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Ranzani R, Razzoli M, Sanson P, Song J, Galati S, Ferrarese C, Lambercy O, Kaelin-Lang A, Gassert R. Feasibility of Adjunct Therapy with a Robotic Hand Orthosis after Botulinum Toxin Injections in Persons with Spasticity: A Pilot Study. Toxins (Basel) 2024; 16:346. [PMID: 39195756 PMCID: PMC11360205 DOI: 10.3390/toxins16080346] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2024] [Revised: 07/28/2024] [Accepted: 07/30/2024] [Indexed: 08/29/2024] Open
Abstract
Upper-limb spasticity, frequent after central nervous system lesions, is typically treated with botulinum neurotoxin type A (BoNT-A) injections to reduce muscle tone and increase range of motion. However, performing adjunct physical therapy post-BoNT-A can be challenging due to residual weakness or spasticity. This study evaluates the feasibility of hand therapy using a robotic hand orthosis (RELab tenoexo) with a mobile phone application as an adjunct to BoNT-A injections. Five chronic spastic patients participated in a two-session pilot study. Functional (Box and Block Test (BBT), Action Research Arm Test (ARAT)), and muscle tone (Modified Ashworth Scale (MAS)) assessments were conducted to assess functional abilities and impairment, along with usability evaluations. In the first session, subjects received BoNT-A injections, and then they performed a simulated unsupervised therapy session with the RELab tenoexo in a second session a month later. Results showed that BoNT-A reduced muscle tone (from 12.2 to 7.4 MAS points). The addition of RELab tenoexo therapy was safe, led to functional improvements in four subjects (two-cube increase in BBT as well as 2.8 points in grasp and 1.3 points in grip on ARAT). Usability results indicate that, with minor improvements, adjunct RELab tenoexo therapy could enhance therapy doses and, potentially, long-term outcomes.
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Affiliation(s)
- Raffaele Ranzani
- Rehabilitation Engineering Laboratory, Department of Health Sciences and Technology, ETH Zurich, Gloriastrasse 37/39, 8092 Zurich, Switzerland; (M.R.); (P.S.); (J.S.); (O.L.); (R.G.)
- School of Medicine and Surgery and Milan Center for Neuroscience (NeuroMi), University of Milano-Bicocca, Piazza dell’Ateneo Nuovo 1, 20126 Milan, Italy;
- Cereneo, Center for Neurology and Rehabilitation, Seestrasse 18, 6354 Vitznau, Switzerland
| | - Margherita Razzoli
- Rehabilitation Engineering Laboratory, Department of Health Sciences and Technology, ETH Zurich, Gloriastrasse 37/39, 8092 Zurich, Switzerland; (M.R.); (P.S.); (J.S.); (O.L.); (R.G.)
| | - Pierre Sanson
- Rehabilitation Engineering Laboratory, Department of Health Sciences and Technology, ETH Zurich, Gloriastrasse 37/39, 8092 Zurich, Switzerland; (M.R.); (P.S.); (J.S.); (O.L.); (R.G.)
| | - Jaeyong Song
- Rehabilitation Engineering Laboratory, Department of Health Sciences and Technology, ETH Zurich, Gloriastrasse 37/39, 8092 Zurich, Switzerland; (M.R.); (P.S.); (J.S.); (O.L.); (R.G.)
| | - Salvatore Galati
- Faculty of Biomedical Sciences, Università della Svizzera Italiana, 6962 Lugano, Switzerland; (S.G.); (A.K.-L.)
- Neurology Department, Neurocenter of Southern Switzerland, Ente Ospedaliero Cantonale, 6900 Lugano, Switzerland
| | - Carlo Ferrarese
- School of Medicine and Surgery and Milan Center for Neuroscience (NeuroMi), University of Milano-Bicocca, Piazza dell’Ateneo Nuovo 1, 20126 Milan, Italy;
| | - Olivier Lambercy
- Rehabilitation Engineering Laboratory, Department of Health Sciences and Technology, ETH Zurich, Gloriastrasse 37/39, 8092 Zurich, Switzerland; (M.R.); (P.S.); (J.S.); (O.L.); (R.G.)
| | - Alain Kaelin-Lang
- Faculty of Biomedical Sciences, Università della Svizzera Italiana, 6962 Lugano, Switzerland; (S.G.); (A.K.-L.)
- Neurology Department, Neurocenter of Southern Switzerland, Ente Ospedaliero Cantonale, 6900 Lugano, Switzerland
- Department of Neurology, Inselspital, Bern University Hospital, University of Bern, 3010 Bern, Switzerland
| | - Roger Gassert
- Rehabilitation Engineering Laboratory, Department of Health Sciences and Technology, ETH Zurich, Gloriastrasse 37/39, 8092 Zurich, Switzerland; (M.R.); (P.S.); (J.S.); (O.L.); (R.G.)
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2
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Sarhan SM, Al-Faiz MZ, Takhakh AM. A review on EMG/EEG based control scheme of upper limb rehabilitation robots for stroke patients. Heliyon 2023; 9:e18308. [PMID: 37533980 PMCID: PMC10391943 DOI: 10.1016/j.heliyon.2023.e18308] [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: 12/16/2022] [Revised: 07/03/2023] [Accepted: 07/13/2023] [Indexed: 08/04/2023] Open
Abstract
Stroke is a common worldwide health problem and a crucial contributor to gained disability. The abilities of people, who are subjected to stroke, to live independently are significantly affected since affected upper limbs' functions are essential for our daily life. This review article focuses on emerging trends in BCI-controlled rehabilitation techniques based on EMG, EEG, or EGM + EEG signals in the last few years. Working on developing rehabilitation robotics, is considered a wealthy scientific area for researchers in the last period. There is a significant advantage that the human acquires from the interaction between the machine and his body, rehabilitation for a patient's limb is very important to get the body limb recovery, and this is what is provided mostly by applying robotic devices.
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Affiliation(s)
- Saad M. Sarhan
- Department of Biomedical Engineering, College of Engineering, Al-Nahrain University, Baghdad, Iraq
| | - Mohammed Z. Al-Faiz
- Department of Control and Computer, College of Information Engineering, Al-Nahrain University, Baghdad, Iraq
| | - Ayad M. Takhakh
- Department of Biomechanics, College of Engineering, Al-Nahrain University, Baghdad, Iraq
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Chen Y, Zhang H, Wang C, Ang KK, Ng SH, Jin H, Lin Z. A hierarchical dynamic Bayesian learning network for EMG-based early prediction of voluntary movement intention. Sci Rep 2023; 13:4730. [PMID: 36959307 PMCID: PMC10036485 DOI: 10.1038/s41598-023-30716-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2022] [Accepted: 02/28/2023] [Indexed: 03/25/2023] Open
Abstract
Decoding human action intention prior to motion onset with surface electromyograms (sEMG) is an emerging neuroengineering topic with interesting clinical applications such as intelligent control of powered prosthesis/exoskeleton devices. Despite extensive prior works in the related fields, it remains a technical challenge due to considerable variability of complex multi-muscle activation patterns in terms of volatile spatio-temporal characteristics. To address this issue, we first hypothesize that the inherent variability of the idle state immediately preceding the motion initiation needs to be addressed explicitly. We therefore design a hierarchical dynamic Bayesian learning network model that integrates an array of Gaussian mixture model - hidden Markov models (GMM-HMMs), where each GMM-HMM learns the multi-sEMG processes either during the idle state, or during the motion initiation phase of a particular motion task. To test the hypothesis and evaluate the new learning network, we design and build a upper-limb sEMG-joystick motion study system, and collect data from 11 healthy volunteers. The data collection protocol adapted from the psychomotor vigilance task includes repeated and randomized binary hand motion tasks (push or pull) starting from either of two designated idle states: relaxed (with minimal muscle tones), or prepared (with muscle tones). We run a series of cross-validation tests to examine the performance of the method in comparison with the conventional techniques. The results suggest that the idle state recognition favors the dynamic Bayesian model over a static classification model. The results also show a statistically significant improvement in motion prediction accuracy by the proposed method (93.83±6.41%) in comparison with the conventional GMM-HMM method (89.71±8.98%) that does not explicitly account for the idle state. Moreover, we examine the progress of prediction accuracy over the course of motion initiation and identify the important hidden states that warrant future research.
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Affiliation(s)
- Yongming Chen
- Institute for Infocomm Research, Agency for Science, Technology and Research, Singapore, Singapore
- School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, Singapore
| | - Haihong Zhang
- Institute for Infocomm Research, Agency for Science, Technology and Research, Singapore, Singapore.
| | - Chuanchu Wang
- Institute for Infocomm Research, Agency for Science, Technology and Research, Singapore, Singapore
| | - Kai Keng Ang
- Institute for Infocomm Research, Agency for Science, Technology and Research, Singapore, Singapore
- School of Computer Science and Engineering, Nanyang Technological University, Singapore, Singapore
| | - Soon Huat Ng
- Institute for Infocomm Research, Agency for Science, Technology and Research, Singapore, Singapore
| | - Huiwen Jin
- Institute for Infocomm Research, Agency for Science, Technology and Research, Singapore, Singapore
- School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, Singapore
| | - Zhiping Lin
- School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, Singapore
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4
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Chen A, Winterbottom L, O'Reilly K, Park S, Nilsen D, Stein J, Ciocarlie M. Design of Spiral-Cable Forearm Exoskeleton to Assist Supination for Hemiparetic Stroke Subjects. IEEE Int Conf Rehabil Robot 2022; 2022:1-6. [PMID: 36176095 PMCID: PMC9673240 DOI: 10.1109/icorr55369.2022.9896608] [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: 06/16/2023]
Abstract
We present the development of a cable-based passive forearm exoskeleton that is designed to assist supination for hemiparetic stroke survivors. Our device uniquely provides torque sufficient for counteracting spasticity within a below-elbow apparatus. The mechanism consists of a spiral single-tendon routing embedded in a rigid forearm brace and terminated at the hand and upper-forearm. A spool with an internal releasable-ratchet mechanism allows the user to manually retract the tendon and rotate the hand to counteract involuntary pronation synergies due to stroke. We characterize the mechanism with benchtop testing and five healthy subjects, and perform a preliminary assessment of the exoskeleton with a single chronic stroke subject having minimal supination ability. The mechanism can be integrated into an existing active hand-opening orthosis to enable supination support during grasping tasks, and also allows for a future actuated supination strategy.
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5
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Xu J, Meeker C, Chen A, Winterbottom L, Fraser M, Park S, Weber LM, Miya M, Nilsen D, Stein J, Ciocarlie M. Adaptive Semi-Supervised Intent Inferral to Control a Powered Hand Orthosis for Stroke. IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION : ICRA : [PROCEEDINGS]. IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION 2022; 2022:8097-8103. [PMID: 37181542 PMCID: PMC10181849 DOI: 10.1109/icra46639.2022.9811932] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/16/2023]
Abstract
In order to provide therapy in a functional context, controls for wearable robotic orthoses need to be robust and intuitive. We have previously introduced an intuitive, user-driven, EMG-based method to operate a robotic hand orthosis, but the process of training a control that is robust to concept drift (changes in the input signal) places a substantial burden on the user. In this paper, we explore semi-supervised learning as a paradigm for controlling a powered hand orthosis for stroke subjects. To the best of our knowledge, this is the first use of semi-supervised learning for an orthotic application. Specifically, we propose a disagreement-based semi-supervision algorithm for handling intrasession concept drift based on multimodal ipsilateral sensing. We evaluate the performance of our algorithm on data collected from five stroke subjects. Our results show that the proposed algorithm helps the device adapt to intrasession drift using unlabeled data and reduces the training burden placed on the user. We also validate the feasibility of our proposed algorithm with a functional task; in these experiments, two subjects successfully completed multiple instances of a pick-and-handover task.
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Affiliation(s)
- Jingxi Xu
- Department of Computer Science, Columbia University, New York, NY 10027, USA
| | - Cassie Meeker
- Department of Mechanical Engineering, Columbia University, New York, NY 10027, USA
| | - Ava Chen
- Department of Mechanical Engineering, Columbia University, New York, NY 10027, USA
| | - Lauren Winterbottom
- Department of Rehabilitation and Regenerative Medicine, Columbia University, New York, NY 10032, USA
| | - Michaela Fraser
- Department of Rehabilitation and Regenerative Medicine, Columbia University, New York, NY 10032, USA
| | - Sangwoo Park
- Department of Mechanical Engineering, Columbia University, New York, NY 10027, USA
| | - Lynne M Weber
- Department of Rehabilitation and Regenerative Medicine, Columbia University, New York, NY 10032, USA
| | - Mitchell Miya
- Department of Mechanical Engineering, Columbia University, New York, NY 10027, USA
| | - Dawn Nilsen
- Department of Rehabilitation and Regenerative Medicine, Columbia University, New York, NY 10032, USA
- Co-Principal Investigators
| | - Joel Stein
- Department of Rehabilitation and Regenerative Medicine, Columbia University, New York, NY 10032, USA
- Co-Principal Investigators
| | - Matei Ciocarlie
- Department of Mechanical Engineering, Columbia University, New York, NY 10027, USA
- Co-Principal Investigators
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6
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Bao T, Xie SQ, Yang P, Zhou P, Zhang ZQ. Towards Robust, Adaptive and Reliable Upper-limb Motion Estimation Using Machine Learning and Deep Learning--A Survey in Myoelectric Control. IEEE J Biomed Health Inform 2022; 26:3822-3835. [PMID: 35294368 DOI: 10.1109/jbhi.2022.3159792] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
To develop multi-functional human-machine interfaces that can help disabled people reconstruct lost functions of upper-limbs, machine learning (ML) and deep learning (DL) techniques have been widely implemented to decode human movement intentions from surface electromyography (sEMG) signals. However, due to the high complexity of upper-limb movements and the inherent non-stable characteristics of sEMG, the usability of ML/DL based control schemes is still greatly limited in practical scenarios. To this end, tremendous efforts have been made to improve model robustness, adaptation, and reliability. In this article, we provide a systematic review on recent achievements, mainly from three categories: multi-modal sensing fusion to gain additional information of the user, transfer learning (TL) methods to eliminate domain shift impacts on estimation models, and post-processing approaches to obtain more reliable outcomes. Special attention is given to fusion strategies, deep TL frameworks, and confidence estimation. \textcolor{red}{Research challenges and emerging opportunities, with respect to hardware development, public resources, and decoding strategies, are also analysed to provide perspectives for future developments.
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Gantenbein J, Dittli J, Meyer JT, Gassert R, Lambercy O. Intention Detection Strategies for Robotic Upper-Limb Orthoses: A Scoping Review Considering Usability, Daily Life Application, and User Evaluation. Front Neurorobot 2022; 16:815693. [PMID: 35264940 PMCID: PMC8900616 DOI: 10.3389/fnbot.2022.815693] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2021] [Accepted: 01/24/2022] [Indexed: 11/13/2022] Open
Abstract
Wearable robotic upper limb orthoses (ULO) are promising tools to assist or enhance the upper-limb function of their users. While the functionality of these devices has continuously increased, the robust and reliable detection of the user's intention to control the available degrees of freedom remains a major challenge and a barrier for acceptance. As the information interface between device and user, the intention detection strategy (IDS) has a crucial impact on the usability of the overall device. Yet, this aspect and the impact it has on the device usability is only rarely evaluated with respect to the context of use of ULO. A scoping literature review was conducted to identify non-invasive IDS applied to ULO that have been evaluated with human participants, with a specific focus on evaluation methods and findings related to functionality and usability and their appropriateness for specific contexts of use in daily life. A total of 93 studies were identified, describing 29 different IDS that are summarized and classified according to a four-level classification scheme. The predominant user input signal associated with the described IDS was electromyography (35.6%), followed by manual triggers such as buttons, touchscreens or joysticks (16.7%), as well as isometric force generated by residual movement in upper-limb segments (15.1%). We identify and discuss the strengths and weaknesses of IDS with respect to specific contexts of use and highlight a trade-off between performance and complexity in selecting an optimal IDS. Investigating evaluation practices to study the usability of IDS, the included studies revealed that, primarily, objective and quantitative usability attributes related to effectiveness or efficiency were assessed. Further, it underlined the lack of a systematic way to determine whether the usability of an IDS is sufficiently high to be appropriate for use in daily life applications. This work highlights the importance of a user- and application-specific selection and evaluation of non-invasive IDS for ULO. For technology developers in the field, it further provides recommendations on the selection process of IDS as well as to the design of corresponding evaluation protocols.
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Affiliation(s)
- Jessica Gantenbein
- Rehabilitation Engineering Laboratory, Department of Health Sciences and Technology, ETH Zurich, Zurich, Switzerland
| | - Jan Dittli
- Rehabilitation Engineering Laboratory, Department of Health Sciences and Technology, ETH Zurich, Zurich, Switzerland
| | - Jan Thomas Meyer
- Rehabilitation Engineering Laboratory, Department of Health Sciences and Technology, ETH Zurich, Zurich, Switzerland
| | - Roger Gassert
- Rehabilitation Engineering Laboratory, Department of Health Sciences and Technology, ETH Zurich, Zurich, Switzerland
- Future Health Technologies, Singapore-ETH Centre, Campus for Research Excellence and Technological Enterprise (CREATE), Singapore, Singapore
| | - Olivier Lambercy
- Rehabilitation Engineering Laboratory, Department of Health Sciences and Technology, ETH Zurich, Zurich, Switzerland
- Future Health Technologies, Singapore-ETH Centre, Campus for Research Excellence and Technological Enterprise (CREATE), Singapore, Singapore
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8
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Capotorti E, Trigili E, McKinney Z, Peperoni E, Dell'Agnello F, Fantozzi M, Baldoni A, Marconi D, Taglione E, Crea S, Vitiello N. A Novel Torque-Controlled Hand Exoskeleton to Decode Hand Movements Combining Semg and Fingers Kinematics: A Feasibility Study. IEEE Robot Autom Lett 2022. [DOI: 10.1109/lra.2021.3111412] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
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9
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Chen A, Winterbottom L, Park S, Xu J, Nilsen DM, Stein J, Ciocarlie M. Thumb Stabilization and Assistance in a Robotic Hand Orthosis for Post-Stroke Hemiparesis. IEEE Robot Autom Lett 2022; 7:8276-8282. [DOI: 10.1109/lra.2022.3185365] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Affiliation(s)
- Ava Chen
- Department of Mechanical Engineering, Columbia University, New York, NY, USA
| | - Lauren Winterbottom
- Department of Rehabilitation and Regenerative Medicine, Columbia University, New York, NY, USA
| | - Sangwoo Park
- Department of Mechanical Engineering, Columbia University, New York, NY, USA
| | - Jingxi Xu
- Department of Computer Science, Columbia University, New York, NY, USA
| | - Dawn M. Nilsen
- Department of Rehabilitation and Regenerative Medicine, Columbia University, New York, NY, USA
| | - Joel Stein
- Department of Rehabilitation and Regenerative Medicine, Columbia University, New York, NY, USA
| | - Matei Ciocarlie
- Department of Mechanical Engineering, Columbia University, New York, NY, USA
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Briko A, Kapravchuk V, Kobelev A, Hammoud A, Leonhardt S, Ngo C, Gulyaev Y, Shchukin S. A Way of Bionic Control Based on EI, EMG, and FMG Signals. SENSORS 2021; 22:s22010152. [PMID: 35009694 PMCID: PMC8747574 DOI: 10.3390/s22010152] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/09/2021] [Revised: 12/07/2021] [Accepted: 12/22/2021] [Indexed: 01/24/2023]
Abstract
Creating highly functional prosthetic, orthotic, and rehabilitation devices is a socially relevant scientific and engineering task. Currently, certain constraints hamper the development of such devices. The primary constraint is the lack of an intuitive and reliable control interface working between the organism and the actuator. The critical point in developing these devices and systems is determining the type and parameters of movements based on control signals recorded on an extremity. In the study, we investigate the simultaneous acquisition of electric impedance (EI), electromyography (EMG), and force myography (FMG) signals during basic wrist movements: grasping, flexion/extension, and rotation. For investigation, a laboratory instrumentation and software test setup were made for registering signals and collecting data. The analysis of the acquired signals revealed that the EI signals in conjunction with the analysis of EMG and FMG signals could potentially be highly informative in anthropomorphic control systems. The study results confirm that the comprehensive real-time analysis of EI, EMG, and FMG signals potentially allows implementing the method of anthropomorphic and proportional control with an acceptable delay.
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Affiliation(s)
- Andrey Briko
- Department of Medical and Technical Information Technology, Bauman Moscow State Technical University, 105005 Moscow, Russia; (V.K.); (A.K.); (A.H.); (S.S.)
- Correspondence: ; Tel.: +7-903-261-60-14
| | - Vladislava Kapravchuk
- Department of Medical and Technical Information Technology, Bauman Moscow State Technical University, 105005 Moscow, Russia; (V.K.); (A.K.); (A.H.); (S.S.)
| | - Alexander Kobelev
- Department of Medical and Technical Information Technology, Bauman Moscow State Technical University, 105005 Moscow, Russia; (V.K.); (A.K.); (A.H.); (S.S.)
| | - Ahmad Hammoud
- Department of Medical and Technical Information Technology, Bauman Moscow State Technical University, 105005 Moscow, Russia; (V.K.); (A.K.); (A.H.); (S.S.)
| | - Steffen Leonhardt
- Medical Information Technology, RWTH Aachen University, 52074 Aachen, Germany; (S.L.); (C.N.)
| | - Chuong Ngo
- Medical Information Technology, RWTH Aachen University, 52074 Aachen, Germany; (S.L.); (C.N.)
| | - Yury Gulyaev
- Kotelnikov Institute of Radioengineering and Electronics (IRE) of Russian Academy of Sciences, 125009 Moscow, Russia;
| | - Sergey Shchukin
- Department of Medical and Technical Information Technology, Bauman Moscow State Technical University, 105005 Moscow, Russia; (V.K.); (A.K.); (A.H.); (S.S.)
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11
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Martinez-Hernandez U, Metcalfe B, Assaf T, Jabban L, Male J, Zhang D. Wearable Assistive Robotics: A Perspective on Current Challenges and Future Trends. SENSORS (BASEL, SWITZERLAND) 2021; 21:6751. [PMID: 34695964 PMCID: PMC8539021 DOI: 10.3390/s21206751] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/30/2021] [Revised: 09/30/2021] [Accepted: 10/06/2021] [Indexed: 11/16/2022]
Abstract
Wearable assistive robotics is an emerging technology with the potential to assist humans with sensorimotor impairments to perform daily activities. This assistance enables individuals to be physically and socially active, perform activities independently, and recover quality of life. These benefits to society have motivated the study of several robotic approaches, developing systems ranging from rigid to soft robots with single and multimodal sensing, heuristics and machine learning methods, and from manual to autonomous control for assistance of the upper and lower limbs. This type of wearable robotic technology, being in direct contact and interaction with the body, needs to comply with a variety of requirements to make the system and assistance efficient, safe and usable on a daily basis by the individual. This paper presents a brief review of the progress achieved in recent years, the current challenges and trends for the design and deployment of wearable assistive robotics including the clinical and user need, material and sensing technology, machine learning methods for perception and control, adaptability and acceptability, datasets and standards, and translation from lab to the real world.
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Affiliation(s)
- Uriel Martinez-Hernandez
- Multimodal Inte-R-Action Lab, University of Bath, Bath BA2 7AY, UK;
- Centre for Autonomous Robotics (CENTAUR), University of Bath, Bath BA2 7AY, UK; (B.M.); (T.A.); (D.Z.)
- Centre for Biosensors, Bioelectronics and Biodevices (C3Bio), University of Bath, Bath BA2 7AY, UK;
- Department of Electronics and Electrical Engineering, University of Bath, Bath BA2 7AY, UK
| | - Benjamin Metcalfe
- Centre for Autonomous Robotics (CENTAUR), University of Bath, Bath BA2 7AY, UK; (B.M.); (T.A.); (D.Z.)
- Centre for Biosensors, Bioelectronics and Biodevices (C3Bio), University of Bath, Bath BA2 7AY, UK;
- Department of Electronics and Electrical Engineering, University of Bath, Bath BA2 7AY, UK
| | - Tareq Assaf
- Centre for Autonomous Robotics (CENTAUR), University of Bath, Bath BA2 7AY, UK; (B.M.); (T.A.); (D.Z.)
- Centre for Biosensors, Bioelectronics and Biodevices (C3Bio), University of Bath, Bath BA2 7AY, UK;
- Department of Electronics and Electrical Engineering, University of Bath, Bath BA2 7AY, UK
| | - Leen Jabban
- Centre for Biosensors, Bioelectronics and Biodevices (C3Bio), University of Bath, Bath BA2 7AY, UK;
- Department of Electronics and Electrical Engineering, University of Bath, Bath BA2 7AY, UK
| | - James Male
- Multimodal Inte-R-Action Lab, University of Bath, Bath BA2 7AY, UK;
- Centre for Autonomous Robotics (CENTAUR), University of Bath, Bath BA2 7AY, UK; (B.M.); (T.A.); (D.Z.)
- Department of Electronics and Electrical Engineering, University of Bath, Bath BA2 7AY, UK
| | - Dingguo Zhang
- Centre for Autonomous Robotics (CENTAUR), University of Bath, Bath BA2 7AY, UK; (B.M.); (T.A.); (D.Z.)
- Centre for Biosensors, Bioelectronics and Biodevices (C3Bio), University of Bath, Bath BA2 7AY, UK;
- Department of Electronics and Electrical Engineering, University of Bath, Bath BA2 7AY, UK
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12
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Khan SM, Khan AA, Farooq O. EMG based classification for pick and place task. Biomed Phys Eng Express 2021; 7. [PMID: 33882462 DOI: 10.1088/2057-1976/abfa81] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2020] [Accepted: 04/21/2021] [Indexed: 11/12/2022]
Abstract
The hand amputee is deprived of number of activities of daily living. To help the hand amputee, it is important to learn the pattern of muscles activity. There are several elements of tasks, which involve forearm along with the wrist and hand. The one very important task is pick and place activity performed by the hand. A pick and place action is a compilation of different finger motions for the grasping of objects at different force levels. This action may be better understood by learning the electromyography signals of forearm muscles. Electromyography is the technique to acquire electrical muscle activity that is used for the pattern recognition technique of assistive devices. Regarding this, the different classification characterizations of EMG signals involved in the pick and place action, subjected to variable grip span and weights were considered in this study. A low-level force measuring gripper, capable to bear the changes in weights and object spans was designed and developed to simulate the task. The grip span varied from 6 cm to 9 cm and the maximum weight used in this study was 750 gms. The pattern recognition classification methodology was performed for the differentiation of phases of the pick and place activity, grip force, and the angular deviation of metacarpal phalangeal (MCP) joint. The classifiers used in this study were decision tree (DT), support vector machines (SVM) and k-nearest neighbor (k-NN) based on the feature sets of the EMG signals. After analyses, it was found that k-NN performed best to classify different phases of the activity and relative deviation of MCP joint with an average classification accuracy of 82% and 91% respectively. However; the SVM performed best in classification of force with a particular feature set. The findings of the study would be helpful in designing the assistive devices for hand amputee.
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Affiliation(s)
- Salman Mohd Khan
- Department of Mechanical Engineering, Aligarh Muslim University, Aligarh, India
| | - Abid Ali Khan
- Department of Mechanical Engineering, Aligarh Muslim University, Aligarh, India
| | - Omar Farooq
- Department of Mechanical Engineering, Aligarh Muslim University, Aligarh, India
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13
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Feng Y, Zhong M, Wang X, Lu H, Wang H, Liu P, Vladareanu L. Active triggering control of pneumatic rehabilitation gloves based on surface electromyography sensors. PeerJ Comput Sci 2021; 7:e448. [PMID: 33977130 PMCID: PMC8064233 DOI: 10.7717/peerj-cs.448] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2020] [Accepted: 02/27/2021] [Indexed: 06/12/2023]
Abstract
The portable and inexpensive hand rehabilitation robot has become a practical rehabilitation device for patients with hand dysfunction. A pneumatic rehabilitation glove with an active trigger control system is proposed, which is based on surface electromyography (sEMG) signals. It can trigger the hand movement based on the patient's hand movement trend, which may improve the enthusiasm and efficiency of patient training. Firstly, analysis of sEMG sensor installation position on human's arm and signal acquisition process were carried out. Then, according to the statistical law, three optimal eigenvalues of sEMG signals were selected as the follow-up neural network classification input. Using the back propagation (BP) neural network, the classifier of hand movement is established. Moreover, the mapping relationship between hand sEMG signals and hand actions is built by training and testing. Different patients choose the same optimal eigenvalues, and the calculation formula of eigenvalues' amplitude is unique. Due to the differences among individuals, the weights and thresholds of each node in the BP neural network model corresponding to different patients are not the same. Therefore, the BP neural network model library is established, and the corresponding network is called for operation when different patients are trained. Finally, based on sEMG signal trigger, the pneumatic glove training control algorithm was proposed. The combination of the trigger signal waveform and the motion signal waveform indicates that the pneumatic rehabilitation glove is triggered to drive the patient's hand movement. Preliminary tests have confirmed that the accuracy rate of trend recognition for hand movement is about 90%. In the future, clinical trials of patients will be conducted to prove the effectiveness of this system.
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Affiliation(s)
- Yongfei Feng
- Faculty of Mechanical Engineering & Mechanics, Ningbo University, Ningbo, Zhejiang Province, China
- Robotics and Mechatronics Department, Institute of Solid Mechanics of the Romanian Academy, Bucharest, Bucharest, Romania
| | - Mingwei Zhong
- Faculty of Mechanical Engineering & Mechanics, Ningbo University, Ningbo, Zhejiang Province, China
| | - Xusheng Wang
- Academy for Engineering & Technology, Fudan University, Shanghai, Shanghai, China
| | - Hao Lu
- Academy for Engineering & Technology, Fudan University, Shanghai, Shanghai, China
| | - Hongbo Wang
- Academy for Engineering & Technology, Fudan University, Shanghai, Shanghai, China
| | - Pengcheng Liu
- Department of Computer Science, University of York, York, York, United Kingdom
| | - Luige Vladareanu
- Robotics and Mechatronics Department, Institute of Solid Mechanics of the Romanian Academy, Bucharest, Bucharest, Romania
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14
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Elbow Motion Trajectory Prediction Using a Multi-Modal Wearable System: A Comparative Analysis of Machine Learning Techniques. SENSORS 2021; 21:s21020498. [PMID: 33445601 PMCID: PMC7827251 DOI: 10.3390/s21020498] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/10/2020] [Revised: 01/06/2021] [Accepted: 01/08/2021] [Indexed: 12/03/2022]
Abstract
Motion intention detection is fundamental in the implementation of human-machine interfaces applied to assistive robots. In this paper, multiple machine learning techniques have been explored for creating upper limb motion prediction models, which generally depend on three factors: the signals collected from the user (such as kinematic or physiological), the extracted features and the selected algorithm. We explore the use of different features extracted from various signals when used to train multiple algorithms for the prediction of elbow flexion angle trajectories. The accuracy of the prediction was evaluated based on the mean velocity and peak amplitude of the trajectory, which are sufficient to fully define it. Results show that prediction accuracy when using solely physiological signals is low, however, when kinematic signals are included, it is largely improved. This suggests kinematic signals provide a reliable source of information for predicting elbow trajectories. Different models were trained using 10 algorithms. Regularization algorithms performed well in all conditions, whereas neural networks performed better when the most important features are selected. The extensive analysis provided in this study can be consulted to aid in the development of accurate upper limb motion intention detection models.
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15
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Yurkewich A, Kozak IJ, Ivanovic A, Rossos D, Wang RH, Hebert D, Mihailidis A. Myoelectric untethered robotic glove enhances hand function and performance on daily living tasks after stroke. J Rehabil Assist Technol Eng 2020; 7:2055668320964050. [PMID: 33403121 PMCID: PMC7745545 DOI: 10.1177/2055668320964050] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2020] [Accepted: 09/02/2020] [Indexed: 02/06/2023] Open
Abstract
Introduction Wearable robots controlled using electromyography could motivate greater use of the affected upper extremity after stroke and enable bimanual activities of daily living to be completed independently. Methods We have developed a myoelectric untethered robotic glove (My-HERO) that provides five-finger extension and grip assistance. Results The myoelectric controller detected the grip and release intents of the 9 participants after stroke with 84.7% accuracy. While using My-HERO, all 9 participants performed better on the Fugl-Meyer Assessment-Hand (8.4 point increase, scale out of 14, p < 0.01) and the Chedoke Arm and Hand Activity Inventory (8.2 point increase, scale out of 91, p < 0.01). Established criteria for clinically meaningful important differences were surpassed for both the hand function and daily living task assessments. The majority of participants provided satisfaction and usability questionnaire scores above 70%. Seven participants desired to use My-HERO in the clinic and at home during their therapy and daily routines. Conclusions People with hand impairment after stroke value that myoelectric untethered robotic gloves enhance their motion and bimanual task performance and motivate them to use their muscles during engaging activities of daily living. They desire to use these gloves daily to enable greater independence and investigate the effects on neuromuscular recovery.
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Affiliation(s)
- Aaron Yurkewich
- Toronto Rehabilitation Institute-KITE, University Health Network, Toronto, Canada.,Institute of Biomaterials and Biomedical Engineering, University of Toronto, Toronto, Canada.,Bioengineering, Imperial College London, London, UK
| | - Illya J Kozak
- Toronto Rehabilitation Institute-KITE, University Health Network, Toronto, Canada
| | - Andrei Ivanovic
- Faculty of Applied Science and Engineering, University of Toronto, Toronto, Canada
| | - Daniel Rossos
- Faculty of Applied Science and Engineering, University of Toronto, Toronto, Canada
| | - Rosalie H Wang
- Toronto Rehabilitation Institute-KITE, University Health Network, Toronto, Canada.,Department of Occupational Science and Occupational Therapy, University of Toronto, Toronto, Canada
| | - Debbie Hebert
- Toronto Rehabilitation Institute-KITE, University Health Network, Toronto, Canada.,Department of Occupational Science and Occupational Therapy, University of Toronto, Toronto, Canada
| | - Alex Mihailidis
- Toronto Rehabilitation Institute-KITE, University Health Network, Toronto, Canada.,Department of Occupational Science and Occupational Therapy, University of Toronto, Toronto, Canada
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16
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Park S, Fraser M, Weber LM, Meeker C, Bishop L, Geller D, Stein J, Ciocarlie M. User-Driven Functional Movement Training With a Wearable Hand Robot After Stroke. IEEE Trans Neural Syst Rehabil Eng 2020; 28:2265-2275. [PMID: 32886611 DOI: 10.1109/tnsre.2020.3021691] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
We studied the performance of a robotic orthosis designed to assist the paretic hand after stroke. It is wearable and fully user-controlled, serving two possible roles: as a therapeutic tool that facilitates device-mediated hand exercises to recover neuromuscular function or as an assistive device for use in everyday activities to aid functional use of the hand. We present the clinical outcomes of a pilot study designed as a feasibility test for these hypotheses. 11 chronic stroke (>2 years) patients with moderate muscle tone (Modified Ashworth Scale ≤ 2 in upper extremity) engaged in a month-long training protocol using the orthosis. Individuals were evaluated using standardized outcome measures, both with and without orthosis assistance. Fugl-Meyer post intervention scores without robotic assistance showed improvement focused specifically at the distal joints of the upper limb, suggesting the use of the orthosis as a rehabilitative device for the hand. Action Research Arm Test scores post intervention with robotic assistance showed that the device may serve an assistive role in grasping tasks. These results highlight the potential for wearable and user-driven robotic hand orthoses to extend the use and training of the affected upper limb after stroke.
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17
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Li H, Zhang J, Chen J, Luo Z, Zhang J, Alhandarish Y, Liu Q, Tang W, Wang L. A Supersensitive, Multidimensional Flexible Strain Gauge Sensor Based on Ag/PDMS for Human Activities Monitoring. Sci Rep 2020; 10:4639. [PMID: 32170154 PMCID: PMC7070104 DOI: 10.1038/s41598-020-61658-z] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2019] [Accepted: 02/27/2020] [Indexed: 11/09/2022] Open
Abstract
For more comprehensive monitoring human state of motion, it is necessary to sense multidimensional stimulus information. In this paper, we reported a supersensitive flexible sensor based on Ag/PDMS composites with sensing abilities of strain and force. The fabrication method is simple and rapid, which only need physically grinding the silver particles and mixing with liquid PDMS. The flexible sensor has excellent performances in multidimensional detection. The strain gauge factor can reach as high as 939 when it was stretched to 36%, and the minimum resolution for force detection is 0.02 N. The sensing characteristic of the sensors with different filling fraction and thickness were analyzed from the microscopic point of view. Multidimensional sensing abilities of flexible sensor have greatly expands its applications. We experimentally verified the Ag/PDMS based sensor in human body dynamic monitoring and sound detecting in real-time, which has shown great potential in motion recognition, haptic perception and soft robotics.
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Affiliation(s)
- Hui Li
- Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, Guangdong, China.
| | - Jinjie Zhang
- Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, Guangdong, China
| | - Jing Chen
- Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, Guangdong, China
| | - Zebang Luo
- Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, Guangdong, China
| | - Jinyong Zhang
- Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, Guangdong, China.,The College of Big Data and Internet, Shenzhen Technology University, Shenzhen, 518118, Guangdong, China
| | - Yousef Alhandarish
- Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, Guangdong, China
| | - Qiuhua Liu
- Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, Guangdong, China
| | - Wei Tang
- Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, Guangdong, China
| | - Lei Wang
- Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, Guangdong, China.
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18
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A Finger Grip Force Sensor with an Open-Pad Structure for Glove-Type Assistive Devices. SENSORS 2019; 20:s20010004. [PMID: 31861271 PMCID: PMC6982706 DOI: 10.3390/s20010004] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/17/2019] [Revised: 12/12/2019] [Accepted: 12/17/2019] [Indexed: 11/24/2022]
Abstract
This paper presents a fingertip grip force sensor based on custom capacitive sensors for glove-type assistive devices with an open-pad structure. The design of the sensor allows using human tactile sensations during grasping and manipulating an object. The proposed sensor can be attached on both sides of the fingertip and measure the force caused by the expansion of the fingertip tissue when a grasping force is applied to the fingertip. The number of measurable degrees of freedom (DoFs) are the two DoFs (flexion and adduction) for the thumb and one DoF (flexion) for the index and middle fingers. The proposed sensor allows the combination with a glove-type assistive device to measure the fingertip force. Calibration was performed for each finger joint angle because the variations in the expansion of the fingertip tissue depend on the joint angles. The root mean square error (RMSE) for fingertip force estimation ranged from 3.75% to 9.71% after calibration, regardless of the finger joint angles or finger posture.
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