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Grosmaire AG, Pila O, Breuckmann P, Duret C. Robot-assisted therapy for upper limb paresis after stroke: Use of robotic algorithms in advanced practice. NeuroRehabilitation 2022; 51:577-593. [PMID: 36530096 DOI: 10.3233/nre-220025] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
BACKGROUND Rehabilitation of stroke-related upper limb paresis is a major public health issue. OBJECTIVE Robotic systems have been developed to facilitate neurorehabilitation by providing key elements required to stimulate brain plasticity and motor recovery, namely repetitive, intensive, adaptative training with feedback. Although the positive effect of robot-assisted therapy on motor impairments has been well demonstrated, the effect on functional capacity is less certain. METHOD This narrative review outlines the principles of robot-assisted therapy for the rehabilitation of post-stroke upper limb paresis. RESULTS A paradigm is proposed to promote not only recovery of impairment but also function. CONCLUSION Further studies that would integrate some principles of the paradigm described in this paper are needed.
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Affiliation(s)
- Anne-Gaëlle Grosmaire
- Unité de Neurorééducation, Médecine Physique et de Réadaptation, Centre de Rééducation Fonctionnelle Les Trois Soleils, Boissise-Le-Roi, France
| | - Ophélie Pila
- Unité de Neurorééducation, Médecine Physique et de Réadaptation, Centre de Rééducation Fonctionnelle Les Trois Soleils, Boissise-Le-Roi, France
| | - Petra Breuckmann
- Unité de Neurorééducation, Médecine Physique et de Réadaptation, Centre de Rééducation Fonctionnelle Les Trois Soleils, Boissise-Le-Roi, France
| | - Christophe Duret
- Unité de Neurorééducation, Médecine Physique et de Réadaptation, Centre de Rééducation Fonctionnelle Les Trois Soleils, Boissise-Le-Roi, France
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Goffredo M, Proietti S, Pournajaf S, Galafate D, Cioeta M, Le Pera D, Posteraro F, Franceschini M. Baseline robot-measured kinematic metrics predict discharge rehabilitation outcomes in individuals with subacute stroke. Front Bioeng Biotechnol 2022; 10:1012544. [PMID: 36561043 PMCID: PMC9763272 DOI: 10.3389/fbioe.2022.1012544] [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: 08/05/2022] [Accepted: 11/16/2022] [Indexed: 12/12/2022] Open
Abstract
Background: The literature on upper limb robot-assisted therapy showed that robot-measured metrics can simultaneously predict registered clinical outcomes. However, only a limited number of studies correlated pre-treatment kinematics with discharge motor recovery. Given the importance of predicting rehabilitation outcomes for optimizing physical therapy, a predictive model for motor recovery that incorporates multidirectional indicators of a patient's upper limb abilities is needed. Objective: The aim of this study was to develop a predictive model for rehabilitation outcome at discharge (i.e., muscle strength assessed by the Motricity Index of the affected upper limb) based on multidirectional 2D robot-measured kinematics. Methods: Re-analysis of data from 66 subjects with subacute stroke who underwent upper limb robot-assisted therapy with an end-effector robot was performed. Two least squares error multiple linear regression models for outcome prediction were developed and differ in terms of validation procedure: the Split Sample Validation (SSV) model and the Leave-One-Out Cross-Validation (LOOCV) model. In both models, the outputs were the discharge Motricity Index of the affected upper limb and its sub-items assessing elbow flexion and shoulder abduction, while the inputs were the admission robot-measured metrics. Results: The extracted robot-measured features explained the 54% and 71% of the variance in clinical scores at discharge in the SSV and LOOCV validation procedures respectively. Normalized errors ranged from 22% to 35% in the SSV models and from 20% to 24% in the LOOCV models. In all models, the movement path error of the trajectories characterized by elbow flexion and shoulder extension was the significant predictor, and all correlations were significant. Conclusion: This study highlights that motor patterns assessed with multidirectional 2D robot-measured metrics are able to predict clinical evalutation of upper limb muscle strength and may be useful for clinicians to assess, manage, and program a more specific and appropriate rehabilitation in subacute stroke patients.
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Affiliation(s)
- Michela Goffredo
- Department of Neurological and Rehabilitation Sciences, IRCCS San Raffaele Roma, Rome, Italy
| | - Stefania Proietti
- Unit of Clinical and Molecular Epidemiology, IRCCS San Raffaele Roma, Rome, Italy,Department of Human Sciences and Promotion of the Quality of Life, San Raffaele University, Rome, Italy
| | - Sanaz Pournajaf
- Department of Neurological and Rehabilitation Sciences, IRCCS San Raffaele Roma, Rome, Italy,*Correspondence: Sanaz Pournajaf,
| | - Daniele Galafate
- Department of Neurological and Rehabilitation Sciences, IRCCS San Raffaele Roma, Rome, Italy
| | - Matteo Cioeta
- Department of Neurological and Rehabilitation Sciences, IRCCS San Raffaele Roma, Rome, Italy
| | - Domenica Le Pera
- Department of Neurological and Rehabilitation Sciences, IRCCS San Raffaele Roma, Rome, Italy
| | | | - Marco Franceschini
- Department of Neurological and Rehabilitation Sciences, IRCCS San Raffaele Roma, Rome, Italy,Department of Human Sciences and Promotion of the Quality of Life, San Raffaele University, Rome, Italy
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3
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Saes M, Mohamed Refai MI, van Beijnum BJF, Bussmann JBJ, Jansma EP, Veltink PH, Buurke JH, van Wegen EEH, Meskers CGM, Krakauer JW, Kwakkel G. Quantifying Quality of Reaching Movements Longitudinally Post-Stroke: A Systematic Review. Neurorehabil Neural Repair 2022; 36:183-207. [PMID: 35100897 PMCID: PMC8902693 DOI: 10.1177/15459683211062890] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/30/2023]
Abstract
Background Disambiguation of behavioral restitution from compensation is important to better understand recovery of upper limb motor control post-stroke and subsequently design better interventions. Measuring quality of movement (QoM) during standardized performance assays and functional tasks using kinematic and kinetic metrics potentially allows for this disambiguation. Objectives To identify longitudinal studies that used kinematic and/or kinetic metrics to investigate post-stroke recovery of reaching and assess whether these studies distinguish behavioral restitution from compensation. Methods A systematic literature search was conducted using the databases PubMed, Embase, Scopus, and Wiley/Cochrane Library up to July 1st, 2020. Studies were identified if they performed longitudinal kinematic and/or kinetic measurements during reaching, starting within the first 6 months post-stroke. Results Thirty-two longitudinal studies were identified, which reported a total of forty-six different kinematic metrics. Although the majority investigated improvements in kinetics or kinematics to quantify recovery of QoM, none of these studies explicitly addressed the distinction between behavioral restitution and compensation. One study obtained kinematic metrics for both performance assays and a functional task. Conclusions Despite the growing number of kinematic and kinetic studies on post-stroke recovery, longitudinal studies that explicitly seek to delineate between behavioral restitution and compensation are still lacking in the literature. To rectify this situation, future studies should measure kinematics and/or kinetics during performance assays to isolate restitution and during a standardized functional task to determine the contributions of restitution and compensation.
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Affiliation(s)
- M Saes
- Department of Rehabilitation Medicine, 1209Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam Movement Sciences, Amsterdam Neuroscience, Amsterdam, Netherlands
| | - M I Mohamed Refai
- Department of Biomedical Signals & Systems, Technical Medical Centre, 214825University of Twente, Enschede, Netherlands
| | - B J F van Beijnum
- Department of Biomedical Signals & Systems, Technical Medical Centre, 214825University of Twente, Enschede, Netherlands
| | - J B J Bussmann
- Department of Rehabilitation Medicine, Erasmus MC, University Medical Centre Rotterdam, Rotterdam, Netherlands
| | - E P Jansma
- Medical Library, 1190Vrije Universiteit Amsterdam, Amsterdam, Netherlands.,Department of Epidemiology and Biostatistics, Amsterdam Public Health Research Institute, Amsterdam UMC, Location VUmcAmsterdam, The Netherlands
| | - P H Veltink
- Department of Biomedical Signals & Systems, Technical Medical Centre, 214825University of Twente, Enschede, Netherlands
| | - J H Buurke
- Department of Biomedical Signals & Systems, Technical Medical Centre, 214825University of Twente, Enschede, Netherlands.,Rehabilitation Technology, Roessingh Research and Development, Enschede, Netherlands.,Department of Physical Therapy and Human Movement Sciences, Feinberg School of Medicine, 12244Northwestern University, Chicago, Il, USA
| | - E E H van Wegen
- Department of Rehabilitation Medicine, 1209Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam Movement Sciences, Amsterdam Neuroscience, Amsterdam, Netherlands
| | - C G M Meskers
- Department of Rehabilitation Medicine, 1209Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam Movement Sciences, Amsterdam Neuroscience, Amsterdam, Netherlands.,Department of Physical Therapy and Human Movement Sciences, Feinberg School of Medicine, 12244Northwestern University, Chicago, Il, USA
| | - J W Krakauer
- Departments of Neurology, Neuroscience and Physical Medicine and Rehabilitation, 1500Johns Hopkins University, Baltimore, MD, United States
| | - G Kwakkel
- Department of Rehabilitation Medicine, 1209Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam Movement Sciences, Amsterdam Neuroscience, Amsterdam, Netherlands.,Department of Physical Therapy and Human Movement Sciences, Feinberg School of Medicine, 12244Northwestern University, Chicago, Il, USA.,Department of Neurorehabilitation, 522567Amsterdam Rehabilitation Research Centre, Amsterdam, Netherlands
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Goffredo M, Pournajaf S, Proietti S, Gison A, Posteraro F, Franceschini M. Retrospective Robot-Measured Upper Limb Kinematic Data From Stroke Patients Are Novel Biomarkers. Front Neurol 2022; 12:803901. [PMID: 34992576 PMCID: PMC8725786 DOI: 10.3389/fneur.2021.803901] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2021] [Accepted: 11/17/2021] [Indexed: 02/06/2023] Open
Abstract
Background: The efficacy of upper-limb Robot-assisted Therapy (ulRT) in stroke subjects is well-established. The robot-measured kinematic data can assess the biomechanical changes induced by ulRT and the progress of patient over time. However, literature on the analysis of pre-treatment kinematic parameters as predictive biomarkers of upper limb recovery is limited. Objective: The aim of this study was to calculate pre-treatment kinematic parameters from point-to-point reaching movements in different directions and to identify biomarkers of upper-limb motor recovery in subacute stroke subjects after ulRT. Methods: An observational retrospective study was conducted on 66 subacute stroke subjects who underwent ulRT with an end-effector robot. Kinematic parameters were calculated from the robot-measured trajectories during movements in different directions. A Generalized Linear Model (GLM) was applied considering the post-treatment Upper Limb Motricity Index and the kinematic parameters (from demanding directions of movement) as dependent variables, and the pre-treatment kinematic parameters as independent variables. Results: A subset of kinematic parameters significantly predicted the motor impairment after ulRT: the accuracy in adduction and internal rotation movements of the shoulder was the major predictor of post-treatment Upper Limb Motricity Index. The post-treatment kinematic parameters of the most demanding directions of movement significantly depended on the ability to execute elbow flexion-extension and abduction and external rotation movements of the shoulder at baseline. Conclusions: The multidirectional analysis of robot-measured kinematic data predicts motor recovery in subacute stroke survivors and paves the way in identifying subjects who may benefit more from ulRT.
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Affiliation(s)
- Michela Goffredo
- Department of Neurological and Rehabilitation Sciences, IRCCS San Raffaele Roma, Rome, Italy
| | - Sanaz Pournajaf
- Department of Neurological and Rehabilitation Sciences, IRCCS San Raffaele Roma, Rome, Italy
| | - Stefania Proietti
- Department of Neurological and Rehabilitation Sciences, IRCCS San Raffaele Roma, Rome, Italy
| | - Annalisa Gison
- Department of Neurological and Rehabilitation Sciences, IRCCS San Raffaele Roma, Rome, Italy
| | - Federico Posteraro
- Rehabilitation Department, Versilia Hospital, Azienda Unità Sanitaria Locale (AUSL) Northwest Tuscany, Camaiore, Italy
| | - Marco Franceschini
- Department of Neurological and Rehabilitation Sciences, IRCCS San Raffaele Roma, Rome, Italy.,Department of Human Sciences and Promotion of the Quality of Life, San Raffaele University, Rome, Italy
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Lee JJ, Shin JH. Predicting Clinically Significant Improvement After Robot-Assisted Upper Limb Rehabilitation in Subacute and Chronic Stroke. Front Neurol 2021; 12:668923. [PMID: 34276535 PMCID: PMC8281036 DOI: 10.3389/fneur.2021.668923] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2021] [Accepted: 06/01/2021] [Indexed: 12/11/2022] Open
Abstract
Prior studies examining predictors of favorable clinical outcomes after upper limb robot-assisted therapy (RT) have many shortcomings. Therefore, the aim of this study was to identify meaningful predictors and a prediction model for clinically significant motor improvement in upper limb impairment after RT for each stroke phase. This retrospective, single-center study enrolled patients with stroke who received RT using InMotion2 along with conventional therapy (CT) from January 2015 to September 2019. Demographic characteristics, clinical measures, and robotic kinematic measures were evaluated. The primary outcome measure was the Fugl-Meyer Assessment-Upper Extremity (FMA-UE) and we classified patients with improvement more than the minimal clinically important difference as responders for each stroke phase. Univariable and multivariable logistic regression analyses were performed to assess the relationship between potential predictors and RT responders and determine meaningful predictors. Subsequently, meaningful predictors were included in the final prediction model. One hundred forty-four patients were enrolled. The Hand Movement Scale and time since onset were significant predictors of clinically significant improvement in upper limb impairment (P = 0.045 and 0.043, respectively), as represented by the FMA-UE score after RT along with CT, in patients with subacute stroke. These variables were also meaningful predictors with borderline statistical significance in patients with chronic stroke (P = 0.076 and 0.066, respectively). Better hand movement and a shorter time since onset can be used as realistic predictors of clinically significant motor improvement in upper limb impairment after RT with InMotion2 alongside CT in patients with subacute and chronic stroke. This information may help healthcare professionals discern optimal patients for RT and accurately inform patients and caregivers about outcomes of RT.
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Affiliation(s)
- Jae Joon Lee
- Department of Rehabilitation Medicine, National Rehabilitation Center, Ministry of Health and Welfare, Seoul, South Korea
| | - Joon-Ho Shin
- Department of Rehabilitation Medicine, National Rehabilitation Center, Ministry of Health and Welfare, Seoul, South Korea.,Translational Research Center for Rehabilitation Robots, National Rehabilitation Center, Ministry of Health and Welfare, Seoul, South Korea
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Goffredo M, Mazzoleni S, Gison A, Infarinato F, Pournajaf S, Galafate D, Agosti M, Posteraro F, Franceschini M. Kinematic Parameters for Tracking Patient Progress during Upper Limb Robot-Assisted Rehabilitation: An Observational Study on Subacute Stroke Subjects. Appl Bionics Biomech 2019; 2019:4251089. [PMID: 31772604 PMCID: PMC6854217 DOI: 10.1155/2019/4251089] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2019] [Revised: 08/02/2019] [Accepted: 08/14/2019] [Indexed: 12/03/2022] Open
Abstract
BACKGROUND Upper limb robot-assisted therapy (RT) provides intensive, repetitive, and task-specific treatment, and its efficacy for stroke survivors is well established in literature. Biomechanical data from robotic devices has been widely employed for patient's assessment, but rarely it has been analysed for tracking patient progress during RT. The goal of this retrospective study is to analyse built-in kinematic data registered by a planar end-effector robot for assessing the time course of motor recovery and patient's workspace exploration skills. A comparison of subjects having mild and severe motor impairment has been also conducted. For that purpose, kinematic data recorded by a planar end-effector robot have been processed for investigating how motor performance in executing point-to-point trajectories with different directions changes during RT. METHODS Observational retrospective study of 68 subacute stroke patients who conducted 20 daily sessions of upper limb RT with the InMotion 2.0 (Bionik Laboratories, USA): planar point-to-point reaching tasks with an "assist as needed" strategy. The following kinematic parameters (KPs) were computed for each subject and for each point-to-point trajectory executed during RT: movement accuracy, movement speed, number of peak speed, and task completion time. The Wilcoxon signed-rank tests were used with clinical outcomes. the Friedman test and post hoc Conover's test (Bonferroni's correction) were applied to KPs. A secondary data analysis has been conducted by comparing patients having different severities of motor impairment. The level of significance was set at p value < 0.05. RESULTS At the RT onset, the movements were less accurate and smoothed, and showed higher times of execution than those executed at the end of treatment. The analysis of the time course of KPs highlighted that RT seems to improve the motor function mainly in the first sessions of treatment: most KPs show significant intersession differences during the first 5/10 sessions. Afterwards, no further significant variations occurred. The ability to perform movements away from the body and from the hemiparetic side remains more challenging. The results obtained from the data stratification show significant differences between subjects with mild and severe motor impairment. CONCLUSION Significant improvements in motor performance were registered during the time course of upper limb RT in subacute stroke patients. The outcomes depend on movement direction and motor impairment and pave the way to optimize healthcare resources and to design patient-tailored rehabilitative protocols.
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Affiliation(s)
- Michela Goffredo
- Department of Neurorehabilitation, IRCCS San Raffaele Pisana, Rome, Italy
| | - Stefano Mazzoleni
- The BioRobotics Institute, Scuola Superiore Sant'Anna, Pisa, Italy
- Rehabilitation Bioengineering Laboratory, Volterra, Italy
| | - Annalisa Gison
- Department of Neurorehabilitation, IRCCS San Raffaele Pisana, Rome, Italy
| | | | - Sanaz Pournajaf
- Department of Neurorehabilitation, IRCCS San Raffaele Pisana, Rome, Italy
| | - Daniele Galafate
- Department of Neurorehabilitation, IRCCS San Raffaele Pisana, Rome, Italy
| | - Maurizio Agosti
- Rehabilitation Medicine Service, NHS-University Hospital of Parma, Parma, Italy
| | - Federico Posteraro
- Rehabilitation Department, Versilia Hospital, AUSL Tuscany North West, Camaiore, Italy
| | - Marco Franceschini
- Department of Neurorehabilitation, IRCCS San Raffaele Pisana, Rome, Italy
- San Raffaele University, Rome, Italy
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