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Crocher V, Brock K, Simondson J, Klaic M, Galea MP. Robotic task specific training for upper limb neurorehabilitation: a mixed methods feasibility trial reporting achievable dose. Disabil Rehabil 2024:1-9. [PMID: 39189418 DOI: 10.1080/09638288.2024.2394175] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2024] [Revised: 07/23/2024] [Accepted: 08/15/2024] [Indexed: 08/28/2024]
Abstract
PURPOSE Robotic devices for upper-limb neurorehabilitation allow an increase in intensity of practice, often relying on video game-based training strategies with limited capacity to individualise training and integrate functional training. This study shows the development of a robotic Task Specific Training (TST) protocol and evaluate the achieved dose. MATERIALS AND METHODS Mixed-method study. A 3D robotic device for the upper limb, was made available to therapists for use during neurorehabilitation sessions. A first phase allowed clinicians to define a dedicated session protocol for TST. In a second phase the protocol was applied and the achieved dose was measured. RESULTS First phase (N = 5): a specific protocol, using deweighting for assessment, followed by customised passive movements and then active movement practice was developed. Second phase: the protocol was successfully applied with all participants (N = 10). Intervention duration: 4.5 ± 0.8 weeks, session frequency: 1.4 ± 0.2sessions/week, session length: 42 ± 9mins, session density: 39 ± 13%, intensity: 214 ± 84 movements/session, difficulty: dn = 0.77 ± 0.1 (normalised reaching distance) and Ɵ = 6.3 ± 23° (transverse reaching angle). Sessions' density and intensity were consistent across participants but clear differences of difficulty were observed. No changes in metrics were observed over the intervention. CONCLUSIONS Robotic systems can support TST with high therapy intensity by modulating the practice difficulty to participants' needs and capabilities.
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Affiliation(s)
- Vincent Crocher
- Department of Mechanical Engineering, The University of Melbourne, Melbourne, Australia
| | - Kim Brock
- St Vincent's Hospital, Melbourne, Australia
| | | | - Marlena Klaic
- Melbourne School of Health Sciences, University of Melbourne, Melbourne, Australia
- Allied Health Department, The Royal Melbourne Hospital, Melbourne, Australia
| | - Mary P Galea
- Department of Medicine (Royal Melbourne Hospital), The University of Melbourne, Melbourne, Australia
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Cai G, Xu J, Zhang C, Jiang J, Chen G, Chen J, Liu Q, Xu G, Lan Y. Identifying biomarkers related to motor function in chronic stroke: A fNIRS and TMS study. CNS Neurosci Ther 2024; 30:e14889. [PMID: 39073240 DOI: 10.1111/cns.14889] [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: 02/03/2024] [Revised: 06/07/2024] [Accepted: 07/17/2024] [Indexed: 07/30/2024] Open
Abstract
BACKGROUND Upper limb motor impairment commonly occurs after stroke, impairing quality of life. Brain network reorganization likely differs between subgroups with differing impairment severity. This study explored differences in functional connectivity (FC) and corticospinal tract (CST) integrity between patients with mild/moderate versus severe hemiplegia poststroke to clarify the neural correlates underlying motor deficits. METHOD Sixty chronic stroke patients with upper limb motor impairment were categorized into mild/moderate and severe groups based on Fugl-Meyer scores. Resting-state FC was assessed using functional near-infrared spectroscopy (fNIRS) to compare connectivity patterns between groups across motor regions. CST integrity was evaluated by inducing motor evoked potentials (MEP) via transcranial magnetic stimulation. RESULTS Compared to the mild/moderate group, the severe group exhibited heightened premotor cortex-primary motor cortex (PMC-M1) connectivity (t = 4.56, p < 0.01). Absence of MEP was also more frequent in the severe group (χ2 = 12.31, p = 0.01). Bayesian models effectively distinguished subgroups and identified the PMC-M1 connection as highly contributory (accuracy = 91.30%, area under the receiver operating characteristic curve [AUC] = 0.86). CONCLUSION Distinct patterns of connectivity and corticospinal integrity exist between stroke subgroups with differing impairments. Strengthened connectivity potentially indicates recruitment of additional motor resources to compensate for damage. These findings elucidate the neural correlates underlying motor deficits poststroke and could guide personalized, network-based therapies targeting predictive biomarkers to improve rehabilitation outcomes.
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Affiliation(s)
- Guiyuan Cai
- Department of Rehabilitation Medicine, School of Medicine, The Second Affiliated Hospital, South China University of Technology, Guangzhou, China
- Guangzhou First People's Hospital, Guangzhou Medical University, Guangzhou, China
| | - Jiayue Xu
- Department of Rehabilitation Medicine, School of Medicine, The Second Affiliated Hospital, South China University of Technology, Guangzhou, China
- Guangzhou First People's Hospital, Guangzhou Medical University, Guangzhou, China
| | - Cailing Zhang
- Department of Rehabilitation Medicine, School of Medicine, The Second Affiliated Hospital, South China University of Technology, Guangzhou, China
- Guangzhou First People's Hospital, Guangzhou Medical University, Guangzhou, China
| | - Junbo Jiang
- Department of Rehabilitation Medicine, School of Medicine, The Second Affiliated Hospital, South China University of Technology, Guangzhou, China
- Guangzhou First People's Hospital, Guangzhou Medical University, Guangzhou, China
| | - Gengbin Chen
- Postgraduate Research Institute, Guangzhou Sport University, Guangzhou, China
| | - Jialin Chen
- Postgraduate Research Institute, Guangzhou Sport University, Guangzhou, China
| | - Quan Liu
- Postgraduate Research Institute, Guangzhou Sport University, Guangzhou, China
| | - Guangqing Xu
- Department of Rehabilitation Medicine, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China
| | - Yue Lan
- Department of Rehabilitation Medicine, School of Medicine, The Second Affiliated Hospital, South China University of Technology, Guangzhou, China
- Guangzhou First People's Hospital, Guangzhou Medical University, Guangzhou, China
- Guangzhou Key Laboratory of Aging Frailty and Neurorehabilitation, Guangzhou, China
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Moon JH, Kim J, Hwang Y, Jang S, Kim J. Novel evaluation of upper-limb motor performance after stroke based on normal reaching movement model. J Neuroeng Rehabil 2023; 20:66. [PMID: 37226265 DOI: 10.1186/s12984-023-01189-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2022] [Accepted: 05/10/2023] [Indexed: 05/26/2023] Open
Abstract
BACKGROUND Upper-limb rehabilitation robots provide repetitive reaching movement training to post-stroke patients. Beyond a pre-determined set of movements, a robot-aided training protocol requires optimization to account for the individuals' unique motor characteristics. Therefore, an objective evaluation method should consider the pre-stroke motor performance of the affected arm to compare one's performance relative to normalcy. However, no study has attempted to evaluate performance based on an individual's normal performance. Herein, we present a novel method for evaluating upper limb motor performance after a stroke based on a normal reaching movement model. METHODS To represent the normal reaching performance of individuals, we opted for three candidate models: (1) Fitts' law for the speed-accuracy relationship, (2) the Almanji model for the mouse-pointing task of cerebral palsy, and (3) our proposed model. We first obtained the kinematic data of healthy (n = 12) and post-stroke (n = 7) subjects with a robot to validate the model and evaluation method and conducted a pilot study with a group of post-stroke patients (n = 12) in a clinical setting. Using the models obtained from the reaching performance of the less-affected arm, we predicted the patients' normal reaching performance to set the standard for evaluating the affected arm. RESULTS We verified that the proposed normal reaching model identifies the reaching of all healthy (n = 12) and less-affected arm (n = 19; 16 of them showed an R2 > 0.7) but did not identify erroneous reaching of the affected arm. Furthermore, our evaluation method intuitively and visually demonstrated the unique motor characteristics of the affected arms. CONCLUSIONS The proposed method can be used to evaluate an individual's reaching characteristics based on an individuals normal reaching model. It has the potential to provide individualized training by prioritizing a set of reaching movements.
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Affiliation(s)
- James Hyungsup Moon
- School of Mechanical Engineering, Sungkyunkwan University, Suwon-Si, Gyeonggi-Do, 16419, Republic of Korea
| | - Jongbum Kim
- Department of Robotics and Mechatronics Engineering, Daegu Gyeongbuk Institute of Science and Technology (DGIST), Daegu, 42988, Republic of Korea
| | - Yeji Hwang
- School of Mechanical Engineering, Sungkyunkwan University, Suwon-Si, Gyeonggi-Do, 16419, Republic of Korea
| | - Sungho Jang
- Department of Physical Medicine and Rehabilitation, College of Medicine, Yeungnam University, Daegu, 42415, Republic of Korea
| | - Jonghyun Kim
- School of Mechanical Engineering, Sungkyunkwan University, Suwon-Si, Gyeonggi-Do, 16419, Republic of Korea.
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Forbrigger S, Liblong M, Davies TC, DePaul V, Morin E, Hashtrudi-Zaad K. Considerations for at-home upper-limb rehabilitation technology following stroke: Perspectives of stroke survivors and therapists. J Rehabil Assist Technol Eng 2023; 10:20556683231171840. [PMID: 37124709 PMCID: PMC10134106 DOI: 10.1177/20556683231171840] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/02/2023] Open
Abstract
Introduction This study investigated the needs of stroke survivors and therapists, and how they may contrast, for the design of robots for at-home post stroke rehabilitation therapy, in the Ontario, Canada, context. Methods Individual interviews were conducted with stroke survivors (n = 10) and therapists (n = 6). The transcripts were coded using thematic analysis inspired by the WHO International Classification of Functioning, Disability, and Health. Results Design recommendations, potential features, and barriers were identified from the interviews. Stroke survivors and therapists agreed on many of the needs for at-home robotic rehabilitation; however, stroke survivors had more insights into their home environment, barriers, and needs relating to technology, while therapists had more insights into therapy methodology and patient safety and interaction. Both groups felt a one-size-fits-all approach to rehabilitation robot design is inappropriate. Designs could address a broader range of impairments by incorporating household items and breaking activities down into their component motions. Designs should incorporate hand and wrist supports and activities. Designs should monitor trunk and shoulder motion and consider incorporating group activities. Conclusion While therapists can provide insight in the early stages of design of rehabilitation technology, stroke survivors' perspectives are crucial to designing for the home environment.
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Affiliation(s)
- Shane Forbrigger
- Department of Electrical and Computer Engineering, Queen’s University, Kingston, ON, Canada
- Keyvan Hashtrudi-Zaad, Department of Electrical and Computer Engineering, Queen’s University, 19 Union St, Kingston, ON K7L 3N9, Canada. Email:
| | - Madeleine Liblong
- Department of Mechanical Engineering, Queen’s University, Kingston, ON, Canada
| | - TC Davies
- Department of Mechanical Engineering, Queen’s University, Kingston, ON, Canada
| | - Vincent DePaul
- School of Rehabilitation Therapy, Queen’s University, Kingston, ON, Canada
| | - Evelyn Morin
- Department of Electrical and Computer Engineering, Queen’s University, Kingston, ON, Canada
| | - Keyvan Hashtrudi-Zaad
- Department of Electrical and Computer Engineering, Queen’s University, Kingston, ON, Canada
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Bressi F, Cricenti L, Campagnola B, Bravi M, Miccinilli S, Santacaterina F, Sterzi S, Straudi S, Agostini M, Paci M, Casanova E, Marino D, La Rosa G, Giansanti D, Perrero L, Battistini A, Filoni S, Sicari M, Petrozzino S, Solaro CM, Gargano S, Benanti P, Boldrini P, Bonaiuti D, Castelli E, Draicchio F, Falabella V, Galeri S, Gimigliano F, Grigioni M, Mazzoleni S, Mazzon S, Molteni F, Petrarca M, Picelli A, Posteraro F, Senatore M, Turchetti G, Morone G, Gallotti M, Germanotta M, Aprile I. Effects of robotic upper limb treatment after stroke on cognitive patterns: A systematic review. NeuroRehabilitation 2022; 51:541-558. [PMID: 36530099 PMCID: PMC9837692 DOI: 10.3233/nre-220149] [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] [Indexed: 12/23/2022]
Abstract
BACKGROUND Robotic therapy (RT) has been internationally recognized for the motor rehabilitation of the upper limb. Although it seems that RT can stimulate and promote neuroplasticity, the effectiveness of robotics in restoring cognitive deficits has been considered only in a few recent studies. OBJECTIVE To verify whether, in the current state of the literature, cognitive measures are used as inclusion or exclusion criteria and/or outcomes measures in robotic upper limb rehabilitation in stroke patients. METHODS The systematic review was conducted according to PRISMA guidelines. Studies eligible were identified through PubMed/MEDLINE and Web of Science from inception to March 2021. RESULTS Eighty-one studies were considered in this systematic review. Seventy-three studies have at least a cognitive inclusion or exclusion criteria, while only seven studies assessed cognitive outcomes. CONCLUSION Despite the high presence of cognitive instruments used for inclusion/exclusion criteria their heterogeneity did not allow the identification of a guideline for the evaluation of patients in different stroke stages. Therefore, although the heterogeneity and the low percentage of studies that included cognitive outcomes, seemed that the latter were positively influenced by RT in post-stroke rehabilitation. Future larger RCTs are needed to outline which cognitive scales are most suitable and their cut-off, as well as what cognitive outcome measures to use in the various stages of post-stroke rehabilitation.
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Affiliation(s)
- Federica Bressi
- Physical Medicine and Rehabilitation Unit, Campus Bio-Medico University Polyclinic Foundation, Rome, Italy
| | - Laura Cricenti
- Physical Medicine and Rehabilitation Unit, Campus Bio-Medico University Polyclinic Foundation, Rome, Italy
| | - Benedetta Campagnola
- Physical Medicine and Rehabilitation Unit, Campus Bio-Medico University Polyclinic Foundation, Rome, Italy,Address for correspondence: Benedetta Campagnola, Physical Medicine and Rehabilitation Unit, Campus Bio-Medico University Polyclinic Foundation, Rome, Italy. E-mail:
| | - Marco Bravi
- Physical Medicine and Rehabilitation Unit, Campus Bio-Medico University Polyclinic Foundation, Rome, Italy
| | - Sandra Miccinilli
- Physical Medicine and Rehabilitation Unit, Campus Bio-Medico University Polyclinic Foundation, Rome, Italy
| | - Fabio Santacaterina
- Physical Medicine and Rehabilitation Unit, Campus Bio-Medico University Polyclinic Foundation, Rome, Italy
| | - Silvia Sterzi
- Physical Medicine and Rehabilitation Unit, Campus Bio-Medico University Polyclinic Foundation, Rome, Italy
| | - Sofia Straudi
- Department of Neuroscience and Rehabilitation, Ferrara University Hospital, Ferrara, Italy
| | | | - Matteo Paci
- AUSL (Unique Sanitary Local Company) District of Central Tuscany, Florence, Italy
| | - Emanuela Casanova
- Unità Operativa di Medicina Riabilitativa e Neuroriabilitazione (SC), IRCCS Istituto delle Scienze Neurologiche di Bologna, Bologna, Italy
| | - Dario Marino
- IRCCS Neurolysis Center “Bonino Pulejo”, Messina, Italy
| | | | - Daniele Giansanti
- National Center for Innovative Technologies in Public Health, Italian National Institute of Health, Rome, Italy
| | - Luca Perrero
- Neurorehabilitation Unit, Azienda Ospedaliera Nazionale SS. Antonio e Biagio e Cesare Arrigo, Alessandria, Italy
| | - Alberto Battistini
- Unità Operativa di Medicina Riabilitativa e Neuroriabilitazione (SC), IRCCS Istituto delle Scienze Neurologiche di Bologna, Bologna, Italy
| | - Serena Filoni
- Padre Pio Onlus Rehabilitation Centers Foundation, San Giovanni Rotondo, Italy
| | - Monica Sicari
- A.O.U. Città della Salute e della Scienza di Torino, Turin, Italy
| | | | | | | | | | - Paolo Boldrini
- Società Italiana di Medicina Fisica e Riabilitativa (SIMFER), Rome, Italy
| | | | - Enrico Castelli
- Department of Paediatric Neurorehabilitation, IRCCS Bambino Gesù Children’s Hospital, Rome, Italy
| | - Francesco Draicchio
- Department of Occupational and Environmental Medicine, Epidemiology and Hygiene, INAIL, Rome, Italy
| | - Vincenzo Falabella
- Italian Federation of Persons with Spinal Cord Injuries (Faip Onlus), Rome, Italy
| | | | - Francesca Gimigliano
- Department of Mental, Physical Health and Preventive Medicine, University of Campania “Luigi Vanvitelli”, Naples, Italy
| | - Mauro Grigioni
- National Center for Innovative Technologies in Public Health, Italian National Institute of Health, Rome, Italy
| | - Stefano Mazzoleni
- Department of Electrical and Information Engineering, Politecnico di Bari, Bari, Italy
| | - Stefano Mazzon
- AULSS6 (Unique Sanitary Local Company) Euganea Padova – Distretto 4 “Alta Padovana”, Padua, Italy
| | - Franco Molteni
- Department of Rehabilitation Medicine, Villa Beretta Rehabilitation Center, Valduce Hospital, Lecco, Italy
| | - Maurizio Petrarca
- Movement Analysis and Robotics Laboratory (MARlab), IRCCS Bambino Gesù Children’s Hospital, Rome, Italy
| | - Alessandro Picelli
- Department of Neurosciences, Biomedicine and Movement Sciences, University of Verona, Verona, Italy
| | - Federico Posteraro
- Department of Rehabilitation, Versilia Hospital – AUSL12, Viareggio, Italy
| | - Michele Senatore
- Associazione Italiana dei Terapisti Occupazionali (AITO), Rome, Italy
| | | | | | | | | | - Irene Aprile
- IRCCS Fondazione Don Carlo Gnocchi, Florence, Italy
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Frisoli A, Barsotti M, Sotgiu E, Lamola G, Procopio C, Chisari C. A randomized clinical control study on the efficacy of three-dimensional upper limb robotic exoskeleton training in chronic stroke. J Neuroeng Rehabil 2022; 19:14. [PMID: 35120546 PMCID: PMC8817500 DOI: 10.1186/s12984-022-00991-y] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2021] [Accepted: 01/21/2022] [Indexed: 11/10/2022] Open
Abstract
Background Although robotics assisted rehabilitation has proven to be effective in stroke rehabilitation, a limited functional improvements in Activities of Daily Life has been also observed after the administration of robotic training. To this aim in this study we compare the efficacy in terms of both clinical and functional outcomes of a robotic training performed with a multi-joint functional exoskeleton in goal-oriented exercises compared to a conventional physical therapy program, equally matched in terms of intensity and time. As a secondary goal of the study, it was assessed the capability of kinesiologic measurements—extracted by the exoskeleton robotic system—of predicting the rehabilitation outcomes using a set of robotic biomarkers collected at the baseline.
Methods A parallel-group randomized clinical trial was conducted within a group of 26 chronic post-stroke patients. Patients were randomly assigned to two groups receiving robotic or manual therapy. The primary outcome was the change in score on the upper extremity section of the Fugl-Meyer Assessment (FMA) scale. As secondary outcome a specifically designed bimanual functional scale, Bimanual Activity Test (BAT), was used for upper limb functional evaluation. Two robotic performance indices were extracted with the purpose of monitoring the recovery process and investigating the interrelationship between pre-treatment robotic biomarkers and post-treatment clinical improvement in the robotic group. Results A significant clinical and functional improvements in both groups (p < 0.01) was reported. More in detail a significantly higher improvement of the robotic group was observed in the proximal portion of the FMA (p < 0.05) and in the reduction of time needed for accomplishing the tasks of the BAT (p < 0.01). The multilinear-regression analysis pointed out a significant correlation between robotic biomarkers at the baseline and change in FMA score (R2 = 0.91, p < 0.05), suggesting their potential ability of predicting clinical outcomes. Conclusion Exoskeleton-based robotic upper limb treatment might lead to better functional outcomes, if compared to manual physical therapy. The extracted robotic performance could represent predictive indices of the recovery of the upper limb. These results are promising for their potential exploitation in implementing personalized robotic therapy. Clinical Trial Registration clinicaltrials.gov, NCT03319992 Unique Protocol ID: RH-UL-LEXOS-10. Registered 20.10.2017, https://clinicaltrials.gov/ct2/show/NCT03319992
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Affiliation(s)
- Antonio Frisoli
- Institute of Mechanical Intelligence, Scuola Superiore Sant'Anna of Pisa, PERCRO Lab, Via Alamanni, 13b, San Giuliano Terme, Ghezzano, 56010, Pisa, Italy.
| | - Michele Barsotti
- Institute of Mechanical Intelligence, Scuola Superiore Sant'Anna of Pisa, PERCRO Lab, Via Alamanni, 13b, San Giuliano Terme, Ghezzano, 56010, Pisa, Italy
| | - Edoardo Sotgiu
- INL-International Iberian Nanotechnology Laboratory, Braga, Portugal
| | | | - Caterina Procopio
- Institute of Mechanical Intelligence, Scuola Superiore Sant'Anna of Pisa, PERCRO Lab, Via Alamanni, 13b, San Giuliano Terme, Ghezzano, 56010, Pisa, Italy
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Trabacca A, Lucarelli E, Losito L. Moving towards precision neurological rehabilitation: a mandatory path to follow in the era of precision neurology. Neurol Sci 2021; 42:3889-3891. [PMID: 34046796 DOI: 10.1007/s10072-021-05349-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2021] [Accepted: 05/21/2021] [Indexed: 10/21/2022]
Affiliation(s)
- Antonio Trabacca
- Unit for Severe Disabilities in Developmental Age and Young Adults (Developmental Neurology and Neurorehabilitation), Scientific Institute IRCCS "E. Medea", Ex Complesso Ospedaliero "A. Di Summa" - Piazza "A. Di Summa", 72100, Brindisi, Italy.
| | - Elisabetta Lucarelli
- Unit for Severe Disabilities in Developmental Age and Young Adults (Developmental Neurology and Neurorehabilitation), Scientific Institute IRCCS "E. Medea", Ex Complesso Ospedaliero "A. Di Summa" - Piazza "A. Di Summa", 72100, Brindisi, Italy
| | - Luciana Losito
- Unit for Severe Disabilities in Developmental Age and Young Adults (Developmental Neurology and Neurorehabilitation), Scientific Institute IRCCS "E. Medea", Ex Complesso Ospedaliero "A. Di Summa" - Piazza "A. Di Summa", 72100, Brindisi, Italy
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8
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Effects of a Soft Robotic Hand for Hand Rehabilitation in Chronic Stroke Survivors. J Stroke Cerebrovasc Dis 2021; 30:105812. [PMID: 33895427 DOI: 10.1016/j.jstrokecerebrovasdis.2021.105812] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2020] [Revised: 03/11/2021] [Accepted: 04/02/2021] [Indexed: 11/21/2022] Open
Abstract
OBJECTIVES Soft robotic hands are proposed for stroke rehabilitation in terms of their high compliance and low inherent stiffness. We investigated the clinical efficacy of a soft robotic hand that could actively flex and extend the fingers in chronic stroke subjects with different levels of spasticity. METHODS Sixteen chronic stroke subjects were recruited into this single-group study. Subjects underwent 20 sessions of 1-hour EMG-driven soft robotic hand training. Training effect was evaluated by the pre-training and post-training assessments with the clinical scores: Action Research Arm Test(ARAT), Fugl-Meyer Assessment for Upper Extremity(FMA-UE), Box-and-Block test(BBT), Modified Ashworth Scale(MAS), and maximum voluntary grip strength. RESULTS For all the recruited subjects (n = 16), significant improvement of upper limb function was generally observed in ARAT (increased mean=2.44, P = 0.032), FMA-UE (increased mean=3.31, P = 0.003), BBT (increased mean=1.81, P = 0.024), and maximum voluntary grip strength (increased mean=2.14 kg, P < 0.001). No significant change was observed in terms of spasticity with the MAS (decreased mean=0.11, P = 0.423). Further analysis showed subjects with mild or no finger flexor spasticity (MAS<2, n = 9) at pre-training had significant improvement of upper limb function after 20 sessions of training. However, for subjects with moderate and severe finger flexor spasticity (MAS=2,3, n = 7) at pre-training, no significant change in clinical scores was shown and only maximum voluntary grip strength had significant increase. CONCLUSION EMG-driven rehabilitation training using the soft robotic hand with flexion and extension could be effective for the functional recovery of upper limb in chronic stroke subjects with mild or no spasticity.
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Adans-Dester C, Hankov N, O’Brien A, Vergara-Diaz G, Black-Schaffer R, Zafonte R, Dy J, Lee SI, Bonato P. Enabling precision rehabilitation interventions using wearable sensors and machine learning to track motor recovery. NPJ Digit Med 2020; 3:121. [PMID: 33024831 PMCID: PMC7506010 DOI: 10.1038/s41746-020-00328-w] [Citation(s) in RCA: 33] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2019] [Accepted: 08/12/2020] [Indexed: 01/19/2023] Open
Abstract
The need to develop patient-specific interventions is apparent when one considers that clinical studies often report satisfactory motor gains only in a portion of participants. This observation provides the foundation for "precision rehabilitation". Tracking and predicting outcomes defining the recovery trajectory is key in this context. Data collected using wearable sensors provide clinicians with the opportunity to do so with little burden on clinicians and patients. The approach proposed in this paper relies on machine learning-based algorithms to derive clinical score estimates from wearable sensor data collected during functional motor tasks. Sensor-based score estimates showed strong agreement with those generated by clinicians. Score estimates of upper-limb impairment severity and movement quality were marked by a coefficient of determination of 0.86 and 0.79, respectively. The application of the proposed approach to monitoring patients' responsiveness to rehabilitation is expected to contribute to the development of patient-specific interventions, aiming to maximize motor gains.
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Affiliation(s)
- Catherine Adans-Dester
- Department of Physical Medicine & Rehabilitation, Harvard Medical School, Spaulding Rehabilitation Hospital, Boston, MA USA
- School of Health & Rehabilitation Sciences, MGH Institute of Health Professions, Boston, MA USA
| | - Nicolas Hankov
- Department of Physical Medicine & Rehabilitation, Harvard Medical School, Spaulding Rehabilitation Hospital, Boston, MA USA
| | - Anne O’Brien
- Department of Physical Medicine & Rehabilitation, Harvard Medical School, Spaulding Rehabilitation Hospital, Boston, MA USA
| | - Gloria Vergara-Diaz
- Department of Physical Medicine & Rehabilitation, Harvard Medical School, Spaulding Rehabilitation Hospital, Boston, MA USA
| | - Randie Black-Schaffer
- Department of Physical Medicine & Rehabilitation, Harvard Medical School, Spaulding Rehabilitation Hospital, Boston, MA USA
| | - Ross Zafonte
- Department of Physical Medicine & Rehabilitation, Harvard Medical School, Spaulding Rehabilitation Hospital, Boston, MA USA
| | - Jennifer Dy
- Department of Electrical and Computer Engineering, Northeastern University, Boston, MA USA
| | - Sunghoon I. Lee
- College of Information and Computer Sciences, University of Massachusetts Amherst, Amherst, MA USA
| | - Paolo Bonato
- Department of Physical Medicine & Rehabilitation, Harvard Medical School, Spaulding Rehabilitation Hospital, Boston, MA USA
- Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA USA
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10
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Giang C, Pirondini E, Kinany N, Pierella C, Panarese A, Coscia M, Miehlbradt J, Magnin C, Nicolo P, Guggisberg A, Micera S. Motor improvement estimation and task adaptation for personalized robot-aided therapy: a feasibility study. Biomed Eng Online 2020; 19:33. [PMID: 32410617 PMCID: PMC7227346 DOI: 10.1186/s12938-020-00779-y] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2019] [Accepted: 05/08/2020] [Indexed: 02/07/2023] Open
Abstract
BACKGROUND In the past years, robotic systems have become increasingly popular in upper limb rehabilitation. Nevertheless, clinical studies have so far not been able to confirm superior efficacy of robotic therapy over conventional methods. The personalization of robot-aided therapy according to the patients' individual motor deficits has been suggested as a pivotal step to improve the clinical outcome of such approaches. METHODS Here, we present a model-based approach to personalize robot-aided rehabilitation therapy within training sessions. The proposed method combines the information from different motor performance measures recorded from the robot to continuously estimate patients' motor improvement for a series of point-to-point reaching movements in different directions. Additionally, it comprises a personalization routine to automatically adapt the rehabilitation training. We engineered our approach using an upper-limb exoskeleton. The implementation was tested with 17 healthy subjects, who underwent a motor-adaptation paradigm, and two subacute stroke patients, exhibiting different degrees of motor impairment, who participated in a pilot test undergoing rehabilitative motor training. RESULTS The results of the exploratory study with healthy subjects showed that the participants divided into fast and slow adapters. The model was able to correctly estimate distinct motor improvement progressions between the two groups of participants while proposing individual training protocols. For the two pilot patients, an analysis of the selected motor performance measures showed that both patients were able to retain the improvements gained during training when reaching movements were reintroduced at a later stage. These results suggest that the automated training adaptation was appropriately timed and specifically tailored to the abilities of each individual. CONCLUSIONS The results of our exploratory study demonstrated the feasibility of the proposed model-based approach for the personalization of robot-aided rehabilitation therapy. The pilot test with two subacute stroke patients further supported our approach, while providing encouraging results for the applicability in clinical settings. Trial registration This study is registered in ClinicalTrials.gov (NCT02770300, registered 30 March 2016, https://clinicaltrials.gov/ct2/show/NCT02770300).
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Affiliation(s)
- Christian Giang
- Bertarelli Foundation Chair in Translational Neuroengineering, Center for Neuroprosthetics and Institute of Bioengineering, School of Engineering, École Polytechnique Fédérale de Lausanne (EPFL), 1015 Lausanne, Switzerland
| | - Elvira Pirondini
- Institute of Bioengineering/Center for Neuroprosthetics, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
- Department of Radiology and Medical Informatics, University of Geneva, Geneva, Switzerland
| | - Nawal Kinany
- Bertarelli Foundation Chair in Translational Neuroengineering, Center for Neuroprosthetics and Institute of Bioengineering, School of Engineering, École Polytechnique Fédérale de Lausanne (EPFL), 1015 Lausanne, Switzerland
- Institute of Bioengineering/Center for Neuroprosthetics, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
- Department of Radiology and Medical Informatics, University of Geneva, Geneva, Switzerland
| | - Camilla Pierella
- Bertarelli Foundation Chair in Translational Neuroengineering, Center for Neuroprosthetics and Institute of Bioengineering, School of Engineering, École Polytechnique Fédérale de Lausanne (EPFL), 1015 Lausanne, Switzerland
| | - Alessandro Panarese
- Translational Neural Engineering Area, The Biorobotics Institute, Scuola Superiore Sant’Anna, 56025 Pisa, Italy
| | - Martina Coscia
- Wyss Center for Bio- and Neuro-Engineering, 1202 Geneva, Switzerland
| | - Jenifer Miehlbradt
- Brain Electrophysiology Attention Movement Laboratory, Institute of Psychology, University of Lausanne, Lausanne, Switzerland
| | - Cécile Magnin
- Division of Neurorehabilitation, Department of Clinical Neurosciences, University Hospital Geneva, Geneva, Switzerland
| | - Pierre Nicolo
- Division of Neurorehabilitation, Department of Clinical Neurosciences, University Hospital Geneva, Geneva, Switzerland
- Laboratory of Cognitive Neurorehabilitation, Department of Clinical Neurosciences, Medical School, University of Geneva, Geneva, Switzerland
| | - Adrian Guggisberg
- Division of Neurorehabilitation, Department of Clinical Neurosciences, University Hospital Geneva, Geneva, Switzerland
- Laboratory of Cognitive Neurorehabilitation, Department of Clinical Neurosciences, Medical School, University of Geneva, Geneva, Switzerland
| | - Silvestro Micera
- Bertarelli Foundation Chair in Translational Neuroengineering, Center for Neuroprosthetics and Institute of Bioengineering, School of Engineering, École Polytechnique Fédérale de Lausanne (EPFL), 1015 Lausanne, Switzerland
- Translational Neural Engineering Area, The Biorobotics Institute, Scuola Superiore Sant’Anna, 56025 Pisa, Italy
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Rosenthal O, Wing AM, Wyatt JL, Punt D, Brownless B, Ko-Ko C, Miall RC. Correction to: Boosting robot-assisted rehabilitation of stroke hemiparesis by individualized selection of upper limb movements – a pilot study. J Neuroeng Rehabil 2019; 16:51. [PMID: 30987648 PMCID: PMC6466732 DOI: 10.1186/s12984-019-0521-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2019] [Accepted: 04/02/2019] [Indexed: 11/22/2022] Open
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Reinkensmeyer DJ. JNER at 15 years: analysis of the state of neuroengineering and rehabilitation. J Neuroeng Rehabil 2019; 16:144. [PMID: 31744511 PMCID: PMC6864952 DOI: 10.1186/s12984-019-0610-0] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2019] [Accepted: 10/16/2019] [Indexed: 11/10/2022] Open
Abstract
On JNER's 15th anniversary, this editorial analyzes the state of the field of neuroengineering and rehabilitation. I first discuss some ways that the nature of neurorehabilitation research has evolved in the past 15 years based on my perspective as editor-in-chief of JNER and a researcher in the field. I highlight increasing reliance on advanced technologies, improved rigor and openness of research, and three, related, new paradigms - wearable devices, the Cybathlon competition, and human augmentation studies - indicators that neurorehabilitation is squarely in the age of wearability. Then, I briefly speculate on how the field might make progress going forward, highlighting the need for new models of training and learning driven by big data, better personalization and targeting, and an increase in the quantity and quality of usability and uptake studies to improve translation.
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Affiliation(s)
- David J Reinkensmeyer
- Department of Mechanical and Aerospace Engineering, University of California at Irvine, California, USA. .,Department of Anatomy and Neurobiology, University of California at Irvine, California, USA. .,Department of Biomedical Engineering, University of California at Irvine, California, USA. .,Department of Physical Medicine and Rehabilitation, University of California at Irvine, California, USA.
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