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Abedi M, Behzadipour S. A novel biomechanical index for quality assessment of the upper-extremity movements in post-stroke patients. Comput Biol Med 2024; 179:108875. [PMID: 39018881 DOI: 10.1016/j.compbiomed.2024.108875] [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: 01/02/2024] [Revised: 06/22/2024] [Accepted: 07/09/2024] [Indexed: 07/19/2024]
Abstract
BACKGROUND While motor recovery is preferred to compensatory movements for stroke patients with mild to moderate motion impairment, current movement quality assessments rarely reflect the differences between a patient's pre- and post-stroke movement patterns. Such comparison can help therapists to identify the rate of the restoration of premorbid motion patterns and prescribe the most effective treatment. METHODS This paper attempted to present a new biomechanical metric for the quality of upper-limb movements which uses the subject's optimal movements as a reference to evaluate his/her UL movement quality. To this end, an inverse optimal control algorithm was applied to find an estimation of the patient's premorbid motion patterns. The new biomechanical index was then calculated as a measure of similarity between the optimal and actual movement trajectories. In the next part, various simulation and clinimetric investigations were performed to evaluate the responses of the new index to variations of the movement quality as well as its test-retest reliability and concurrent validity. RESULTS Simulation-based analyses demonstrated that the proposed index, in contrast to the previous popular biomechanical indices, can successfully detect a wide range of abnormalities in motion signals. In addition, it showed good test-retest reliability (ICC = 0.89) and moderate correlation with clinical indices, Fugl-Meyer Assessment (r = 0.66), Action Research Arm Test (r = 0.47), and ABILHAND (r = 0.27). CONCLUSIONS Although the proposed index has the same degree of clinimetric properties as the previous metrics, the ability to identify the level of movement restoration and also various types and severities of motor disabilities may lead to better design and management of motor rehabilitation.
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Affiliation(s)
- Majid Abedi
- Mechanical Engineering Department, Sharif University of Technology, Tehran, Iran; Djawad Movafaghian Research Center in Rehab Technologies, Sharif University of Technology, Tehran, Iran
| | - Saeed Behzadipour
- Mechanical Engineering Department, Sharif University of Technology, Tehran, Iran; Djawad Movafaghian Research Center in Rehab Technologies, Sharif University of Technology, Tehran, Iran.
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Tong W, Yue W, Chen F, Shi W, Zhang L, Wan J. MSE-VGG: A Novel Deep Learning Approach Based on EEG for Rapid Ischemic Stroke Detection. SENSORS (BASEL, SWITZERLAND) 2024; 24:4234. [PMID: 39001013 PMCID: PMC11244239 DOI: 10.3390/s24134234] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/30/2024] [Revised: 06/12/2024] [Accepted: 06/27/2024] [Indexed: 07/16/2024]
Abstract
Ischemic stroke is a type of brain dysfunction caused by pathological changes in the blood vessels of the brain which leads to brain tissue ischemia and hypoxia and ultimately results in cell necrosis. Without timely and effective treatment in the early time window, ischemic stroke can lead to long-term disability and even death. Therefore, rapid detection is crucial in patients with ischemic stroke. In this study, we developed a deep learning model based on fusion features extracted from electroencephalography (EEG) signals for the fast detection of ischemic stroke. Specifically, we recruited 20 ischemic stroke patients who underwent EEG examination during the acute phase of stroke and collected EEG signals from 19 adults with no history of stroke as a control group. Afterwards, we constructed correlation-weighted Phase Lag Index (cwPLI), a novel feature, to explore the synchronization information and functional connectivity between EEG channels. Moreover, the spatio-temporal information from functional connectivity and the nonlinear information from complexity were fused by combining the cwPLI matrix and Sample Entropy (SaEn) together to further improve the discriminative ability of the model. Finally, the novel MSE-VGG network was employed as a classifier to distinguish ischemic stroke from non-ischemic stroke data. Five-fold cross-validation experiments demonstrated that the proposed model possesses excellent performance, with accuracy, sensitivity, and specificity reaching 90.17%, 89.86%, and 90.44%, respectively. Experiments on time consumption verified that the proposed method is superior to other state-of-the-art examinations. This study contributes to the advancement of the rapid detection of ischemic stroke, shedding light on the untapped potential of EEG and demonstrating the efficacy of deep learning in ischemic stroke identification.
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Affiliation(s)
- Wei Tong
- School of Information and Electronic Engineering, Zhejiang University of Science and Technology, Hangzhou 310023, China; (W.T.); (W.S.); (L.Z.); (J.W.)
| | - Weiqi Yue
- School of Computer Science, Hangzhou Dianzi University, Hangzhou 310018, China;
| | - Fangni Chen
- School of Information and Electronic Engineering, Zhejiang University of Science and Technology, Hangzhou 310023, China; (W.T.); (W.S.); (L.Z.); (J.W.)
| | - Wei Shi
- School of Information and Electronic Engineering, Zhejiang University of Science and Technology, Hangzhou 310023, China; (W.T.); (W.S.); (L.Z.); (J.W.)
| | - Lei Zhang
- School of Information and Electronic Engineering, Zhejiang University of Science and Technology, Hangzhou 310023, China; (W.T.); (W.S.); (L.Z.); (J.W.)
| | - Jian Wan
- School of Information and Electronic Engineering, Zhejiang University of Science and Technology, Hangzhou 310023, China; (W.T.); (W.S.); (L.Z.); (J.W.)
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Scano A, Lanzani V, Brambilla C, d’Avella A. Transferring Sensor-Based Assessments to Clinical Practice: The Case of Muscle Synergies. SENSORS (BASEL, SWITZERLAND) 2024; 24:3934. [PMID: 38931719 PMCID: PMC11207859 DOI: 10.3390/s24123934] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/20/2024] [Revised: 06/10/2024] [Accepted: 06/15/2024] [Indexed: 06/28/2024]
Abstract
Sensor-based assessments in medical practice and rehabilitation include the measurement of physiological signals such as EEG, EMG, ECG, heart rate, and NIRS, and the recording of movement kinematics and interaction forces. Such measurements are commonly employed in clinics with the aim of assessing patients' pathologies, but so far some of them have found full exploitation mainly for research purposes. In fact, even though the data they allow to gather may shed light on physiopathology and mechanisms underlying motor recovery in rehabilitation, their practical use in the clinical environment is mainly devoted to research studies, with a very reduced impact on clinical practice. This is especially the case for muscle synergies, a well-known method for the evaluation of motor control in neuroscience based on multichannel EMG recordings. In this paper, considering neuromotor rehabilitation as one of the most important scenarios for exploiting novel methods to assess motor control, the main challenges and future perspectives for the standard clinical adoption of muscle synergy analysis are reported and critically discussed.
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Affiliation(s)
- Alessandro Scano
- Institute of Intelligent Industrial Systems and Technologies for Advanced Manufacturing (STIIMA), Italian Council of National Research (CNR), 20133 Milan, Italy; (V.L.); (C.B.)
| | - Valentina Lanzani
- Institute of Intelligent Industrial Systems and Technologies for Advanced Manufacturing (STIIMA), Italian Council of National Research (CNR), 20133 Milan, Italy; (V.L.); (C.B.)
| | - Cristina Brambilla
- Institute of Intelligent Industrial Systems and Technologies for Advanced Manufacturing (STIIMA), Italian Council of National Research (CNR), 20133 Milan, Italy; (V.L.); (C.B.)
| | - Andrea d’Avella
- Laboratory of Neuromotor Physiology, IRCCS Fondazione Santa Lucia, Via Ardeatina 306-354, 00179 Rome, Italy;
- Department of Biology, University of Rome Tor Vergata, Via della Ricerca Scientifica, 00133 Rome, Italy
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Marin-Pardo O, Donnelly MR, Phanord CS, Wong K, Liew SL. Improvements in motor control are associated with improved quality of life following an at-home muscle biofeedback program for chronic stroke. Front Hum Neurosci 2024; 18:1356052. [PMID: 38818030 PMCID: PMC11138207 DOI: 10.3389/fnhum.2024.1356052] [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: 12/14/2023] [Accepted: 04/29/2024] [Indexed: 06/01/2024] Open
Abstract
Introduction Chronic stroke survivors with severe arm impairment have limited options for effective rehabilitation. High intensity, repetitive task practice (RTP) is known to improve upper limb function among stroke survivors who have some volitional muscle activation. However, clients without volitional movement of their arm are ineligible for RTP-based interventions and require hands-on facilitation from a clinician or robotic therapy to simulate task practice. Such approaches can be expensive, burdensome, and have marginal effects. Alternatively, supervised at-home telerehabilitation using muscle biofeedback may provide a more accessible, affordable, and effective rehabilitation option for stroke survivors with severe arm impairment, and could potentially help people with severe stroke regain enough volitional activation to be eligible for RTP-types of therapies. Feedback of muscle activity via electromyography (EMG) has been previously used with clients who have minimal or no movement to improve functional performance. Specifically, training to reduce unintended co-contractions of the impaired hand using EMG biofeedback may modestly improve motor control in people with limited movement. Importantly, these modest and covert functional changes may influence the perceived impact of stroke-related disability in daily life. In this manuscript, we examine whether physical changes following use of a portable EMG biofeedback system (Tele-REINVENT) for severe upper limb hemiparesis also relate to perceived quality of life improvements. Secondarily, we examined the effects of Tele-REINVENT, which uses EMG to quantify antagonistic muscle activity during movement attempt trials and transform individuated action into computer game control, on several different domains of stroke recovery. Methods For this pilot study, nine stroke survivors (age = 37-73 years) with chronic impairment (Fugl-Meyer = 14-40/66) completed 30 1-hour sessions of home-based training, consisting of six weeks of gaming that reinforced wrist extensor muscle activity while attenuating coactivation of flexor muscles. To assess motor control and performance, we measured changes in active wrist ranges of motion, the Fugl-Meyer Assessment, and Action Research Arm Test. We also collected an EMG-based test of muscle control to examine more subtle changes. To examine changes in perceived quality of life, we utilized the Stroke Impact Scale along with participant feedback. Results Results from our pilot data suggest that 30 sessions of remote training can induce modest changes on clinical and functional assessments, showing a statistically significant improvement of active wrist ranges of motion at the group level, changes that could allow some people with severe stroke to be eligible for other therapeutic approaches, such as RTP. Additionally, changes in motor control were correlated with the perceived impact of stroke on participation and impairment after training. We also report changes in corticomuscular coherence, which showed a laterality change from the ipsilesional motor cortex towards the contralesional hemisphere during wrist extension attempts. Finally, all participants showed high adherence to the protocol and reported enjoying using the system. Conclusion Overall, Tele-REINVENT represents a promising telerehabilitation intervention that might improve sensorimotor outcomes in severe chronic stroke, and that improving sensorimotor abilities even modestly may improve quality of life. We propose that Tele-REINVENT may be used as a precursor to help participants gain enough active movement to participate other occupational therapy interventions, such as RTP. Future work is needed to examine if home-based telerehabilitation to provide feedback of individuated muscle activity could increase meaningful rehabilitation accessibility and outcomes for underserved populations.
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Affiliation(s)
- Octavio Marin-Pardo
- Chan Division of Occupational Science and Occupational Therapy, University of Southern California, Los Angeles, CA, United States
| | - Miranda Rennie Donnelly
- Chan Division of Occupational Science and Occupational Therapy, University of Southern California, Los Angeles, CA, United States
| | - Coralie S. Phanord
- Chan Division of Occupational Science and Occupational Therapy, University of Southern California, Los Angeles, CA, United States
| | - Kira Wong
- Chan Division of Occupational Science and Occupational Therapy, University of Southern California, Los Angeles, CA, United States
| | - Sook-Lei Liew
- Chan Division of Occupational Science and Occupational Therapy, University of Southern California, Los Angeles, CA, United States
- Stevens Neuroimaging and Neuroinformatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
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Li X, Zeng H, Li Y, Song A. Quantitative Assessment via Multi-Domain Fusion of Muscle Synergy Associated With Upper-Limb Motor Function for Stroke Rehabilitation. IEEE Trans Biomed Eng 2024; 71:1430-1441. [PMID: 38051628 DOI: 10.1109/tbme.2023.3339634] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/07/2023]
Abstract
Quantitative assessment of upper limb motor function aids therapists in providing appropriate rehabilitation strategies, which plays an essential role in post-stroke rehabilitation. Traditional assessments, relying on clinical scales or kinematic metrics, often involve subjective scores or are influenced by compensatory strategies. Recently, the use of muscle synergies, representing simplified neuromuscular control, has emerged as a promising approach for post-stroke assessment. In general, muscle synergies are decomposed into two components: synergy vectors and synergy activation. Synergy vectors represent the relative weighting of each muscle within each synergy, that is muscle coordination; synergy activation represents the recruitment of the muscle synergy over time, that is muscle activation strength. Both components are vital for adequately assessing patients' motor function. Therefore, we integrate the spatial domain and temporal domain features extracted from synergy vectors and synergy activation, constructing a multi-domain assessment system using a Random Forest classifier, which may provide great qualitative classification accuracy. Furthermore, a novel functional score is generated from the probabilities belonging to the pathological group. Finally, A study involving ten healthy subjects and ten post-stroke patients validates the proposed method. The experimental results show that the classification accuracy was enhanced to 98.56% by fusing the characteristics derived from different domains, which was higher than that based on spatial domain (94.90%) and temporal domain (91.08%), respectively. Furthermore, the assessment score generated by multi-domain fusion framework exhibited a significant correlation with the clinical score. These promising results show the potential of applying the proposed method to clinical assessments for post-stroke patients.
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Freitas M, Pinho F, Pinho L, Silva S, Figueira V, Vilas-Boas JP, Silva A. Biomechanical Assessment Methods Used in Chronic Stroke: A Scoping Review of Non-Linear Approaches. SENSORS (BASEL, SWITZERLAND) 2024; 24:2338. [PMID: 38610549 PMCID: PMC11014015 DOI: 10.3390/s24072338] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/16/2024] [Revised: 03/22/2024] [Accepted: 04/04/2024] [Indexed: 04/14/2024]
Abstract
Non-linear and dynamic systems analysis of human movement has recently become increasingly widespread with the intention of better reflecting how complexity affects the adaptability of motor systems, especially after a stroke. The main objective of this scoping review was to summarize the non-linear measures used in the analysis of kinetic, kinematic, and EMG data of human movement after stroke. PRISMA-ScR guidelines were followed, establishing the eligibility criteria, the population, the concept, and the contextual framework. The examined studies were published between 1 January 2013 and 12 April 2023, in English or Portuguese, and were indexed in the databases selected for this research: PubMed®, Web of Science®, Institute of Electrical and Electronics Engineers®, Science Direct® and Google Scholar®. In total, 14 of the 763 articles met the inclusion criteria. The non-linear measures identified included entropy (n = 11), fractal analysis (n = 1), the short-term local divergence exponent (n = 1), the maximum Floquet multiplier (n = 1), and the Lyapunov exponent (n = 1). These studies focused on different motor tasks: reaching to grasp (n = 2), reaching to point (n = 1), arm tracking (n = 2), elbow flexion (n = 5), elbow extension (n = 1), wrist and finger extension upward (lifting) (n = 1), knee extension (n = 1), and walking (n = 4). When studying the complexity of human movement in chronic post-stroke adults, entropy measures, particularly sample entropy, were preferred. Kinematic assessment was mainly performed using motion capture systems, with a focus on joint angles of the upper limbs.
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Affiliation(s)
- Marta Freitas
- Escola Superior de Saúde do Vale do Ave, Cooperativa de Ensino Superior Politécnico e Universitário, Rua José António Vidal, 81, 4760-409 Vila Nova de Famalicão, Portugal; (F.P.); (L.P.); (S.S.); (V.F.)
- HM—Health and Human Movement Unit, Polytechnic University of Health, Cooperativa de Ensino Superior Politécnico e Universitário, CRL, 4760-409 Vila Nova de Famalicão, Portugal
- Center for Rehabilitation Research (CIR), R. Dr. António Bernardino de Almeida 400, 4200-072 Porto, Portugal;
- Porto Biomechanics Laboratory (LABIOMEP), 4200-450 Porto, Portugal
| | - Francisco Pinho
- Escola Superior de Saúde do Vale do Ave, Cooperativa de Ensino Superior Politécnico e Universitário, Rua José António Vidal, 81, 4760-409 Vila Nova de Famalicão, Portugal; (F.P.); (L.P.); (S.S.); (V.F.)
- HM—Health and Human Movement Unit, Polytechnic University of Health, Cooperativa de Ensino Superior Politécnico e Universitário, CRL, 4760-409 Vila Nova de Famalicão, Portugal
| | - Liliana Pinho
- Escola Superior de Saúde do Vale do Ave, Cooperativa de Ensino Superior Politécnico e Universitário, Rua José António Vidal, 81, 4760-409 Vila Nova de Famalicão, Portugal; (F.P.); (L.P.); (S.S.); (V.F.)
- HM—Health and Human Movement Unit, Polytechnic University of Health, Cooperativa de Ensino Superior Politécnico e Universitário, CRL, 4760-409 Vila Nova de Famalicão, Portugal
- Center for Rehabilitation Research (CIR), R. Dr. António Bernardino de Almeida 400, 4200-072 Porto, Portugal;
- Porto Biomechanics Laboratory (LABIOMEP), 4200-450 Porto, Portugal
| | - Sandra Silva
- Escola Superior de Saúde do Vale do Ave, Cooperativa de Ensino Superior Politécnico e Universitário, Rua José António Vidal, 81, 4760-409 Vila Nova de Famalicão, Portugal; (F.P.); (L.P.); (S.S.); (V.F.)
- HM—Health and Human Movement Unit, Polytechnic University of Health, Cooperativa de Ensino Superior Politécnico e Universitário, CRL, 4760-409 Vila Nova de Famalicão, Portugal
- Department of Medical Sciences, University of Aveiro, 3810-193 Aveiro, Portugal
- School of Health Sciences, University of Aveiro, 3810-193 Aveiro, Portugal;
| | - Vânia Figueira
- Escola Superior de Saúde do Vale do Ave, Cooperativa de Ensino Superior Politécnico e Universitário, Rua José António Vidal, 81, 4760-409 Vila Nova de Famalicão, Portugal; (F.P.); (L.P.); (S.S.); (V.F.)
- HM—Health and Human Movement Unit, Polytechnic University of Health, Cooperativa de Ensino Superior Politécnico e Universitário, CRL, 4760-409 Vila Nova de Famalicão, Portugal
- Porto Biomechanics Laboratory (LABIOMEP), 4200-450 Porto, Portugal
| | - João Paulo Vilas-Boas
- School of Health Sciences, University of Aveiro, 3810-193 Aveiro, Portugal;
- Centre for Research, Training, Innovation and Intervention in Sport (CIFI2D), Faculty of Sport, University of Porto, 4200-450 Porto, Portugal
| | - Augusta Silva
- Center for Rehabilitation Research (CIR), R. Dr. António Bernardino de Almeida 400, 4200-072 Porto, Portugal;
- Department of Physiotherapy, School of Health, Polytechnic of Porto, 4200-072 Porto, Portugal
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Huang X, Liao O, Jiang S, Li J, Ma X. Kinematic analysis in post-stroke patients with moderate to severe upper limb paresis and non-disabled controls. Clin Biomech (Bristol, Avon) 2024; 113:106206. [PMID: 38401320 DOI: 10.1016/j.clinbiomech.2024.106206] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/04/2023] [Revised: 12/23/2023] [Accepted: 02/13/2024] [Indexed: 02/26/2024]
Abstract
BACKGROUND Kinematic analysis has been recommended to quantify the upper limb motor function after stroke. However, previous studies have rarely reported the kinematic data of the post-stroke patients with moderate to severe upper limb paresis due to the poor accomplishment of the complex tasks. METHODS 27 post-stroke individuals and 20 non-disabled people participated in the study. The trunk and upper limb movements during the Hand-to-mouth task were captured by the motion capture system and upper extremity kinematic analysis software automatically. The subgroup analysis within stroke group were conducted layering by the Fugl-Meyer Assessment for Upper Extremity scores (severe: 16-31; moderate: 32-50). FINDINGS The paretic upper limbs in the stroke group tended to use more trunk and shoulder compensatory strategies to offset the impact of spasticity and weakness compared with non-disabled controls. The less-affected limbs in the stroke group also showed abnormal kinematic data. There were significant differences between the kinematic metrics of severe and moderate subgroups. INTERPRETATION The Hand-to-mouth task is a good and feasible option for kinematic analysis of these patients. It is essential to layer the severity of the paresis and put more emphasis on trunk movements in the future kinematic studies.
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Affiliation(s)
- Xinyun Huang
- Acupuncture Center, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai 200437, China; Yueyang Clinical Medical College, Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China; Shanghai Research Institute of Acupuncture and Meridian, Shanghai 200030, China
| | - Ouping Liao
- Yueyang Clinical Medical College, Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China; Traditional Chinese Medicine department, DeYang People's Hospital, Sichuan 618099, China
| | - Shuyun Jiang
- Gait and Motion Analysis Center, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai 200437, China
| | - Jing Li
- Acupuncture Center, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai 200437, China
| | - Xiaopeng Ma
- Acupuncture Center, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai 200437, China; Shanghai Research Institute of Acupuncture and Meridian, Shanghai 200030, China.
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Lassi M, Dalise S, Bandini A, Spina V, Azzollini V, Vissani M, Micera S, Mazzoni A, Chisari C. Neurophysiological underpinnings of an intensive protocol for upper limb motor recovery in subacute and chronic stroke patients. Eur J Phys Rehabil Med 2024; 60:13-26. [PMID: 37987741 DOI: 10.23736/s1973-9087.23.07922-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2023]
Abstract
BACKGROUND Upper limb (UL) motor impairment following stroke is a leading cause of functional limitations in activities of daily living. Robot-assisted therapy supports rehabilitation, but how its efficacy and the underlying neural mechanisms depend on the time after stroke is yet to be assessed. AIM We investigated the response to an intensive protocol of robot-assisted rehabilitation in sub-acute and chronic stroke patients, by analyzing the underlying changes in clinical scores, electroencephalography (EEG) and end-effector kinematics. We aimed at identifying neural correlates of the participants' upper limb motor function recovery, following an intensive 2-week rehabilitation protocol. DESIGN Prospective cohort study. SETTING Inpatients and outpatients from the Neurorehabilitation Unit of Pisa University Hospital, Italy. POPULATION Sub-acute and chronic stroke survivors. METHODS Thirty-one stroke survivors (14 sub-acute, 17 chronic) with mild-to-moderate UL paresis were enrolled. All participants underwent ten rehabilitative sessions of task-oriented exercises with a planar end-effector robotic device. All patients were evaluated with the Fugl-Meyer Assessment Scale and the Wolf Motor Function Test, at recruitment (T0), end-of-treatment (T1), and one-month follow-up (T2). Along with clinical scales, kinematic parameters and quantitative EEG were collected for each patient. Kinematics metrics were related to velocity, acceleration and smoothness of the movement. Relative power in four frequency bands was extracted from the EEG signals. The evolution over time of kinematic and EEG features was analyzed, in correlation with motor recovery. RESULTS Both groups displayed significant gains in motility after treatment. Sub-acute patients displayed more pronounced clinical improvements, significant changes in kinematic parameters, and a larger increase in Beta-band in the motor area of the affected hemisphere. In both groups these improvements were associated to a decrease in the Delta-band of both hemispheres. Improvements were retained at T2. CONCLUSIONS The intensive two-week rehabilitation protocol was effective in both chronic and sub-acute patients, and improvements in the two groups shared similar dynamics. However, stronger cortical and behavioral changes were observed in sub-acute patients suggesting different reorganizational patterns. CLINICAL REHABILITATION IMPACT This study paves the way to personalized approaches to UL motor rehabilitation after stroke, as highlighted by different neurophysiological modifications following recovery in subacute and chronic stroke patients.
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Affiliation(s)
- Michael Lassi
- The Biorobotics Institute, Scuola Superiore Sant'Anna, Pisa, Italy
- Department of Excellence in Robotics and AI, Scuola Superiore Sant'Anna, Pisa, Italy
| | - Stefania Dalise
- Neurorehabilitation Unit, Pisa University Hospital, Pisa, Italy
| | - Andrea Bandini
- The Biorobotics Institute, Scuola Superiore Sant'Anna, Pisa, Italy
- Department of Excellence in Robotics and AI, Scuola Superiore Sant'Anna, Pisa, Italy
- Health Science Interdisciplinary Research Center, Scuola Superiore Sant'Anna, Pisa, Italy
| | - Vincenzo Spina
- Neurorehabilitation Unit, Pisa University Hospital, Pisa, Italy
| | | | - Matteo Vissani
- The Biorobotics Institute, Scuola Superiore Sant'Anna, Pisa, Italy
- Department of Excellence in Robotics and AI, Scuola Superiore Sant'Anna, Pisa, Italy
- Harvard Medical School, Boston, MA, USA
- Department of Neurosurgery, Massachusetts General Hospital, Boston, MA, USA
| | - Silvestro Micera
- The Biorobotics Institute, Scuola Superiore Sant'Anna, Pisa, Italy
- Department of Excellence in Robotics and AI, Scuola Superiore Sant'Anna, Pisa, Italy
- Bertarelli Foundation Chair in Translational Neural Engineering, Center for Neuroprosthetics and Institute of Bioengineering, École Polytechnique Fèdèrale de Lausanne, Lausanne, Switzerland
| | - Alberto Mazzoni
- The Biorobotics Institute, Scuola Superiore Sant'Anna, Pisa, Italy
- Department of Excellence in Robotics and AI, Scuola Superiore Sant'Anna, Pisa, Italy
| | - Carmelo Chisari
- Neurorehabilitation Unit, Pisa University Hospital, Pisa, Italy -
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Lorenz EA, Su X, Skjæret-Maroni N. A review of combined functional neuroimaging and motion capture for motor rehabilitation. J Neuroeng Rehabil 2024; 21:3. [PMID: 38172799 PMCID: PMC10765727 DOI: 10.1186/s12984-023-01294-6] [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: 06/23/2023] [Accepted: 12/11/2023] [Indexed: 01/05/2024] Open
Abstract
BACKGROUND Technological advancements in functional neuroimaging and motion capture have led to the development of novel methods that facilitate the diagnosis and rehabilitation of motor deficits. These advancements allow for the synchronous acquisition and analysis of complex signal streams of neurophysiological data (e.g., EEG, fNIRS) and behavioral data (e.g., motion capture). The fusion of those data streams has the potential to provide new insights into cortical mechanisms during movement, guide the development of rehabilitation practices, and become a tool for assessment and therapy in neurorehabilitation. RESEARCH OBJECTIVE This paper aims to review the existing literature on the combined use of motion capture and functional neuroimaging in motor rehabilitation. The objective is to understand the diversity and maturity of technological solutions employed and explore the clinical advantages of this multimodal approach. METHODS This paper reviews literature related to the combined use of functional neuroimaging and motion capture for motor rehabilitation following the PRISMA guidelines. Besides study and participant characteristics, technological aspects of the used systems, signal processing methods, and the nature of multimodal feature synchronization and fusion were extracted. RESULTS Out of 908 publications, 19 were included in the final review. Basic or translation studies were mainly represented and based predominantly on healthy participants or stroke patients. EEG and mechanical motion capture technologies were most used for biomechanical data acquisition, and their subsequent processing is based mainly on traditional methods. The system synchronization techniques at large were underreported. The fusion of multimodal features mainly supported the identification of movement-related cortical activity, and statistical methods were occasionally employed to examine cortico-kinematic relationships. CONCLUSION The fusion of motion capture and functional neuroimaging might offer advantages for motor rehabilitation in the future. Besides facilitating the assessment of cognitive processes in real-world settings, it could also improve rehabilitative devices' usability in clinical environments. Further, by better understanding cortico-peripheral coupling, new neuro-rehabilitation methods can be developed, such as personalized proprioceptive training. However, further research is needed to advance our knowledge of cortical-peripheral coupling, evaluate the validity and reliability of multimodal parameters, and enhance user-friendly technologies for clinical adaptation.
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Affiliation(s)
- Emanuel A Lorenz
- Department of Computer Science, Norwegian University of Science and Technology, Trondheim, Norway.
| | - Xiaomeng Su
- Department of Computer Science, Norwegian University of Science and Technology, Trondheim, Norway
| | - Nina Skjæret-Maroni
- Department of Neuromedicine and Movement Science, Norwegian University of Science and Technology, Trondheim, Norway
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Casile A, Fregna G, Boarini V, Paoluzzi C, Manfredini F, Lamberti N, Baroni A, Straudi S. Quantitative Comparison of Hand Kinematics Measured with a Markerless Commercial Head-Mounted Display and a Marker-Based Motion Capture System in Stroke Survivors. SENSORS (BASEL, SWITZERLAND) 2023; 23:7906. [PMID: 37765963 PMCID: PMC10535006 DOI: 10.3390/s23187906] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/20/2023] [Revised: 08/25/2023] [Accepted: 09/13/2023] [Indexed: 09/29/2023]
Abstract
Upper-limb paresis is common after stroke. An important tool to assess motor recovery is to use marker-based motion capture systems to measure the kinematic characteristics of patients' movements in ecological scenarios. These systems are, however, very expensive and not readily available for many rehabilitation units. Here, we explored whether the markerless hand motion capabilities of the cost-effective Oculus Quest head-mounted display could be used to provide clinically meaningful measures. A total of 14 stroke patients executed ecologically relevant upper-limb tasks in an immersive virtual environment. During task execution, we recorded their hand movements simultaneously by means of the Oculus Quest's and a marker-based motion capture system. Our results showed that the markerless estimates of the hand position and peak velocity provided by the Oculus Quest were in very close agreement with those provided by a marker-based commercial system with their regression line having a slope close to 1 (maximum distance: mean slope = 0.94 ± 0.1; peak velocity: mean slope = 1.06 ± 0.12). Furthermore, the Oculus Quest had virtually the same sensitivity as that of a commercial system in distinguishing healthy from pathological kinematic measures. The Oculus Quest was as accurate as a commercial marker-based system in measuring clinically meaningful upper-limb kinematic parameters in stroke patients.
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Affiliation(s)
- Antonino Casile
- Department of Biomedical and Dental Sciences and Morphofunctional Imaging, University of Messina, 98122 Messina, Italy
- Center of Translational Neurophysiology of Speech and Communication (CTNSC), Istituto Italiano di Tecnologia (IIT), 44121 Ferrara, Italy;
| | - Giulia Fregna
- Doctoral Program in Translational Neurosciences and Neurotechnologies, University of Ferrara, 44121 Ferrara, Italy;
| | - Vittorio Boarini
- Center of Translational Neurophysiology of Speech and Communication (CTNSC), Istituto Italiano di Tecnologia (IIT), 44121 Ferrara, Italy;
- Department of Mathematics and Computer Science, University of Ferrara, 44121 Ferrara, Italy
| | - Chiara Paoluzzi
- Department of Neuroscience and Rehabilitation, University of Ferrara, 44121 Ferrara, Italy; (C.P.); (N.L.); (A.B.); (S.S.)
| | - Fabio Manfredini
- Department of Neuroscience and Rehabilitation, University of Ferrara, 44121 Ferrara, Italy; (C.P.); (N.L.); (A.B.); (S.S.)
- Department of Neuroscience, Ferrara University Hospital, 44124 Ferrara, Italy
| | - Nicola Lamberti
- Department of Neuroscience and Rehabilitation, University of Ferrara, 44121 Ferrara, Italy; (C.P.); (N.L.); (A.B.); (S.S.)
| | - Andrea Baroni
- Department of Neuroscience and Rehabilitation, University of Ferrara, 44121 Ferrara, Italy; (C.P.); (N.L.); (A.B.); (S.S.)
- Department of Neuroscience, Ferrara University Hospital, 44124 Ferrara, Italy
| | - Sofia Straudi
- Department of Neuroscience and Rehabilitation, University of Ferrara, 44121 Ferrara, Italy; (C.P.); (N.L.); (A.B.); (S.S.)
- Department of Neuroscience, Ferrara University Hospital, 44124 Ferrara, Italy
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Scano A, Guanziroli E, Brambilla C, Amendola C, Pirovano I, Gasperini G, Molteni F, Spinelli L, Molinari Tosatti L, Rizzo G, Re R, Mastropietro A. A Narrative Review on Multi-Domain Instrumental Approaches to Evaluate Neuromotor Function in Rehabilitation. Healthcare (Basel) 2023; 11:2282. [PMID: 37628480 PMCID: PMC10454517 DOI: 10.3390/healthcare11162282] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2023] [Revised: 08/02/2023] [Accepted: 08/10/2023] [Indexed: 08/27/2023] Open
Abstract
In clinical scenarios, the use of biomedical sensors, devices and multi-parameter assessments is fundamental to provide a comprehensive portrait of patients' state, in order to adapt and personalize rehabilitation interventions and support clinical decision-making. However, there is a huge gap between the potential of the multidomain techniques available and the limited practical use that is made in the clinical scenario. This paper reviews the current state-of-the-art and provides insights into future directions of multi-domain instrumental approaches in the clinical assessment of patients involved in neuromotor rehabilitation. We also summarize the main achievements and challenges of using multi-domain approaches in the assessment of rehabilitation for various neurological disorders affecting motor functions. Our results showed that multi-domain approaches combine information and measurements from different tools and biological signals, such as kinematics, electromyography (EMG), electroencephalography (EEG), near-infrared spectroscopy (NIRS), and clinical scales, to provide a comprehensive and objective evaluation of patients' state and recovery. This multi-domain approach permits the progress of research in clinical and rehabilitative practice and the understanding of the pathophysiological changes occurring during and after rehabilitation. We discuss the potential benefits and limitations of multi-domain approaches for clinical decision-making, personalized therapy, and prognosis. We conclude by highlighting the need for more standardized methods, validation studies, and the integration of multi-domain approaches in clinical practice and research.
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Affiliation(s)
- Alessandro Scano
- Institute of Intelligent Industrial Systems and Technologies for Advanced Manufacturing (STIIMA), Italian Council of National Research (CNR), Via A. Corti 12, 20133 Milan, Italy; (C.B.); (L.M.T.)
| | - Eleonora Guanziroli
- Villa Beretta Rehabilitation Center, Via N. Sauro 17, 23845 Costa Masnaga, Italy; (E.G.); (G.G.); (F.M.)
| | - Cristina Brambilla
- Institute of Intelligent Industrial Systems and Technologies for Advanced Manufacturing (STIIMA), Italian Council of National Research (CNR), Via A. Corti 12, 20133 Milan, Italy; (C.B.); (L.M.T.)
| | - Caterina Amendola
- Dipartimento di Fisica, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133 Milan, Italy; (C.A.); (R.R.)
| | - Ileana Pirovano
- Institute of Biomedical Technologies (ITB), Italian National Research Council (CNR), Via Fratelli Cervi 93, 20054 Segrate, Italy; (I.P.); (G.R.); (A.M.)
| | - Giulio Gasperini
- Villa Beretta Rehabilitation Center, Via N. Sauro 17, 23845 Costa Masnaga, Italy; (E.G.); (G.G.); (F.M.)
| | - Franco Molteni
- Villa Beretta Rehabilitation Center, Via N. Sauro 17, 23845 Costa Masnaga, Italy; (E.G.); (G.G.); (F.M.)
| | - Lorenzo Spinelli
- Institute for Photonics and Nanotechnology (IFN), Italian National Research Council (CNR), Piazza Leonardo da Vinci 32, 20133 Milan, Italy;
| | - Lorenzo Molinari Tosatti
- Institute of Intelligent Industrial Systems and Technologies for Advanced Manufacturing (STIIMA), Italian Council of National Research (CNR), Via A. Corti 12, 20133 Milan, Italy; (C.B.); (L.M.T.)
| | - Giovanna Rizzo
- Institute of Biomedical Technologies (ITB), Italian National Research Council (CNR), Via Fratelli Cervi 93, 20054 Segrate, Italy; (I.P.); (G.R.); (A.M.)
| | - Rebecca Re
- Dipartimento di Fisica, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133 Milan, Italy; (C.A.); (R.R.)
- Institute for Photonics and Nanotechnology (IFN), Italian National Research Council (CNR), Piazza Leonardo da Vinci 32, 20133 Milan, Italy;
| | - Alfonso Mastropietro
- Institute of Biomedical Technologies (ITB), Italian National Research Council (CNR), Via Fratelli Cervi 93, 20054 Segrate, Italy; (I.P.); (G.R.); (A.M.)
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