify the wearer's movement in real-time. Measurement of the angle of the knee and ankle can be difficult in the case of patients who cannot move the lower-limb joint properly. Therefore, in this study, the knee angle as well as the angles of the talocrural and subtalar joints of the ankle were estimated during walking by applying the neural network to two inertial measurement unit (IMU) sensors attached to the thigh and shank. First, for angle estimation, the gyroscope and accelerometer data of the IMU sensor were obtained while walking at a treadmill speed of 1 to 2.5 km/h while wearing an exoskeleton robot. The weights according to each walking speed were calculated using a neural network algorithm programmed in MATLAB software. Second, an appropriate weight was selected according to the walking speed through the IMU data, and the knee angle and the angles of the talocrural and subtalar joints of the ankle were estimated in real-time during walking through a feedforward neural network using the IMU data received in real-time. We confirmed that the angle estimation error was accurately estimated as 1.69° ± 1.43 (mean absolute error (MAE) ± standard deviation (SD)) for the knee joint, 1.29° ± 1.01 for the talocrural joint, and 0.82° ± 0.69 for the subtalar joint. Therefore, the proposed algorithm has potential for gait rehabilitation as it addresses the difficulty of estimating angles of lower extremity patients using torque and EMG sensors.
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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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Arendsen LJ, Guggenberger R, Zimmer M, Weigl T, Gharabaghi A. Peripheral Electrical Stimulation Modulates Cortical Beta-Band Activity. Front Neurosci 2021; 15:632234. [PMID: 33867919 PMCID: PMC8044771 DOI: 10.3389/fnins.2021.632234] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2020] [Accepted: 03/08/2021] [Indexed: 11/24/2022] Open
Abstract
Low-frequency peripheral electrical stimulation using a matrix electrode (PEMS) modulates spinal nociceptive pathways. However, the effects of this intervention on cortical oscillatory activity have not been assessed yet. The aim of this study was to investigate the effects of low-frequency PEMS (4 Hz) on cortical oscillatory activity in different brain states in healthy pain-free participants. In experiment 1, PEMS was compared to sham stimulation. In experiment 2, motor imagery (MI) was used to modulate the sensorimotor brain state. PEMS was applied either during MI-induced oscillatory desynchronization (concurrent PEMS) or after MI (delayed PEMS) in a cross-over design. For both experiments, PEMS was applied on the left forearm and resting-state electroencephalography (EEG) was recording before and after each stimulation condition. Experiment 1 showed a significant decrease of global resting-state beta power after PEMS compared to sham (p = 0.016), with a median change from baseline of −16% for PEMS and −0.54% for sham. A cluster-based permutation test showed a significant difference in resting-state beta power comparing pre- and post-PEMS (p = 0.018) that was most pronounced over bilateral central and left frontal sensors. Experiment 2 did not identify a significant difference in the change from baseline of global EEG power for concurrent PEMS compared to delayed PEMS. Two cluster-based permutation tests suggested that frontal beta power may be increased following both concurrent and delayed PEMS. This study provides novel evidence for supraspinal effects of low-frequency PEMS and an initial indication that the presence of a cognitive task such as MI may influence the effects of PEMS on beta activity. Chronic pain has been associated with changes in beta activity, in particular an increase of beta power in frontal regions. Thus, brain state-dependent PEMS may offer a novel approach to the treatment of chronic pain. However, further studies are warranted to investigate optimal stimulation conditions to achieve a reduction of pain.
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Affiliation(s)
- Laura J Arendsen
- Institute for Neuromodulation and Neurotechnology, University of Tübingen, Tübingen, Germany
| | - Robert Guggenberger
- Institute for Neuromodulation and Neurotechnology, University of Tübingen, Tübingen, Germany
| | - Manuela Zimmer
- Institute for Neuromodulation and Neurotechnology, University of Tübingen, Tübingen, Germany
| | - Tobias Weigl
- Department of Anesthesiology and Intensive Care Medicine, University Hospital Bonn, Bonn, Germany
| | - Alireza Gharabaghi
- Institute for Neuromodulation and Neurotechnology, University of Tübingen, Tübingen, Germany
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Lee HC, Kuo FL, Lin YN, Liou TH, Lin JC, Huang SW. Effects of Robot-Assisted Rehabilitation on Hand Function of People With Stroke: A Randomized, Crossover-Controlled, Assessor-Blinded Study. Am J Occup Ther 2021; 75:7501205020p1-7501205020p11. [PMID: 33399050 DOI: 10.5014/ajot.2021.038232] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
IMPORTANCE The effects of robot-assisted task-oriented training with tangible objects among patients with stroke remain unknown. OBJECTIVE To investigate the effects of robot-assisted therapy (RT) with a Gloreha device on sensorimotor and hand function and ability to perform activities of daily living (ADLs) among patients with stroke. DESIGN Randomized, crossover-controlled, assessor-blinded study. SETTING Rehabilitation clinic. PARTICIPANTS Patients (N = 24) with moderate motor and sensory deficits. INTERVENTION Patients participated in 12 RT sessions and 12 conventional therapy (CT) sessions, with order counterbalanced, for 6 wk, with a 1-mo washout period. OUTCOMES AND MEASURES Performance was assessed four times: before and after RT and before and after CT. Outcomes were measured using the Fugl-Meyer Assessment-Upper Extremity (FMA-UE), Box and Block Test, electromyography of the extensor digitorum communis (EDC) and brachioradialis, and a grip dynamometer for motor function; Semmes-Weinstein hand monofilament and the Revised Nottingham Sensory Assessment for sensory function; and the Modified Barthel Index (MBI) for ADL ability. RESULTS RT resulted in significantly improved FMA-UE proximal (p = .038) and total (p = .046) and MBI (p = .030) scores. Participants' EDC muscles exhibited higher efficacy during the small-block grasping task of the Box and Block Test after RT than after CT (p = .050). CONCLUSIONS AND RELEVANCE RT with the Gloreha device can facilitate whole-limb function, leading to beneficial effects on arm motor function, EDC muscle recruitment efficacy, and ADL ability for people with subacute and chronic stroke. WHAT THIS ARTICLE ADDS The evidence suggests that a task-oriented approach combined with the Gloreha device can facilitate engagement in whole-limb active movement and efficiently promote functional recovery.
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Affiliation(s)
- Hsin-Chieh Lee
- Hsin-Chieh Lee, MS, is Occupational Therapist, Department of Physical Medicine and Rehabilitation, Shuang Ho Hospital, Taipei Medical University, Taipei, Taiwan
| | - Fen-Ling Kuo
- Fen-Ling Kuo, MS, is Occupational Therapist, Department of Physical Medicine and Rehabilitation, Shuang Ho Hospital, Taipei Medical University, Taipei, Taiwan
| | - Yen-Nung Lin
- Yen-Nung Lin, MD, MS, is Physiatrist, Department of Physical Medicine and Rehabilitation, Wan Fang Hospital, and Graduate Institute of Injury Prevention and Control, Taipei Medical University, Taipei City, Taiwan
| | - Tsan-Hon Liou
- Tsan-Hon Liou, MD, PhD, is Physiatrist, Department of Physical Medicine and Rehabilitation, Shuang Ho Hospital, and Department of Physical Medicine and Rehabilitation, School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan
| | - Jui-Chi Lin
- Jui-Chi Lin, MS, is Occupational Therapist, Department of Physical Medicine and Rehabilitation, Shuang Ho Hospital, Taipei Medical University, Taipei, Taiwan;
| | - Shih-Wei Huang
- Shih-Wei Huang, MD, is Physiatrist, Department of Physical Medicine and Rehabilitation, Shuang Ho Hospital, and Department of Physical Medicine and Rehabilitation, School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan
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Design of a 2DoF Ankle Exoskeleton with a Polycentric Structure and a Bi-Directional Tendon-Driven Actuator Controlled Using a PID Neural Network. ACTUATORS 2021. [DOI: 10.3390/act10010009] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Abstract
Lower limb exoskeleton robots help with walking movements through mechanical force, by identifying the wearer’s walking intention. When the exoskeleton robot is lightweight and comfortable to wear, the stability of walking increases, and energy can be used efficiently. However, because it is difficult to implement the complex anatomical movements of the human body, most are designed simply. Due to this, misalignment between the human and robot movement causes the wearer to feel uncomfortable, and the stability of walking is reduced. In this paper, we developed a two degrees of freedom (2DoF) ankle exoskeleton robot with a subtalar joint and a talocrural joint, applying a four-bar linkage to realize the anatomical movement of a simple 1DoF structure mainly used for ankles. However, bidirectional tendon-driven actuators (BTDAs) do not consider the difference in a length change of both cables due to dorsiflexion (DF) and plantar flexion (PF) during walking, causing misalignment. To solve this problem, a BTDA was developed by considering the length change of both cables. Cable-driven actuators and exoskeleton robot systems create uncertainty. Accordingly, adaptive control was performed with a proportional-integral-differential neural network (PIDNN) controller to minimize system uncertainty.
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Samuel OW, Asogbon MG, Geng Y, Jiang N, Mzurikwao D, Zheng Y, Wong KKL, Vollero L, Li G. Decoding movement intent patterns based on spatiotemporal and adaptive filtering method towards active motor training in stroke rehabilitation systems. Neural Comput Appl 2021. [DOI: 10.1007/s00521-020-05536-9] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
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Naik B, Mehta A, Shah M. Denouements of machine learning and multimodal diagnostic classification of Alzheimer's disease. Vis Comput Ind Biomed Art 2020; 3:26. [PMID: 33151420 PMCID: PMC7642580 DOI: 10.1186/s42492-020-00062-w] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2020] [Accepted: 10/16/2020] [Indexed: 12/19/2022] Open
Abstract
Alzheimer's disease (AD) is the most common type of dementia. The exact cause and treatment of the disease are still unknown. Different neuroimaging modalities, such as magnetic resonance imaging (MRI), positron emission tomography, and single-photon emission computed tomography, have played a significant role in the study of AD. However, the effective diagnosis of AD, as well as mild cognitive impairment (MCI), has recently drawn large attention. Various technological advancements, such as robots, global positioning system technology, sensors, and machine learning (ML) algorithms, have helped improve the diagnostic process of AD. This study aimed to determine the influence of implementing different ML classifiers in MRI and analyze the use of support vector machines with various multimodal scans for classifying patients with AD/MCI and healthy controls. Conclusions have been drawn in terms of employing different classifier techniques and presenting the optimal multimodal paradigm for the classification of AD.
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Affiliation(s)
- Binny Naik
- Department of Computer Engineering, Indus University, Ahmedabad, Gujarat, 382115, India
| | - Ashir Mehta
- Department of Computer Engineering, Indus University, Ahmedabad, Gujarat, 382115, India
| | - Manan Shah
- Department of Chemical Engineering, School of Technology, Pandit Deendayal Petroleum University, Gandhinagar, Gujarat, 382007, India.
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Pilla A, Trigili E, McKinney Z, Fanciullacci C, Malasoma C, Posteraro F, Crea S, Vitiello N. Robotic Rehabilitation and Multimodal Instrumented Assessment of Post-stroke Elbow Motor Functions-A Randomized Controlled Trial Protocol. Front Neurol 2020; 11:587293. [PMID: 33193052 PMCID: PMC7643017 DOI: 10.3389/fneur.2020.587293] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2020] [Accepted: 09/22/2020] [Indexed: 12/03/2022] Open
Abstract
Background: The reliable assessment, attribution, and alleviation of upper-limb joint stiffness are essential clinical objectives in the early rehabilitation from stroke and other neurological disorders, to prevent the progression of neuromuscular pathology and enable proactive physiotherapy toward functional recovery. However, the current clinical evaluation and treatment of this stiffness (and underlying muscle spasticity) are severely limited by their dependence on subjective evaluation and manual limb mobilization, thus rendering the evaluation imprecise and the treatment insufficiently tailored to the specific pathologies and residual capabilities of individual patients. Methods: To address these needs, the proposed clinical trial will employ the NEUROExos Elbow Module (NEEM), an active robotic exoskeleton, for the passive mobilization and active training of elbow flexion and extension in 60 sub-acute and chronic stroke patients with motor impairments (hemiparesis and/or spasticity) of the right arm. The study protocol is a randomized controlled trial consisting of a 4-week functional rehabilitation program, with both clinical and robotically instrumented assessments to be conducted at baseline and post-treatment. The primary outcome measures will be a set of standard clinical scales for upper limb spasticity and motor function assessment, including the Modified Ashworth Scale and Fugl-Meyer Index, to confirm the safety and evaluate the efficacy of robotic rehabilitation in reducing elbow stiffness and improving function. Secondary outcomes will include biomechanical, muscular activity, and motor performance parameters extracted from instrumented assessments using the NEEM along with synchronous EMG recordings. Conclusions: This randomized controlled trial aims to validate an innovative instrumented methodology for clinical spasticity assessment and functional rehabilitation, relying on the precision and accuracy of an elbow exoskeleton combined with EMG recordings and the expertise of a physiotherapist, thus complementing and maximizing the benefits of both practices. Clinical Trial Registration:www.ClinicalTrials.gov, identifier NCT04484571.
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Affiliation(s)
- Alessandro Pilla
- The BioRobotics Institute, Scuola Superiore Sant'Anna, Pontedera, Italy
| | - Emilio Trigili
- The BioRobotics Institute, Scuola Superiore Sant'Anna, Pontedera, Italy
| | - Zach McKinney
- The BioRobotics Institute, Scuola Superiore Sant'Anna, Pontedera, Italy
| | | | - Chiara Malasoma
- Rehabilitation Department, Versilia Hospital, USL Nord Ovest Toscana (AUSLTNO), Lido di Camaiore (LU), Italy
| | - Federico Posteraro
- Rehabilitation Department, Versilia Hospital, USL Nord Ovest Toscana (AUSLTNO), Lido di Camaiore (LU), Italy
| | - Simona Crea
- The BioRobotics Institute, Scuola Superiore Sant'Anna, Pontedera, Italy.,IRCCS Fondazione Don Carlo Gnocchi, Firenze, Italy.,Department of Excellence in Robotics & AI, Scuola Superiore Sant'Anna, Piazza Martiri della Libertà, Pisa, Italy
| | - Nicola Vitiello
- The BioRobotics Institute, Scuola Superiore Sant'Anna, Pontedera, Italy.,IRCCS Fondazione Don Carlo Gnocchi, Firenze, Italy.,Department of Excellence in Robotics & AI, Scuola Superiore Sant'Anna, Piazza Martiri della Libertà, Pisa, Italy
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Samuel OW, Grace Asogbon M, Geng Y, Rusydi MI, Mzurikwao ZB, Chen S, Fang P, Li G. Characterizing Multiple Patterns of Motor Intent Using Spatial-Temporal Information for Intuitively Active Motor Training in Stroke Survivors. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2020; 2020:3831-3834. [PMID: 33018836 DOI: 10.1109/embc44109.2020.9176308] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Upper extremity motor function loss severely affects stroke survivors during daily life activities. Different rehabilitation robotic systems have been developed to allow stroke survivors regain their motor function. Meanwhile, most of the robots only operate in a passive mode and restrict the users to navigate predefined trajectories which may not align with their motion intent, thus limiting motor recovery. One way to resolve this issue would be to utilize a decoded movement intent to trigger intuitively active motor training for patients. In this direction, this study proposed and investigated the use of spatial-temporal neuromuscular descriptor (STD) for optimal decoding of multiple patterns of movement intents in patient to provide inputs for active motor training in the rehabilitation robotic systems. The STD performance was validated using High-Density surface electromyogram recordings from five stroke survivors who performed 21 limb movements. Experimental results show that the STD achieved a significant reduction in limb movement classification error (13.36%) even in the presence of the inevitable White Gaussian Noise compared to other methods (p<0.05). The STD also showed obvious class separability for individual movement. Findings from this study suggest that the STD may provide potential inputs for intuitively active motor training in stroke rehabilitation robotic systems.Clinical Relevance- This study showed that spatial-temporal neuromuscular information could aid adequate decoding of movement intents upon which intuitively active motor training could be achieved in stroke rehabilitation robotic systems.
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Bae S, Lee Y, Chang P. There is No test-retest reliability of brain activation induced by robotic passive hand movement: A functional NIRS study. Brain Behav 2020; 10:e01788. [PMID: 32794359 PMCID: PMC7559614 DOI: 10.1002/brb3.1788] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/03/2019] [Revised: 06/03/2020] [Accepted: 07/06/2020] [Indexed: 01/08/2023] Open
Abstract
INTRODUCTION The basic paradigm of rehabilitation is based on the brain plasticity, and for promoting it, test-retest reliability (TRR) of brain activation in which certain area of the brain is repeatedly activated is required. In this study, we investigated whether the robotic passive movement has the TRR of brain activation. While active training has been shown to have TRR, but there still have been arguments over the TRR by passive movement. METHODS In order to test TRR, 10 repetitive sessions and various intervals (1 day, 3 days, 7 days, 23 days, 15 min, and 6 hr) were applied to five subjects, which had the same statistical power as applying two sessions to 50 subjects. In each session, three robot speeds (0.25, 0.5, and 0.75 Hz) were applied to provide passive movement using the robot. The fNIRS signal (oxy-Hb) generated in the primary sensorimotor area (SM1) was measured on a total of 29 channels. At this time, we used activation maps and intraclass correlation coefficient (ICC) values to examine the TRR and the effect of robot speeds and intervals on TRR. RESULTS As a result, activation maps showed prominent variation regardless of robot speeds and interval, and the ICC value (=0.002) showed no TRR of brain activation for robotic passive movement. CONCLUSION The brain activation induced by the robotic passive movement alone has very poor TRR, suggesting that further enhancement is required to strengthen the TRR by complementing active user engagements.
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Affiliation(s)
- Sungjin Bae
- Department of Robotics EngineeringGraduate SchoolDaegu Gyeongbuk Institute of Science & TechnologyDaeguKorea
| | - Yonghee Lee
- Department of StatisticsUniversity of SeoulSeoulKorea
| | - Pyung‐Hun Chang
- Department of Robotics EngineeringGraduate SchoolDaegu Gyeongbuk Institute of Science & TechnologyDaeguKorea
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Atashzar SF, Huang HY, Duca FD, Burdet E, Farina D. Energetic Passivity Decoding of Human Hip Joint for Physical Human-Robot Interaction. IEEE Robot Autom Lett 2020; 5:5953-5960. [DOI: 10.1109/lra.2020.3010459] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/30/2023]
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Wang J, Zhang J, Zuo G, Shi C, Guo S. A reward–punishment feedback control strategy based on energy information for wrist rehabilitation. INT J ADV ROBOT SYST 2020. [DOI: 10.1177/1729881420940651] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
Based on evidence from the previous research in rehabilitation robot control strategies, we found that the common feature of the effective control strategies to promote subjects’ engagement is creating a reward–punishment feedback mechanism. This article proposes a reward–punishment feedback control strategy based on energy information. Firstly, an engagement estimated approach based on energy information is developed to evaluate subjects’ performance. Secondly, the estimated result forms a reward–punishment term, which is introduced into a standard model-based adaptive controller. This modified adaptive controller is capable of giving the reward–punishment feedback to subjects according to their engagement. Finally, several experiments are implemented using a wrist rehabilitation robot to evaluate the proposed control strategy with 10 healthy subjects who have not cardiovascular and cerebrovascular diseases. The results of these experiments show that the mean coefficient of determination ( R 2) of the data obtained by the proposed approach and the classical approach is 0.7988, which illustrate the reliability of the engagement estimated approach based on energy information. And the results also demonstrate that the proposed controller has great potential to promote patients’ engagement for wrist rehabilitation.
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Affiliation(s)
- Jiajin Wang
- Cixi Institute of BioMedical Engineering, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Science, Ningbo, Zhejiang, China
- Department of Mechanical Automation Engineering, School of Mechatronic Engineering and Automation, Shanghai University, Shanghai, China
| | - Jiaji Zhang
- Cixi Institute of BioMedical Engineering, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Science, Ningbo, Zhejiang, China
| | - Guokun Zuo
- Cixi Institute of BioMedical Engineering, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Science, Ningbo, Zhejiang, China
| | - Changcheng Shi
- Cixi Institute of BioMedical Engineering, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Science, Ningbo, Zhejiang, China
| | - Shuai Guo
- Department of Mechanical Automation Engineering, School of Mechatronic Engineering and Automation, Shanghai University, Shanghai, China
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Koroleva ES, Tolmachev IV, Alifirova VM, Boiko AS, Levchuk LA, Loonen AJM, Ivanova SA. Serum BDNF's Role as a Biomarker for Motor Training in the Context of AR-Based Rehabilitation after Ischemic Stroke. Brain Sci 2020; 10:E623. [PMID: 32916851 PMCID: PMC7564457 DOI: 10.3390/brainsci10090623] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2020] [Revised: 09/01/2020] [Accepted: 09/07/2020] [Indexed: 12/18/2022] Open
Abstract
BACKGROUND brain-derived neurotrophic factor (BDNF) may play a role during neurorehabilitation following ischemic stroke. This study aimed to elucidate the possible role of BDNF during early recovery from ischemic stroke assisted by motor training. METHODS fifty patients were included after acute recovery from ischemic stroke: 21 first received classical rehabilitation followed by 'motor rehabilitation using motion sensors and augmented reality' (AR-rehabilitation), 14 only received AR-rehabilitation, and 15 were only observed. Serum BDNF levels were measured on the first day of stroke, on the 14th day, before AR-based rehabilitation (median, 45th day), and after the AR-based rehabilitation (median, 82nd day). Motor impairment was quantified clinically using the Fugl-Meyer scale (FMA); functional disability and activities of daily living (ADL) were measured using the Modified Rankin Scale (mRS). For comparison, serum BDNF was measured in 50 healthy individuals. RESULTS BDNF levels were found to significantly increase during the phase with AR-based rehabilitation. The pattern of the sequentially measured BDNF levels was similar in the treated patients. Untreated patients had significantly lower BDNF levels at the endpoint. CONCLUSIONS the fluctuations of BDNF levels are not consistently related to motor improvement but seem to react to active treatment. Without active rehabilitation treatment, BDNF tends to decrease.
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Affiliation(s)
- Ekaterina S. Koroleva
- Department of Neurology and Neurosurgery, Siberian State Medical University, Moskovsky trakt, 2, 634050 Tomsk, Russia; (E.S.K.); (V.M.A.)
| | - Ivan V. Tolmachev
- Department of Medical and Biological Cybernetics, Siberian State Medical University, Moskovsky trakt, 2, 634050 Tomsk, Russia;
| | - Valentina M. Alifirova
- Department of Neurology and Neurosurgery, Siberian State Medical University, Moskovsky trakt, 2, 634050 Tomsk, Russia; (E.S.K.); (V.M.A.)
| | - Anastasiia S. Boiko
- Mental Health Research Institute, Tomsk National Research Medical Center of the Russian Academy of Sciences, Aleutskaya str., 4, 634014 Tomsk, Russia; (A.S.B.); (L.A.L.); (S.A.I.)
| | - Lyudmila A. Levchuk
- Mental Health Research Institute, Tomsk National Research Medical Center of the Russian Academy of Sciences, Aleutskaya str., 4, 634014 Tomsk, Russia; (A.S.B.); (L.A.L.); (S.A.I.)
| | - Anton J. M. Loonen
- PharmacoTherapy, -Epidemiology and -Economics, Groningen Research Institute of Pharmacy, University of Groningen, Antonius Deusinglaan 1, 9713AV Groningen, The Netherlands
| | - Svetlana A. Ivanova
- Mental Health Research Institute, Tomsk National Research Medical Center of the Russian Academy of Sciences, Aleutskaya str., 4, 634014 Tomsk, Russia; (A.S.B.); (L.A.L.); (S.A.I.)
- Department of Psychiatry, Addictology and Psychotherapy, Siberian State Medical University, Moskovsky trakt, 2, 634050 Tomsk, Russia
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Dawson-Elli AR, Adamczyk PG. Design and Validation of a Lower-Limb Haptic Rehabilitation Robot. IEEE Trans Neural Syst Rehabil Eng 2020; 28:1584-1594. [PMID: 32634097 PMCID: PMC7556714 DOI: 10.1109/tnsre.2020.3000735] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
Abstract
Present robots for investigating lower-limb motor control and rehabilitation focus on gait training. An alternative approach is to focus on restoring precursor abilities such as motor adaptation and volitional movement, as is common in upper-limb robotic therapy. Here we describe NOTTABIKE, a one degree-of-freedom rehabilitation robot designed to probe and promote these underlying capabilities. A recumbent exercise cycle platform is powered with a servomotor and instrumented with angular encoders, force-torque sensing pedals, and a wireless EMG system. Virtual environments ranging from spring-mass-damper systems to novel foot-to-crank mechanical laws present variants of leg-reaching and pedaling tasks that challenge perception, cognition, motion planning, and motor control systems. This paper characterizes the dynamic performance and haptic rendering accuracy of NOTTABIKE and presents an example motor adaptation task to illustrate its use. Torque and velocity mode controllers showed near unity magnitude ratio and phase loss less than 60 degrees up to 10 Hz. Spring rendering demonstrated 1% mean error in stiffness, and damper rendering performed comparably at 2.5%. Virtual mass rendering was less accurate but successful in varying perceived mass. NOTTABIKE will be used to study lower-limb motor adaptation in intact and impaired persons and to develop rehabilitation protocols that promote volitional movement recovery.
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A review of methods for achieving upper limb movement following spinal cord injury through hybrid muscle stimulation and robotic assistance. Exp Neurol 2020; 328:113274. [DOI: 10.1016/j.expneurol.2020.113274] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2019] [Revised: 02/26/2020] [Accepted: 03/02/2020] [Indexed: 11/20/2022]
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Wright ZA, Majeed YA, Patton JL, Huang FC. Key components of mechanical work predict outcomes in robotic stroke therapy. J Neuroeng Rehabil 2020; 17:53. [PMID: 32316977 PMCID: PMC7175566 DOI: 10.1186/s12984-020-00672-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2018] [Accepted: 03/05/2020] [Indexed: 12/01/2022] Open
Abstract
Background Clinical practice typically emphasizes active involvement during therapy. However, traditional approaches can offer only general guidance on the form of involvement that would be most helpful to recovery. Beyond assisting movement, robots allow comprehensive methods for measuring practice behaviors, including the energetic input of the learner. Using data from our previous study of robot-assisted therapy, we examined how separate components of mechanical work contribute to predicting training outcomes. Methods Stroke survivors (n = 11) completed six sessions in two-weeks of upper extremity motor exploration (self-directed movement practice) training with customized forces, while a control group (n = 11) trained without assistance. We employed multiple regression analysis to predict patient outcomes with computed mechanical work as independent variables, including separate features for elbow versus shoulder joints, positive (concentric) and negative (eccentric), flexion and extension. Results Our analysis showed that increases in total mechanical work during therapy were positively correlated with our final outcome metric, velocity range. Further analysis revealed that greater amounts of negative work at the shoulder and positive work at the elbow as the most important predictors of recovery (using cross-validated regression, R2 = 52%). However, the work features were likely mutually correlated, suggesting a prediction model that first removed shared variance (using PCA, R2 = 65–85%). Conclusions These results support robotic training for stroke survivors that increases energetic activity in eccentric shoulder and concentric elbow actions. Trial registration ClinicalTrials.gov, Identifier: NCT02570256. Registered 7 October 2015 – Retrospectively registered,
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Affiliation(s)
- Zachary A Wright
- Department of Bioengineering, University of Illinois at Chicago, Chicago, IL, USA.,Arms + Hands Lab, Shirley Ryan AbilityLab, Chicago, IL, USA
| | - Yazan A Majeed
- Department of Bioengineering, University of Illinois at Chicago, Chicago, IL, USA.,Arms + Hands Lab, Shirley Ryan AbilityLab, Chicago, IL, USA
| | - James L Patton
- Department of Bioengineering, University of Illinois at Chicago, Chicago, IL, USA.,Arms + Hands Lab, Shirley Ryan AbilityLab, Chicago, IL, USA
| | - Felix C Huang
- Department of Mechanical Engineering, Tufts University, Medford, MA, USA.
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Kopke JV, Ellis MD, Hargrove LJ. Determining User Intent of Partly Dynamic Shoulder Tasks in Individuals With Chronic Stroke Using Pattern Recognition. IEEE Trans Neural Syst Rehabil Eng 2020; 28:350-358. [PMID: 31751245 PMCID: PMC8522906 DOI: 10.1109/tnsre.2019.2955029] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2023]
Abstract
Stroke remains the leading cause of long-term disability in the US. Although therapy can achieve limited improvement of paretic arm use and performance, weakness and abnormal muscle synergies-which cause unintentional elbow, wrist, and finger flexion during shoulder abduction-contribute significantly to limb disuse and compound rehabilitation efforts. Emerging wearable exoskeleton technology could provide powered abduction support for the paretic arm, but requires a clinically feasible, robust control scheme capable of differentiating multiple shoulder degrees-of-freedom. This study examines whether pattern recognition of sensor data can accurately identify user intent for 9 combinations of 1- and 2- degree-of-freedom shoulder tasks. Participants with stroke (n = 12) used their paretic and non-paretic arms, and healthy controls (n = 12) used their dominant arm to complete tasks on a lab-based robot involving combinations of abduction, adduction, and internal and external rotation of the shoulder. We examined the effect of arm (paretic, non-paretic), load level (25% vs 50% maximal voluntary torque), and dataset (electromyography, load cell, or combined) on classifier performance. Results suggest that paretic arm, lower load levels, and using load cell or EMG data alone reduced classifier accuracy. However, this method still shows promise. Further work will examine classifier-user interaction during active control of a robotic device and optimization/minimization of sensors.
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Lee T, Lee D, Song B, Baek YS. Design and Control of a Polycentric Knee Exoskeleton Using an Electro-Hydraulic Actuator. SENSORS (BASEL, SWITZERLAND) 2019; 20:E211. [PMID: 31905974 PMCID: PMC6982713 DOI: 10.3390/s20010211] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/18/2019] [Revised: 12/26/2019] [Accepted: 12/27/2019] [Indexed: 12/03/2022]
Abstract
An exoskeleton robot helps the wearer with mechanical forces by identifying the wearer's intentions and requires high energy efficiency, sufficient load capacity, and a comfortable fit. However, since it is difficult to implement complex anatomical movements of the human body, most exoskeleton robots are designed simply, unlike the anatomy of real humans. This forces the wearer to accept the robot's stiffness entirely, and to use energy inefficiently from the power source. In this paper, a simple 1 degree of freedom (DoF) structure, which was mainly used in the knees of exoskeleton robots, was designed with a polycentric (multi-axial) structure to minimize the misalignment between wearer and robot, so that torque transfer could be carried out efficiently. In addition, the overall robot system was constructed by using an electro-hydraulic actuator (EHA) to solve the problems of the energy inefficiency of conventional hydraulic actuators and the low load capacity of conventional electric actuators. After the configuration of the hardware system, the sliding mode controller was designed to address the EHA nonlinear models and the uncertainty of the plant design. This was configured as Simulink for the first verification, and the experiment was conducted by applying it to the actual model to demonstrate the performance of the sliding mode control. In this process, an optical rotary encoder was used as the main feedback sensor of the controller. The proposed polycentric knee exoskeleton robot system using the EHA was able to reach the desired target value well despite the presence of many model uncertainties.
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Affiliation(s)
| | | | | | - Yoon Su Baek
- Department of Mechanical Engineering, Yonsei University, Seoul 03722, Korea; (T.L.); (D.L.); (B.S.)
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