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Lanzani V, Brambilla C, Scano A. Spinal maps in phasic and tonic EMG: Investigating intra-subject and inter-subject variability. Neuroscience 2025; 564:83-96. [PMID: 39557191 DOI: 10.1016/j.neuroscience.2024.11.043] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2024] [Revised: 10/21/2024] [Accepted: 11/15/2024] [Indexed: 11/20/2024]
Abstract
Reaching movements are essential for daily tasks and they have been widely investigated through kinematic, kinetic, and electromyographic (EMG) analyses. Recent studies have suggested that the central nervous system simplifies control of reaching movements by using muscle synergies. An alternative approach is to investigate how EMG activity reflects at theneural level with the representation of spinal maps that visualize the spatiotemporal activity of motoneuronal pools. Spinal maps have been rarely used and their investigation could be made by exploiting recent findings in EMG processing such as the separation of phasic (motion-related) and tonic components (anti-gravity). In this study, we aimed at characterizing spinal maps in the upper limb workspace. EMG data from 15 participants were recorded during repeated point-to-point movements toward target boards placed in five orientations. EMG waveforms were divided into total EMG envelope, tonic EMG, and phasic EMG. The multidimensional Pearson's correlation coefficient was used to assess thesimilarity of spinal maps among repetitions of movements within subjects (intra-subject variability) and among participants (inter-subject variability). Spinal maps of tonic and total EMG showed high intra- and inter-subject similarity in all planes, while phasic spinal maps were less repeatable and more subject-specific. These results may be useful as areference for rehabilitation, clinical, and neurological evaluations, especially for longitudinal assessments.
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Affiliation(s)
- Valentina Lanzani
- Institute of Intelligent Industrial Systems and Technologies for Advanced Manufacturing (STIIMA), Italian Council of National Research (CNR), Milan, Italy.
| | - Cristina Brambilla
- Institute of Intelligent Industrial Systems and Technologies for Advanced Manufacturing (STIIMA), Italian Council of National Research (CNR), Milan, Italy.
| | - Alessandro Scano
- Institute of Intelligent Industrial Systems and Technologies for Advanced Manufacturing (STIIMA), Italian Council of National Research (CNR), Milan, Italy.
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Buccilli B. Pediatric stroke: We need to look for it. J Neurol Sci 2024; 467:123276. [PMID: 39510868 DOI: 10.1016/j.jns.2024.123276] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2023] [Revised: 09/28/2024] [Accepted: 10/14/2024] [Indexed: 11/15/2024]
Abstract
PURPOSE This review provides a comprehensive overview of the characteristics and diagnosis of pediatric stroke, emphasizing the importance of early recognition and accurate assessment. Pediatric stroke is a complex condition with diverse etiologies, and its timely diagnosis is critical for initiating appropriate interventions and improving clinical outcomes. RECENT FINDINGS Recent advances in neuroimaging techniques, including magnetic resonance imaging (MRI) and magnetic resonance angiography (MRA), have significantly enhanced the diagnostic capabilities for pediatric stroke. Additionally, a better understanding of its underlying etiologies in specific cases, and of the importance of differential diagnosis have improved the outcome and prevention strategies in this vulnerable population. Despite these improvements, though, research still has a long way to go to optimize the management of this condition. SUMMARY Timely and accurate diagnosis of pediatric stroke remains a challenge due to its rarity and variability in clinical presentation, and to the presence of many mimic conditions. The integration of clinical evaluation, neuroimaging, and comorbidities analysis is crucial for achieving a precise diagnosis and guiding tailored treatment strategies for affected children.
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Affiliation(s)
- Barbara Buccilli
- Icahn School of Medicine at Mount Sinai, Department of Neurosurgery, 1 Gustave L. Levy Place, New York, NY 10029-6574, United States of America
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3
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Buccilli B. Exploring new horizons: Emerging therapeutic strategies for pediatric stroke. Exp Neurol 2024; 374:114701. [PMID: 38278205 DOI: 10.1016/j.expneurol.2024.114701] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2023] [Revised: 12/31/2023] [Accepted: 01/23/2024] [Indexed: 01/28/2024]
Abstract
Pediatric stroke presents unique challenges, and optimizing treatment strategies is essential for improving outcomes in this vulnerable population. This review aims to provide an overview of new, innovative, and potential treatments for pediatric stroke, with a primary objective to stimulate further research in this field. Our review highlights several promising approaches in the realm of pediatric stroke management, including but not limited to stem cell therapy and robotic rehabilitation. These innovative interventions offer new avenues for enhancing functional recovery, reducing long-term disability, and tailoring treatments to individual patient needs. The findings of this review underscore the importance of ongoing research and development of innovative treatments in pediatric stroke. These advancements hold significant clinical relevance, offering the potential to improve the lives of children affected by stroke by enhancing the precision, efficacy, and accessibility of therapeutic interventions. Embracing these innovations is essential in our pursuit of better outcomes and a brighter future for pediatric stroke care.
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Affiliation(s)
- Barbara Buccilli
- Icahn School of Medicine at Mount Sinai, Department of Neurosurgery, 1 Gustave L. Levy Pl, New York, NY 10029, United States of America.
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4
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Mitsopoulos K, Fiska V, Tagaras K, Papias A, Antoniou P, Nizamis K, Kasimis K, Sarra PD, Mylopoulou D, Savvidis T, Praftsiotis A, Arvanitidis A, Lyssas G, Chasapis K, Moraitopoulos A, Astaras A, Bamidis PD, Athanasiou A. NeuroSuitUp: System Architecture and Validation of a Motor Rehabilitation Wearable Robotics and Serious Game Platform. SENSORS (BASEL, SWITZERLAND) 2023; 23:3281. [PMID: 36991992 PMCID: PMC10053382 DOI: 10.3390/s23063281] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Revised: 03/04/2023] [Accepted: 03/14/2023] [Indexed: 06/19/2023]
Abstract
BACKGROUND This article presents the system architecture and validation of the NeuroSuitUp body-machine interface (BMI). The platform consists of wearable robotics jacket and gloves in combination with a serious game application for self-paced neurorehabilitation in spinal cord injury and chronic stroke. METHODS The wearable robotics implement a sensor layer, to approximate kinematic chain segment orientation, and an actuation layer. Sensors consist of commercial magnetic, angular rate and gravity (MARG), surface electromyography (sEMG), and flex sensors, while actuation is achieved through electrical muscle stimulation (EMS) and pneumatic actuators. On-board electronics connect to a Robot Operating System environment-based parser/controller and to a Unity-based live avatar representation game. BMI subsystems validation was performed using exercises through a Stereoscopic camera Computer Vision approach for the jacket and through multiple grip activities for the glove. Ten healthy subjects participated in system validation trials, performing three arm and three hand exercises (each 10 motor task trials) and completing user experience questionnaires. RESULTS Acceptable correlation was observed in 23/30 arm exercises performed with the jacket. No significant differences in glove sensor data during actuation state were observed. No difficulty to use, discomfort, or negative robotics perception were reported. CONCLUSIONS Subsequent design improvements will implement additional absolute orientation sensors, MARG/EMG based biofeedback to the game, improved immersion through Augmented Reality and improvements towards system robustness.
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Affiliation(s)
- Konstantinos Mitsopoulos
- Medical Physics & Digital Innovation Laboratory, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
| | - Vasiliki Fiska
- Medical Physics & Digital Innovation Laboratory, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
| | - Konstantinos Tagaras
- Medical Physics & Digital Innovation Laboratory, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
| | - Athanasios Papias
- Medical Physics & Digital Innovation Laboratory, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
| | - Panagiotis Antoniou
- Medical Physics & Digital Innovation Laboratory, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
| | - Konstantinos Nizamis
- Department of Design, Production and Management, University of Twente, 7522 NB Enschede, The Netherlands
| | - Konstantinos Kasimis
- Department of Physiotherapy, International Hellenic University, 57400 Thessaloniki, Greece
| | - Paschalina-Danai Sarra
- Medical Physics & Digital Innovation Laboratory, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
| | - Diamanto Mylopoulou
- Medical Physics & Digital Innovation Laboratory, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
| | - Theodore Savvidis
- Medical Physics & Digital Innovation Laboratory, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
| | - Apostolos Praftsiotis
- Medical Physics & Digital Innovation Laboratory, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
| | - Athanasios Arvanitidis
- Medical Physics & Digital Innovation Laboratory, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
| | - George Lyssas
- Medical Physics & Digital Innovation Laboratory, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
| | - Konstantinos Chasapis
- Medical Physics & Digital Innovation Laboratory, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
| | - Alexandros Moraitopoulos
- Medical Physics & Digital Innovation Laboratory, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
| | - Alexander Astaras
- Department of Computer Science, American College of Thessaloniki, 55535 Thessaloniki, Greece
| | - Panagiotis D. Bamidis
- Medical Physics & Digital Innovation Laboratory, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
| | - Alkinoos Athanasiou
- Medical Physics & Digital Innovation Laboratory, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
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Gonzalez A, Garcia L, Kilby J, McNair P. Robotic devices for paediatric rehabilitation: a review of design features. Biomed Eng Online 2021; 20:89. [PMID: 34488777 PMCID: PMC8420060 DOI: 10.1186/s12938-021-00920-5] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2021] [Accepted: 08/06/2021] [Indexed: 01/11/2023] Open
Abstract
Children with physical disabilities often have limited performance in daily activities, hindering their physical development, social development and mental health. Therefore, rehabilitation is essential to mitigate the adverse effects of the different causes of physical disabilities and improve independence and quality of life. In the last decade, robotic rehabilitation has shown the potential to augment traditional physical rehabilitation. However, to date, most robotic rehabilitation devices are designed for adult patients who differ in their needs compared to paediatric patients, limiting the devices' potential because the paediatric patients' needs are not adequately considered. With this in mind, the current work reviews the existing literature on robotic rehabilitation for children with physical disabilities, intending to summarise how the rehabilitation robots could fulfil children's needs and inspire researchers to develop new devices. A literature search was conducted utilising the Web of Science, PubMed and Scopus databases. Based on the inclusion-exclusion criteria, 206 publications were included, and 58 robotic devices used by children with a physical disability were identified. Different design factors and the treated conditions using robotic technology were compared. Through the analyses, it was identified that weight, safety, operability and motivation were crucial factors to the successful design of devices for children. The majority of the current devices were used for lower limb rehabilitation. Neurological disorders, in particular cerebral palsy, were the most common conditions for which devices were designed. By far, the most common actuator was the electric motor. Usually, the devices present more than one training strategy being the assistive strategy the most used. The admittance/impedance method is the most popular to interface the robot with the children. Currently, there is a trend on developing exoskeletons, as they can assist children with daily life activities outside of the rehabilitation setting, propitiating a wider adoption of the technology. With this shift in focus, it appears likely that new technologies to actuate the system (e.g. serial elastic actuators) and to detect the intention (e.g. physiological signals) of children as they go about their daily activities will be required.
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Affiliation(s)
- Alberto Gonzalez
- BioDesign Lab, School of Engineering, Computer and Mathematical Sciences, Auckland University of Technology, Auckland, New Zealand
| | - Lorenzo Garcia
- BioDesign Lab, School of Engineering, Computer and Mathematical Sciences, Auckland University of Technology, Auckland, New Zealand.
| | - Jeff Kilby
- BioDesign Lab, School of Engineering, Computer and Mathematical Sciences, Auckland University of Technology, Auckland, New Zealand
| | - Peter McNair
- Health and Rehabilitation Research Institute, Auckland University of Technology, Auckland, New Zealand
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Converging Robotic Technologies in Targeted Neural Rehabilitation: A Review of Emerging Solutions and Challenges. SENSORS 2021; 21:s21062084. [PMID: 33809721 PMCID: PMC8002299 DOI: 10.3390/s21062084] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/12/2021] [Revised: 03/05/2021] [Accepted: 03/11/2021] [Indexed: 11/17/2022]
Abstract
Recent advances in the field of neural rehabilitation, facilitated through technological innovation and improved neurophysiological knowledge of impaired motor control, have opened up new research directions. Such advances increase the relevance of existing interventions, as well as allow novel methodologies and technological synergies. New approaches attempt to partially overcome long-term disability caused by spinal cord injury, using either invasive bridging technologies or noninvasive human-machine interfaces. Muscular dystrophies benefit from electromyography and novel sensors that shed light on underlying neuromotor mechanisms in people with Duchenne. Novel wearable robotics devices are being tailored to specific patient populations, such as traumatic brain injury, stroke, and amputated individuals. In addition, developments in robot-assisted rehabilitation may enhance motor learning and generate movement repetitions by decoding the brain activity of patients during therapy. This is further facilitated by artificial intelligence algorithms coupled with faster electronics. The practical impact of integrating such technologies with neural rehabilitation treatment can be substantial. They can potentially empower nontechnically trained individuals-namely, family members and professional carers-to alter the programming of neural rehabilitation robotic setups, to actively get involved and intervene promptly at the point of care. This narrative review considers existing and emerging neural rehabilitation technologies through the perspective of replacing or restoring functions, enhancing, or improving natural neural output, as well as promoting or recruiting dormant neuroplasticity. Upon conclusion, we discuss the future directions for neural rehabilitation research, diagnosis, and treatment based on the discussed technologies and their major roadblocks. This future may eventually become possible through technological evolution and convergence of mutually beneficial technologies to create hybrid solutions.
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Wen Y, Avrillon S, Hernandez-Pavon JC, Kim SJ, Hug F, Pons JL. A convolutional neural network to identify motor units from high-density surface electromyography signals in real time. J Neural Eng 2021; 18. [PMID: 33721852 DOI: 10.1088/1741-2552/abeead] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2020] [Accepted: 03/15/2021] [Indexed: 11/11/2022]
Abstract
OBJECTIVES This paper aims to investigate the feasibility and the validity of applying deep convolutional neural networks (CNN) to identify motor unit (MU) spike trains and estimate the neural drive to muscles from high-density electromyography (HD-EMG) signals in real time. Two distinct deep CNNs are compared with the convolution kernel compensation (CKC) algorithm using simulated and experimentally recorded signals. The effects of window size and step size of the input HD-EMG signals are also investigated. APPROACH The MU spike trains were first identified with the CKC algorithm. The HD-EMG signals and spike trains were used to train the deep CNN. Then, the deep CNN decomposed the HD-EMG signals into MU discharge times in real time. Two CNN approaches are compared with the CKC: 1) multiple single-output deep CNN (SO-DCNN) with one MU decomposed per network, and 2) one multiple-output deep CNN (MO-DCNN) to decompose all MUs (up to 23) with one network. MAIN RESULTS The MO-DCNN outperformed the SO-DCNN in terms of training time (3.2 to 21.4 s/epoch vs. 6.5 to 47.8 s/epoch, respectively) and prediction time (0.04 vs. 0.27 s/sample, respectively). The optimal window size and step size for MO-DCNN were 120 and 20 data points, respectively. It results in sensitivity of 98% and 85% with simulated and experimentally recorded HD-EMG signals, respectively. There is a high cross-correlation coefficient between the neural drive estimated with CKC and that estimated with MO-DCNN (range of r-value across conditions: 0.88-0.95). SIGNIFICANCE We demonstrate the feasibility and the validity of using deep CNN to accurately identify MU activity from HD-EMG with a latency lower than 80 ms, which falls within the lower bound of the human electromechanical delay. This method opens many opportunities for using the neural drive to interface humans with assistive devices.
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Affiliation(s)
- Yue Wen
- Legs and Walking Lab, Shirley Ryan AbilityLab, 355 East Erie Street, Chicago, Illinois, 60611-2654, UNITED STATES
| | - Simon Avrillon
- Shirley Ryan AbilityLab, 355 E Erie St, Chicago, Illinois, 60601, UNITED STATES
| | - Julio Cesar Hernandez-Pavon
- Department of Physical Medicine and Rehabilitation, Northwestern University Feinberg School of Medicine, 251 E Huron St, Chicago, Illinois, 60611, UNITED STATES
| | - Sangjoon Jonathan Kim
- Shirley Ryan AbilityLab, 355 E Erie St, Chicago, Illinois, 60611-2654, UNITED STATES
| | - Francois Hug
- Laboratoire 'Motricite, Interactions, Performance', Universite de Nantes, JE 2438 UFRSTAPS,, 25 bis Guy Mollet BP 72206, Nantes, F-44000 France, Nantes, 72206, FRANCE
| | - Jose Luis Pons
- Bioengineering Group, Spanish Research Council, Serrano 117, Arganda del Rey (Madrid), 28006, SPAIN
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Nizamis K, Rijken NHM, van Middelaar R, Neto J, Koopman BFJM, Sartori M. Characterization of Forearm Muscle Activation in Duchenne Muscular Dystrophy via High-Density Electromyography: A Case Study on the Implications for Myoelectric Control. Front Neurol 2020; 11:231. [PMID: 32351441 PMCID: PMC7174775 DOI: 10.3389/fneur.2020.00231] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2019] [Accepted: 03/11/2020] [Indexed: 12/26/2022] Open
Abstract
Duchenne muscular dystrophy (DMD) is a genetic disorder that results in progressive muscular degeneration. Although medical advances increased their life expectancy, DMD individuals are still highly dependent on caregivers. Hand/wrist function is central for providing independence, and robotic exoskeletons are good candidates for effectively compensating for deteriorating functionality. Robotic hand exoskeletons require the accurate decoding of motor intention typically via surface electromyography (sEMG). Traditional low-density sEMG was used in the past to explore the muscular activations of individuals with DMD; however, it cannot provide high spatial resolution. This study characterized, for the first time, the forearm high-density (HD) electromyograms of three individuals with DMD while performing seven hand/wrist-related tasks and compared them to eight healthy individuals (all data available online). We looked into the spatial distribution of HD-sEMG patterns by using principal component analysis (PCA) and also assessed the repeatability and the amplitude distributions of muscle activity. Additionally, we used a machine learning approach to assess DMD individuals' potentials for myocontrol. Our analysis showed that although participants with DMD were able to repeat similar HD-sEMG patterns across gestures (similarly to healthy participants), a fewer number of electrodes was activated during their gestures compared to the healthy participants. Additionally, participants with DMD activated their muscles close to maximal contraction level (0.63 ± 0.23), whereas healthy participants had lower normalized activations (0.26 ± 0.2). Lastly, participants with DMD showed on average fewer PCs (3), explaining 90% of the complete gesture space than the healthy (5). However, the ability of the DMD participants to produce repeatable HD-sEMG patterns was unexpectedly comparable to that of healthy participants, and the same holds true for their offline myocontrol performance, disproving our hypothesis and suggesting a clear potential for the myocontrol of wearable exoskeletons. Our findings present evidence for the first time on how DMD leads to progressive alterations in hand/wrist motor control in DMD individuals compared to healthy. The better understanding of these alterations can lead to further developments for the intuitive and robust myoelectric control of active hand exoskeletons for individuals with DMD.
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Affiliation(s)
- Kostas Nizamis
- Department of Biomechanical Engineering, Technical Medical Centre, University of Twente, Enschede, Netherlands
| | - Noortje H M Rijken
- Faculty Physical Activity and Health, Saxion University of Applied Sciences, Enschede, Netherlands
| | - Robbert van Middelaar
- Department of Biomechanical Engineering, Technical Medical Centre, University of Twente, Enschede, Netherlands
| | - João Neto
- Faculty of Sciences, University of Lisbon, Lisbon, Portugal
| | - Bart F J M Koopman
- Department of Biomechanical Engineering, Technical Medical Centre, University of Twente, Enschede, Netherlands
| | - Massimo Sartori
- Department of Biomechanical Engineering, Technical Medical Centre, University of Twente, Enschede, Netherlands
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Bos RA, Nizamis K, Koopman BFJM, Herder JL, Sartori M, Plettenburg DH. A Case Study With Symbihand: An sEMG-Controlled Electrohydraulic Hand Orthosis for Individuals With Duchenne Muscular Dystrophy. IEEE Trans Neural Syst Rehabil Eng 2019; 28:258-266. [PMID: 31825868 DOI: 10.1109/tnsre.2019.2952470] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
With recent improvements in healthcare, individuals with Duchenne muscular dystrophy (DMD) have prolonged life expectancy, and it is therefore vital to preserve their independence. Hand function plays a central role in maintaining independence in daily living. This requires sufficient grip force and the ability to modulate it with no substantially added effort. Individuals with DMD have low residual grip force and its modulation is challenging and fatiguing. To assist their hand function, we developed a novel dynamic hand orthosis called SymbiHand, where the user's hand motor intention is decoded by means of surface electromyography, enabling the control of an electrohydraulic pump for actuation. Mechanical work is transported using hydraulic transmission and flexible structures to redirect interaction forces, enhancing comfort by minimizing shear forces. This paper outlines SymbiHand's design and control, and a case study with an individual with DMD. Results show that SymbiHand increased the participant's maximum grasping force from 2.4 to 8 N. During a grasping force-tracking task, muscular activation was decreased by more than 40% without compromising task performance. These results suggest that SymbiHand has the potential to decrease muscular activation and increase grasping force for individuals with DMD, adding to the hand a total mass of no more than 241 g. Changes in mass distributions and an active thumb support are necessary for improved usability, in addition to larger-scale studies for generalizing its assistive potential.
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