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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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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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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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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: 0] [Impact Index Per Article: 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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Zhao K, Zhang Z, Wen H, Liu B, Li J, Andrea d’Avella, Scano A. Muscle synergies for evaluating upper limb in clinical applications: A systematic review. Heliyon 2023; 9:e16202. [PMID: 37215841 PMCID: PMC10199229 DOI: 10.1016/j.heliyon.2023.e16202] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2022] [Revised: 04/11/2023] [Accepted: 05/09/2023] [Indexed: 09/28/2023] Open
Abstract
INTRODUCTION Muscle synergies have been proposed as a strategy employed by the central nervous system to control movements. Muscle synergy analysis is a well-established framework to examine the pathophysiological basis of neurological diseases and has been applied for analysis and assessment in clinical applications in the last decades, even if it has not yet been widely used in clinical diagnosis, rehabilitative treatment and interventions. Even if inconsistencies in the outputs among studies and lack of a normative pipeline including signal processing and synergy analysis limit the progress, common findings and results are identifiable as a basis for future research. Therefore, a literature review that summarizes methods and main findings of previous works on upper limb muscle synergies in clinical environment is needed to i) summarize the main findings so far, ii) highlight the barriers limiting their use in clinical applications, and iii) suggest future research directions needed for facilitating translation of experimental research to clinical scenarios. METHODS Articles in which muscle synergies were used to analyze and assess upper limb function in neurological impairments were reviewed. The literature research was conducted in Scopus, PubMed, and Web of Science. Experimental protocols (e.g., the aim of the study, number and type of participants, number and type of muscles, and tasks), methods (e.g., muscle synergy models and synergy extraction methods, signal processing methods), and the main findings of eligible studies were reported and discussed. RESULTS 383 articles were screened and 51 were selected, which involved a total of 13 diseases and 748 patients and 1155 participants. Each study investigated on average 15 ± 10 patients. Four to forty-one muscles were included in the muscle synergy analysis. Point-to-point reaching was the most used task. The preprocessing of EMG signals and algorithms for synergy extraction varied among studies, and non-negative matrix factorization was the most used method. Five EMG normalization methods and five methods for identifying the optimal number of synergies were used in the selected papers. Most of the studies report that analyses on synergy number, structure, and activations provide novel insights on the physiopathology of motor control that cannot be gained with standard clinical assessments, and suggest that muscle synergies may be useful to personalize therapies and to develop new therapeutic strategies. However, in the selected studies synergies were used only for assessment; different testing procedures were used and, in general, study-specific modifications of muscle synergies were observed; single session or longitudinal studies mainly aimed at assessing stroke (71% of the studies), even though other pathologies were also investigated. Synergy modifications were either study-specific or were not observed, with few analyses available for temporal coefficients. Thus, several barriers prevent wider adoption of muscle synergy analysis including a lack of standardized experimental protocols, signal processing procedures, and synergy extraction methods. A compromise in the design of the studies must be found to combine the systematicity of motor control studies and the feasibility of clinical studies. There are however several potential developments that might promote the use of muscle synergy analysis in clinical practice, including refined assessments based on synergistic approaches not allowed by other methods and the availability of novel models. Finally, neural substrates of muscle synergies are discussed, and possible future research directions are proposed. CONCLUSIONS This review provides new perspectives about the challenges and open issues that need to be addressed in future work to achieve a better understanding of motor impairments and rehabilitative therapy using muscle synergies. These include the application of the methods on wider scales, standardization of procedures, inclusion of synergies in the clinical decisional process, assessment of temporal coefficients and temporal-based models, extensive work on the algorithms and understanding of the physio-pathological mechanisms of pathology, as well as the application and adaptation of synergy-based approaches to various rehabilitative scenarios for increasing the available evidence.
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Affiliation(s)
- Kunkun Zhao
- School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, China
| | - Zhisheng Zhang
- School of Mechanical Engineering, Southeast University, Nanjing, China
| | - Haiying Wen
- School of Mechanical Engineering, Southeast University, Nanjing, China
| | - Bin Liu
- School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, China
| | - Jianqing Li
- School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, China
| | - Andrea d’Avella
- Laboratory of Neuromotor Physiology, IRCCS Fondazione Santa Lucia, Rome, Italy
- Department of Biomedical and Dental Sciences and Morphofunctional Imaging, University of Messina, Italy
| | - Alessandro Scano
- Institute of Intelligent Industrial Technologies and Systems for Advanced Manufacturing (STIIMA), National Research Council of Italy (CNR), Milan, Italy
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Maura RM, Rueda Parra S, Stevens RE, Weeks DL, Wolbrecht ET, Perry JC. Literature review of stroke assessment for upper-extremity physical function via EEG, EMG, kinematic, and kinetic measurements and their reliability. J Neuroeng Rehabil 2023; 20:21. [PMID: 36793077 PMCID: PMC9930366 DOI: 10.1186/s12984-023-01142-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2021] [Accepted: 01/19/2023] [Indexed: 02/17/2023] Open
Abstract
BACKGROUND Significant clinician training is required to mitigate the subjective nature and achieve useful reliability between measurement occasions and therapists. Previous research supports that robotic instruments can improve quantitative biomechanical assessments of the upper limb, offering reliable and more sensitive measures. Furthermore, combining kinematic and kinetic measurements with electrophysiological measurements offers new insights to unlock targeted impairment-specific therapy. This review presents common methods for analyzing biomechanical and neuromuscular data by describing their validity and reporting their reliability measures. METHODS This paper reviews literature (2000-2021) on sensor-based measures and metrics for upper-limb biomechanical and electrophysiological (neurological) assessment, which have been shown to correlate with clinical test outcomes for motor assessment. The search terms targeted robotic and passive devices developed for movement therapy. Journal and conference papers on stroke assessment metrics were selected using PRISMA guidelines. Intra-class correlation values of some of the metrics are recorded, along with model, type of agreement, and confidence intervals, when reported. RESULTS A total of 60 articles are identified. The sensor-based metrics assess various aspects of movement performance, such as smoothness, spasticity, efficiency, planning, efficacy, accuracy, coordination, range of motion, and strength. Additional metrics assess abnormal activation patterns of cortical activity and interconnections between brain regions and muscle groups; aiming to characterize differences between the population who had a stroke and the healthy population. CONCLUSION Range of motion, mean speed, mean distance, normal path length, spectral arc length, number of peaks, and task time metrics have all demonstrated good to excellent reliability, as well as provide a finer resolution compared to discrete clinical assessment tests. EEG power features for multiple frequency bands of interest, specifically the bands relating to slow and fast frequencies comparing affected and non-affected hemispheres, demonstrate good to excellent reliability for populations at various stages of stroke recovery. Further investigation is needed to evaluate the metrics missing reliability information. In the few studies combining biomechanical measures with neuroelectric signals, the multi-domain approaches demonstrated agreement with clinical assessments and provide further information during the relearning phase. Combining the reliable sensor-based metrics in the clinical assessment process will provide a more objective approach, relying less on therapist expertise. This paper suggests future work on analyzing the reliability of metrics to prevent biasedness and selecting the appropriate analysis.
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Affiliation(s)
- Rene M. Maura
- Mechanical Engineering Department, University of Idaho, Moscow, ID USA
| | | | - Richard E. Stevens
- Engineering and Physics Department, Whitworth University, Spokane, WA USA
| | - Douglas L. Weeks
- College of Medicine, Washington State University, Spokane, WA USA
| | - Eric T. Wolbrecht
- Mechanical Engineering Department, University of Idaho, Moscow, ID USA
| | - Joel C. Perry
- Mechanical Engineering Department, University of Idaho, Moscow, ID USA
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Du Q, Luo J, Cheng Q, Wang Y, Guo S. Vibrotactile enhancement in hand rehabilitation has a reinforcing effect on sensorimotor brain activities. Front Neurosci 2022; 16:935827. [PMID: 36267238 PMCID: PMC9577243 DOI: 10.3389/fnins.2022.935827] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2022] [Accepted: 08/31/2022] [Indexed: 11/13/2022] Open
Abstract
Objective Stroke patients often suffer from hand dysfunction or loss of tactile perception, which in turn interferes with hand rehabilitation. Tactile-enhanced multi-sensory feedback rehabilitation is an approach worth considering, but its effectiveness has not been well studied. By using functional near-infrared spectroscopy (fNIRS) to analyze the causal activity patterns in the sensorimotor cortex, the present study aims to investigate the cortical hemodynamic effects of hand rehabilitation training when tactile stimulation is applied, and to provide a basis for rehabilitation program development. Methods A vibrotactile enhanced pneumatically actuated hand rehabilitation device was tested on the less-preferred hand of 14 healthy right-handed subjects. The training tasks consisted of move hand and observe video (MO), move hand and vibration stimulation (MV), move hand, observe video, and vibration stimulation (MOV), and a contrast resting task. Region of interest (ROI), a laterality index (LI), and causal brain network analysis methods were used to explore the brain’s cortical blood flow response to a multi-sensory feedback rehabilitation task from multiple perspectives. Results (1) A more pronounced contralateral activation in the right-brain region occurred under the MOV stimulation. Rehabilitation tasks containing vibrotactile enhancement (MV and MOV) had significantly more oxyhemoglobin than the MO task at 5 s after the task starts, indicating faster contralateral activation in sensorimotor brain regions. (2) Five significant lateralized channel connections were generated under the MV and MOV tasks (p < 0.05), one significant lateralized channel connection was generated by the MO task, and the Rest were not, showing that MV and MOV caused stronger lateralization activation. (3) We investigated all thresholds of granger causality (GC) resulting in consistent relative numbers of effect connections. MV elicited stronger causal interactions between the left and right cerebral hemispheres, and at the GC threshold of 0.4, there were 13 causal network connection pairs for MV, 7 for MO, and 9 for MOV. Conclusion Vibrotactile cutaneous stimulation as a tactile enhancement can produce a stronger stimulation of the brain’s sensorimotor brain areas, promoting the establishment of neural pathways, and causing a richer effect between the left and right cerebral hemispheres. The combination of kinesthetic, vibrotactile, and visual stimulation can achieve a more prominent training efficiency from the perspective of functional cerebral hemodynamics.
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Affiliation(s)
- Qiang Du
- Academy for Engineering and Technology, Fudan University, Shanghai, China
- Shanghai Engineering Research Center of AI and Robotics, Shanghai, China
- Engineering Research Center of AI and Robotics, Ministry of Education, Shanghai, China
| | - Jingjing Luo
- Academy for Engineering and Technology, Fudan University, Shanghai, China
- Shanghai Engineering Research Center of AI and Robotics, Shanghai, China
- Engineering Research Center of AI and Robotics, Ministry of Education, Shanghai, China
- Jihua Laboratory, Foshan, China
- *Correspondence: Jingjing Luo,
| | - Qiying Cheng
- Academy for Engineering and Technology, Fudan University, Shanghai, China
- Shanghai Engineering Research Center of AI and Robotics, Shanghai, China
- Engineering Research Center of AI and Robotics, Ministry of Education, Shanghai, China
| | - Youhao Wang
- Academy for Engineering and Technology, Fudan University, Shanghai, China
- Shanghai Engineering Research Center of AI and Robotics, Shanghai, China
- Engineering Research Center of AI and Robotics, Ministry of Education, Shanghai, China
| | - Shijie Guo
- Academy for Engineering and Technology, Fudan University, Shanghai, China
- Shanghai Engineering Research Center of AI and Robotics, Shanghai, China
- Engineering Research Center of AI and Robotics, Ministry of Education, Shanghai, China
- Department of the State Key Laboratory of Reliability and Intelligence of Electrical Equipment and the Hebei Key Laboratory of Robot Perception and Human-Robot Interaction, Hebei University of Technology, Tianjin, China
- Shijie Guo,
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Longatelli V, Torricelli D, Tornero J, Pedrocchi A, Molteni F, Pons JL, Gandolla M. A unified scheme for the benchmarking of upper limb functions in neurological disorders. J Neuroeng Rehabil 2022; 19:102. [PMID: 36167552 PMCID: PMC9513990 DOI: 10.1186/s12984-022-01082-8] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2022] [Accepted: 09/08/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND In neurorehabilitation, we are witnessing a growing awareness of the importance of standardized quantitative assessment of limb functions. Detailed assessments of the sensorimotor deficits following neurological disorders are crucial. So far, this assessment has relied mainly on clinical scales, which showed several drawbacks. Different technologies could provide more objective and repeatable measurements. However, the current literature lacks practical guidelines for this purpose. Nowadays, the integration of available metrics, protocols, and algorithms into one harmonized benchmarking ecosystem for clinical and research practice is necessary. METHODS This work presents a benchmarking framework for upper limb capacity. The scheme resulted from a multidisciplinary and iterative discussion among several partners with previous experience in benchmarking methodology, robotics, and clinical neurorehabilitation. We merged previous knowledge in benchmarking methodologies for human locomotion and direct clinical and engineering experience in upper limb rehabilitation. The scheme was designed to enable an instrumented evaluation of arm capacity and to assess the effectiveness of rehabilitative interventions with high reproducibility and resolution. It includes four elements: (1) a taxonomy for motor skills and abilities, (2) a list of performance indicators, (3) a list of required sensor modalities, and (4) a set of reproducible experimental protocols. RESULTS We proposed six motor primitives as building blocks of most upper-limb daily-life activities and combined them into a set of functional motor skills. We identified the main aspects to be considered during clinical evaluation, and grouped them into ten motor abilities categories. For each ability, we proposed a set of performance indicators to quantify the proposed ability on a quantitative and high-resolution scale. Finally, we defined the procedures to be followed to perform the benchmarking assessment in a reproducible and reliable way, including the definition of the kinematic models and the target muscles. CONCLUSIONS This work represents the first unified scheme for the benchmarking of upper limb capacity. To reach a consensus, this scheme should be validated with real experiments across clinical conditions and motor skills. This validation phase is expected to create a shared database of human performance, necessary to have realistic comparisons of treatments and drive the development of new personalized technologies.
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Affiliation(s)
- Valeria Longatelli
- Neuroengineering and Medical Robotics Laboratory and WE-COBOT Laboratory, Department of Electronics, Information, and Bioengineering, Politecnico di Milano, Milan, Italy.
| | - Diego Torricelli
- Neural Rehabilitation Group, Cajal Institute, Spanish National Research Council (CSIC), Madrid, Spain
| | - Jesús Tornero
- Advanced Neurorehabilitation Unit, Hospital Los Madroños, Madrid, Spain
| | - Alessandra Pedrocchi
- Neuroengineering and Medical Robotics Laboratory and WE-COBOT Laboratory, Department of Electronics, Information, and Bioengineering, Politecnico di Milano, Milan, Italy
| | - Franco Molteni
- Villa Beretta Rehabilitation Center, Valduce Hospital, Costa Masnaga, Italy
| | | | - Marta Gandolla
- WE-COBOT Laboratory, Department of Mechanical Engineering, Politecnico di Milano, Milan, Italy
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9
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Zheng Z, Wu Z, Zhao R, Ni Y, Jing X, Gao S. A Review of EMG-, FMG-, and EIT-Based Biosensors and Relevant Human–Machine Interactivities and Biomedical Applications. BIOSENSORS 2022; 12:bios12070516. [PMID: 35884319 PMCID: PMC9313012 DOI: 10.3390/bios12070516] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/06/2022] [Revised: 07/06/2022] [Accepted: 07/09/2022] [Indexed: 11/23/2022]
Abstract
Wearables developed for human body signal detection receive increasing attention in the current decade. Compared to implantable sensors, wearables are more focused on body motion detection, which can support human–machine interaction (HMI) and biomedical applications. In wearables, electromyography (EMG)-, force myography (FMG)-, and electrical impedance tomography (EIT)-based body information monitoring technologies are broadly presented. In the literature, all of them have been adopted for many similar application scenarios, which easily confuses researchers when they start to explore the area. Hence, in this article, we review the three technologies in detail, from basics including working principles, device architectures, interpretation algorithms, application examples, merits and drawbacks, to state-of-the-art works, challenges remaining to be solved and the outlook of the field. We believe the content in this paper could help readers create a whole image of designing and applying the three technologies in relevant scenarios.
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Affiliation(s)
| | | | | | | | | | - Shuo Gao
- Correspondence: ; Tel.: +86-18600737330
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10
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Góngora L, Paglialonga A, Mastropietro A, Rizzo G, Barbieri R. A Novel Approach for Segment-Length Selection Based on Stationarity to Perform Effective Connectivity Analysis Applied to Resting-State EEG Signals. SENSORS 2022; 22:s22134747. [PMID: 35808250 PMCID: PMC9269473 DOI: 10.3390/s22134747] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/19/2022] [Revised: 06/17/2022] [Accepted: 06/20/2022] [Indexed: 02/05/2023]
Abstract
Connectivity among different areas within the brain is a topic that has been notably studied in the last decade. In particular, EEG-derived measures of effective connectivity examine the directionalities and the exerted influences raised from the interactions among neural sources that are masked out on EEG signals. This is usually performed by fitting multivariate autoregressive models that rely on the stationarity that is assumed to be maintained over shorter bits of the signals. However, despite being a central condition, the selection process of a segment length that guarantees stationary conditions has not been systematically addressed within the effective connectivity framework, and thus, plenty of works consider different window sizes and provide a diversity of connectivity results. In this study, a segment-size-selection procedure based on fourth-order statistics is proposed to make an informed decision on the appropriate window size that guarantees stationarity both in temporal and spatial terms. Specifically, kurtosis is estimated as a function of the window size and used to measure stationarity. A search algorithm is implemented to find the segments with similar stationary properties while maximizing the number of channels that exhibit the same properties and grouping them accordingly. This approach is tested on EEG signals recorded from six healthy subjects during resting-state conditions, and the results obtained from the proposed method are compared to those obtained using the classical approach for mapping effective connectivity. The results show that the proposed method highlights the influence that arises in the Default Mode Network circuit by selecting a window of 4 s, which provides, overall, the most uniform stationary properties across channels.
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Affiliation(s)
- Leonardo Góngora
- Department of Electronics, Informatics and Bioengineering, Politecnico di Milano, 20133 Milan, Italy;
| | - Alessia Paglialonga
- Istituto di Elettronica e di Ingegneria dell’Informazione e delle Telecomunicazioni (IEIIT), Consiglio Nazionale delle Ricerche (CNR), 20133 Milan, Italy;
| | - Alfonso Mastropietro
- Istituto di Tecnologie Biomediche (ITB), Consiglio Nazionale delle Ricerche (CNR), 20054 Segrate, Italy; (A.M.); (G.R.)
| | - Giovanna Rizzo
- Istituto di Tecnologie Biomediche (ITB), Consiglio Nazionale delle Ricerche (CNR), 20054 Segrate, Italy; (A.M.); (G.R.)
| | - Riccardo Barbieri
- Department of Electronics, Informatics and Bioengineering, Politecnico di Milano, 20133 Milan, Italy;
- Correspondence:
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11
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Wang X, Fu Y, Ye B, Babineau J, Ding Y, Mihailidis A. Technology-Based Compensation Assessment and Detection of Upper Extremity Activities of Stroke Survivors: Systematic Review. J Med Internet Res 2022; 24:e34307. [PMID: 35699982 PMCID: PMC9237771 DOI: 10.2196/34307] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2021] [Revised: 03/25/2022] [Accepted: 04/10/2022] [Indexed: 11/13/2022] Open
Abstract
Background Upper extremity (UE) impairment affects up to 80% of stroke survivors and accounts for most of the rehabilitation after discharge from the hospital release. Compensation, commonly used by stroke survivors during UE rehabilitation, is applied to adapt to the loss of motor function and may impede the rehabilitation process in the long term and lead to new orthopedic problems. Intensive monitoring of compensatory movements is critical for improving the functional outcomes during rehabilitation. Objective This review analyzes how technology-based methods have been applied to assess and detect compensation during stroke UE rehabilitation. Methods We conducted a wide database search. All studies were independently screened by 2 reviewers (XW and YF), with a third reviewer (BY) involved in resolving discrepancies. The final included studies were rated according to their level of clinical evidence based on their correlation with clinical scales (with the same tasks or the same evaluation criteria). One reviewer (XW) extracted data on publication, demographic information, compensation types, sensors used for compensation assessment, compensation measurements, and statistical or artificial intelligence methods. Accuracy was checked by another reviewer (YF). Four research questions were presented. For each question, the data were synthesized and tabulated, and a descriptive summary of the findings was provided. The data were synthesized and tabulated based on each research question. Results A total of 72 studies were included in this review. In all, 2 types of compensation were identified: disuse of the affected upper limb and awkward use of the affected upper limb to adjust for limited strength, mobility, and motor control. Various models and quantitative measurements have been proposed to characterize compensation. Body-worn technology (25/72, 35% studies) was the most used sensor technology to assess compensation, followed by marker-based motion capture system (24/72, 33% studies) and marker-free vision sensor technology (16/72, 22% studies). Most studies (56/72, 78% studies) used statistical methods for compensation assessment, whereas heterogeneous machine learning algorithms (15/72, 21% studies) were also applied for automatic detection of compensatory movements and postures. Conclusions This systematic review provides insights for future research on technology-based compensation assessment and detection in stroke UE rehabilitation. Technology-based compensation assessment and detection have the capacity to augment rehabilitation independent of the constant care of therapists. The drawbacks of each sensor in compensation assessment and detection are discussed, and future research could focus on methods to overcome these disadvantages. It is advised that open data together with multilabel classification algorithms or deep learning algorithms could benefit from automatic real time compensation detection. It is also recommended that technology-based compensation predictions be explored.
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Affiliation(s)
- Xiaoyi Wang
- School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan, China
| | - Yan Fu
- School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan, China
| | - Bing Ye
- KITE - Toronto Rehabilitation Institute, University Health Network, Toronto, ON, Canada.,Department of Occupational Science and Occupational Therapy, University of Toronto, Toronto, ON, Canada
| | - Jessica Babineau
- Library and Information Services, University Health Network, Toronto, ON, Canada
| | - Yong Ding
- Department of Rehabilitation Medicine, Hubei Provincial Hospital of Traditional Chinese Medicine, Wuhan, China
| | - Alex Mihailidis
- KITE - Toronto Rehabilitation Institute, University Health Network, Toronto, ON, Canada.,Department of Occupational Science and Occupational Therapy, University of Toronto, Toronto, ON, Canada
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12
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Kumari R, Janković M, Costa A, Savić A, Konstantinović L, Djordjević O, Vucković A. Short term priming effect of brain-actuated muscle stimulation using bimanual movements in stroke. Clin Neurophysiol 2022; 138:108-121. [DOI: 10.1016/j.clinph.2022.03.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2021] [Revised: 02/26/2022] [Accepted: 03/01/2022] [Indexed: 11/03/2022]
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13
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Scano A, Mira RM, Gabbrielli G, Molteni F, Terekhov V. Whole-Body Adaptive Functional Electrical Stimulation Kinesitherapy Can Promote the Restoring of Physiological Muscle Synergies for Neurological Patients. SENSORS 2022; 22:s22041443. [PMID: 35214345 PMCID: PMC8877830 DOI: 10.3390/s22041443] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/21/2021] [Revised: 01/28/2022] [Accepted: 02/11/2022] [Indexed: 12/03/2022]
Abstract
Background: Neurological diseases and traumas are major factors that may reduce motor functionality. Functional electrical stimulation is a technique that helps regain motor function, assisting patients in daily life activities and in rehabilitation practices. In this study, we evaluated the efficacy of a treatment based on whole-body Adaptive Functional Electrical Stimulation Kinesitherapy (AFESK™) with the use of muscle synergies, a well-established method for evaluation of motor coordination. The evaluation is performed on retrospectively gathered data of neurological patients executing whole-body movements before and after AFESK-based treatments. Methods: Twenty-four chronic neurologic patients and 9 healthy subjects were recruited in this study. The patient group was further subdivided in 3 subgroups: hemiplegic, tetraplegic and paraplegic. All patients underwent two acquisition sessions: before treatment and after a FES based rehabilitation treatment at the VIKTOR Physio Lab. Patients followed whole-body exercise protocols tailored to their needs. The control group of healthy subjects performed all movements in a single session and provided reference data for evaluating patients’ performance. sEMG was recorded on relevant muscles and muscle synergies were extracted for each patient’s EMG data and then compared to the ones extracted from the healthy volunteers. To evaluate the effect of the treatment, the motricity index was measured and patients’ extracted synergies were compared to the control group before and after treatment. Results: After the treatment, patients’ motricity index increased for many of the screened body segments. Muscle synergies were more similar to those of healthy people. Globally, the normalized synergy similarity in respect to the control group was 0.50 before the treatment and 0.60 after (p < 0.001), with improvements for each subgroup of patients. Conclusions: AFESK treatment induced favorable changes in muscle activation patterns in chronic neurologic patients, partially restoring muscular patterns similar to healthy people. The evaluation of the synergic relationships of muscle activity when performing test exercises allows to assess the results of rehabilitation measures in patients with impaired locomotor functions.
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Affiliation(s)
- Alessandro Scano
- UOS STIIMA Lecco—Human-Centered, Smart & Safe, Living Environment, Italian National Research Council (CNR), Via Previati 1/E, 23900 Lecco, Italy;
- Correspondence: (A.S.); (V.T.)
| | - Robert Mihai Mira
- UOS STIIMA Lecco—Human-Centered, Smart & Safe, Living Environment, Italian National Research Council (CNR), Via Previati 1/E, 23900 Lecco, Italy;
| | | | - Franco Molteni
- Villa Beretta Rehabilitation Center, Ospedale Valduce, Via N. Sauro 17, 23845 Costa Masnaga, Italy;
| | - Viktor Terekhov
- VIKTOR S.r.l.—Via Pasubio, 5, 24044 Dalmine (BG), Italy;
- Correspondence: (A.S.); (V.T.)
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14
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Garro F, Chiappalone M, Buccelli S, De Michieli L, Semprini M. Neuromechanical Biomarkers for Robotic Neurorehabilitation. Front Neurorobot 2021; 15:742163. [PMID: 34776920 PMCID: PMC8579108 DOI: 10.3389/fnbot.2021.742163] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2021] [Accepted: 09/22/2021] [Indexed: 02/06/2023] Open
Abstract
One of the current challenges for translational rehabilitation research is to develop the strategies to deliver accurate evaluation, prediction, patient selection, and decision-making in the clinical practice. In this regard, the robot-assisted interventions have gained popularity as they can provide the objective and quantifiable assessment of the motor performance by taking the kinematics parameters into the account. Neurophysiological parameters have also been proposed for this purpose due to the novel advances in the non-invasive signal processing techniques. In addition, other parameters linked to the motor learning and brain plasticity occurring during the rehabilitation have been explored, looking for a more holistic rehabilitation approach. However, the majority of the research done in this area is still exploratory. These parameters have shown the capability to become the “biomarkers” that are defined as the quantifiable indicators of the physiological/pathological processes and the responses to the therapeutical interventions. In this view, they could be finally used for enhancing the robot-assisted treatments. While the research on the biomarkers has been growing in the last years, there is a current need for a better comprehension and quantification of the neuromechanical processes involved in the rehabilitation. In particular, there is a lack of operationalization of the potential neuromechanical biomarkers into the clinical algorithms. In this scenario, a new framework called the “Rehabilomics” has been proposed to account for the rehabilitation research that exploits the biomarkers in its design. This study provides an overview of the state-of-the-art of the biomarkers related to the robotic neurorehabilitation, focusing on the translational studies, and underlying the need to create the comprehensive approaches that have the potential to take the research on the biomarkers into the clinical practice. We then summarize some promising biomarkers that are being under investigation in the current literature and provide some examples of their current and/or potential applications in the neurorehabilitation. Finally, we outline the main challenges and future directions in the field, briefly discussing their potential evolution and prospective.
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Affiliation(s)
- Florencia Garro
- Rehab Technologies, Istituto Italiano di Tecnologia, Genoa, Italy.,Department of Informatics, Bioengineering, Robotics and Systems Engineering, University of Genoa, Genoa, Italy
| | - Michela Chiappalone
- Rehab Technologies, Istituto Italiano di Tecnologia, Genoa, Italy.,Department of Informatics, Bioengineering, Robotics and Systems Engineering, University of Genoa, Genoa, Italy
| | - Stefano Buccelli
- Rehab Technologies, Istituto Italiano di Tecnologia, Genoa, Italy
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15
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Brambilla C, Pirovano I, Mira RM, Rizzo G, Scano A, Mastropietro A. Combined Use of EMG and EEG Techniques for Neuromotor Assessment in Rehabilitative Applications: A Systematic Review. SENSORS (BASEL, SWITZERLAND) 2021; 21:7014. [PMID: 34770320 PMCID: PMC8588321 DOI: 10.3390/s21217014] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/23/2021] [Revised: 10/19/2021] [Accepted: 10/20/2021] [Indexed: 12/22/2022]
Abstract
Electroencephalography (EEG) and electromyography (EMG) are widespread and well-known quantitative techniques used for gathering biological signals at cortical and muscular levels, respectively. Indeed, they provide relevant insights for increasing knowledge in different domains, such as physical and cognitive, and research fields, including neuromotor rehabilitation. So far, EEG and EMG techniques have been independently exploited to guide or assess the outcome of the rehabilitation, preferring one technique over the other according to the aim of the investigation. More recently, the combination of EEG and EMG started to be considered as a potential breakthrough approach to improve rehabilitation effectiveness. However, since it is a relatively recent research field, we observed that no comprehensive reviews available nor standard procedures and setups for simultaneous acquisitions and processing have been identified. Consequently, this paper presents a systematic review of EEG and EMG applications specifically aimed at evaluating and assessing neuromotor performance, focusing on cortico-muscular interactions in the rehabilitation field. A total of 213 articles were identified from scientific databases, and, following rigorous scrutiny, 55 were analyzed in detail in this review. Most of the applications are focused on the study of stroke patients, and the rehabilitation target is usually on the upper or lower limbs. Regarding the methodological approaches used to acquire and process data, our results show that a simultaneous EEG and EMG acquisition is quite common in the field, but it is mostly performed with EMG as a support technique for more specific EEG approaches. Non-specific processing methods such as EEG-EMG coherence are used to provide combined EEG/EMG signal analysis, but rarely both signals are analyzed using state-of-the-art techniques that are gold-standard in each of the two domains. Future directions may be oriented toward multi-domain approaches able to exploit the full potential of combined EEG and EMG, for example targeting a wider range of pathologies and implementing more structured clinical trials to confirm the results of the current pilot studies.
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Affiliation(s)
- Cristina Brambilla
- Istituto di Sistemi e Tecnologie Industriali Intelligenti per il Manifatturiero Avanzato (STIIMA), Consiglio Nazionale delle Ricerche (CNR), Via Previati 1/E, 23900 Lecco, Italy; (C.B.); (R.M.M.); (A.S.)
| | - Ileana Pirovano
- Istituto di Tecnologie Biomediche (ITB), Consiglio Nazionale delle Ricerche (CNR), via Fratelli Cervi 93, 20054 Segrate, Italy; (I.P.); (A.M.)
| | - Robert Mihai Mira
- Istituto di Sistemi e Tecnologie Industriali Intelligenti per il Manifatturiero Avanzato (STIIMA), Consiglio Nazionale delle Ricerche (CNR), Via Previati 1/E, 23900 Lecco, Italy; (C.B.); (R.M.M.); (A.S.)
| | - Giovanna Rizzo
- Istituto di Tecnologie Biomediche (ITB), Consiglio Nazionale delle Ricerche (CNR), via Fratelli Cervi 93, 20054 Segrate, Italy; (I.P.); (A.M.)
| | - Alessandro Scano
- Istituto di Sistemi e Tecnologie Industriali Intelligenti per il Manifatturiero Avanzato (STIIMA), Consiglio Nazionale delle Ricerche (CNR), Via Previati 1/E, 23900 Lecco, Italy; (C.B.); (R.M.M.); (A.S.)
| | - Alfonso Mastropietro
- Istituto di Tecnologie Biomediche (ITB), Consiglio Nazionale delle Ricerche (CNR), via Fratelli Cervi 93, 20054 Segrate, Italy; (I.P.); (A.M.)
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16
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Irastorza-Landa N, García-Cossio E, Sarasola-Sanz A, Brötz D, Birbaumer N, Ramos-Murguialday A. Functional synergy recruitment index as a reliable biomarker of motor function and recovery in chronic stroke patients. J Neural Eng 2021; 18. [PMID: 33530072 DOI: 10.1088/1741-2552/abe244] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2020] [Accepted: 02/02/2021] [Indexed: 12/14/2022]
Abstract
Objective. Stroke affects the expression of muscle synergies underlying motor control, most notably in patients with poorer motor function. The majority of studies on muscle synergies have conventionally approached this analysis by assuming alterations in the inner structures of synergies after stroke. Although different synergy-based features based on this assumption have to some extent described pathological mechanisms in post-stroke neuromuscular control, a biomarker that reliably reflects motor function and recovery is still missing.Approach. Based on the theory of muscle synergies, we alternatively hypothesize that functional synergy structures are physically preserved and measure the temporal correlation between the recruitment profiles of healthy modules by paretic and healthy muscles, a feature hereafter reported as the FSRI. We measured clinical scores and extracted the muscle synergies of both ULs of 18 chronic stroke survivors from the electromyographic activity of 8 muscles during bilateral movements before and after 4 weeks of non-invasive BMI controlled robot therapy and physiotherapy. We computed the FSRI as well as features quantifying inter-limb structural differences and evaluated the correlation of these synergy-based measures with clinical scores.Main results. Correlation analysis revealed weak relationships between conventional features describing inter-limb synergy structural differences and motor function. In contrast, FSRI values during specific or combined movement data significantly correlated with UL motor function and recovery scores. Additionally, we observed that BMI-based training with contingent positive proprioceptive feedback led to improved FSRI values during the specific trained finger extension movement.Significance. We demonstrated that FSRI can be used as a reliable physiological biomarker of motor function and recovery in stroke, which can be targeted via BMI-based proprioceptive therapies and adjuvant physiotherapy to boost effective rehabilitation.
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Affiliation(s)
- Nerea Irastorza-Landa
- Neuroprosthetics Group, Institute of Medical Psychology and Behavioral Neurobiology, University of Tübingen, Tübingen, Germany.,International Max Planck Research School for Cognitive and Systems Neuroscience, Tübingen, Germany.,IKERBASQUE, Basque Foundation for Science, Bilbao, Spain.,Neurotechnology Laboratory, TECNALIA, Basque Research and Technology Alliance (BRTA), Donostia-San Sebastián, Spain
| | | | - Andrea Sarasola-Sanz
- Neuroprosthetics Group, Institute of Medical Psychology and Behavioral Neurobiology, University of Tübingen, Tübingen, Germany.,Neurotechnology Laboratory, TECNALIA, Basque Research and Technology Alliance (BRTA), Donostia-San Sebastián, Spain
| | - Doris Brötz
- Neuroprosthetics Group, Institute of Medical Psychology and Behavioral Neurobiology, University of Tübingen, Tübingen, Germany
| | - Niels Birbaumer
- Neuroprosthetics Group, Institute of Medical Psychology and Behavioral Neurobiology, University of Tübingen, Tübingen, Germany.,Wyss Center for Bio and Neuroengineering, Geneva, Switzerland
| | - Ander Ramos-Murguialday
- Neuroprosthetics Group, Institute of Medical Psychology and Behavioral Neurobiology, University of Tübingen, Tübingen, Germany.,Neurotechnology Laboratory, TECNALIA, Basque Research and Technology Alliance (BRTA), Donostia-San Sebastián, Spain
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17
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Pierella C, Pirondini E, Kinany N, Coscia M, Giang C, Miehlbradt J, Magnin C, Nicolo P, Dalise S, Sgherri G, Chisari C, Van De Ville D, Guggisberg A, Micera S. A multimodal approach to capture post-stroke temporal dynamics of recovery. J Neural Eng 2020; 17:045002. [DOI: 10.1088/1741-2552/ab9ada] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
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18
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Amano Y, Noma T, Etoh S, Miyata R, Kawamura K, Shimodozono M. Reaching exercise for chronic paretic upper extremity after stroke using a novel rehabilitation robot with arm-weight support and concomitant electrical stimulation and vibration: before-and-after feasibility trial. Biomed Eng Online 2020; 19:28. [PMID: 32375788 PMCID: PMC7203976 DOI: 10.1186/s12938-020-00774-3] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2019] [Accepted: 04/25/2020] [Indexed: 12/03/2022] Open
Abstract
Background Our group developed a rehabilitation robot to assist with repetitive, active reaching movement of a paretic upper extremity. The robot is equipped with a servo motor-controlled arm-weight support and works in conjunction with neuromuscular electrical stimulation and vibratory stimulation to facilitate agonist-muscle contraction. In this before-and-after pilot study, we assessed the feasibility of applying the robot to improve motor control and function of the hemiparetic upper extremity in patients who suffered chronic stroke. Methods We enrolled 6 patients with chronic stroke and hemiparesis who, while sitting and without assistance, could reach 10 cm both sagitally and vertically (from a starting position located 10 cm forward from the patient’s navel level) with the affected upper extremity. The patients were assigned to receive reaching exercise intervention with the robot (Yaskawa Electric Co., Ltd. Fukuoka, Japan) for 2 weeks at 15 min/day in addition to regular occupational therapy for 40 min/day. Outcomes assessed before and after 2 weeks of intervention included the upper extremity component of the Fugl-Meyer Assessment (UE-FMA), the Action Research Arm Test (ARAT), and, during reaching movement, kinematic analysis. Results None of the patients experienced adverse events. The mean score of UE-FMA increased from 44.8 [SD 14.4] to 48.0 [SD 14.4] (p = 0.026, r = 0.91), and both the shoulder–elbow and wrist–hand scores increased after 2-week intervention. An increase was also observed in ARAT score, from mean 29.8 [SD 16.3] to 36.2 [SD 18.1] (p = 0.042, r = 0.83). Kinematic analysis during the reaching movement revealed a significant increase in active range of motion (AROM) at the elbow, and movement time tended to decrease. Furthermore, trajectory length for the wrist (“hand path”) and the acromion (“trunk compensatory movement”) showed a decreasing trend. Conclusions This robot-assisted modality is feasible and our preliminary findings suggest it improved motor control and motor function of the hemiparetic upper extremity in patients with chronic stroke. Training with this robot might induce greater AROM for the elbow and decrease compensatory trunk movement, thus contributing to movement efficacy and efficiency. Trial registration UMIN Clinical Trial Registry, as UMIN000018132, on June 30, 2015. https://upload.umin.ac.jp/cgi-open-bin/ctr/ctr_view.cgi?recptno=R000020398
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Affiliation(s)
- Yumeko Amano
- Department of Rehabilitation and Physical Medicine, Kagoshima University Graduate School of Medical and Dental Sciences, 8-35-1 Sakuragaoka, Kagoshima, 890-8520, Japan
| | - Tomokazu Noma
- Kagoshima University Hospital Kirishima Rehabilitation Center, Kagoshima, Japan.,Department of Rehabilitation, Faculty of Health Science, Nihon Fukushi University, Higashi-nukumi-cho 26-2, Handa, Aichi, 475-0012, Japan
| | - Seiji Etoh
- Department of Rehabilitation and Physical Medicine, Kagoshima University Graduate School of Medical and Dental Sciences, 8-35-1 Sakuragaoka, Kagoshima, 890-8520, Japan
| | - Ryuji Miyata
- Department of Rehabilitation and Physical Medicine, Kagoshima University Graduate School of Medical and Dental Sciences, 8-35-1 Sakuragaoka, Kagoshima, 890-8520, Japan
| | - Kentaro Kawamura
- Department of Rehabilitation and Physical Medicine, Kagoshima University Graduate School of Medical and Dental Sciences, 8-35-1 Sakuragaoka, Kagoshima, 890-8520, Japan
| | - Megumi Shimodozono
- Department of Rehabilitation and Physical Medicine, Kagoshima University Graduate School of Medical and Dental Sciences, 8-35-1 Sakuragaoka, Kagoshima, 890-8520, Japan.
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A Low-Cost Soft Robotic Hand Exoskeleton for Use in Therapy of Limited Hand–Motor Function. APPLIED SCIENCES-BASEL 2019. [DOI: 10.3390/app9183751] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
We present the design and validation of a low-cost, customizable and 3D-printed anthropomorphic soft robotic hand exoskeleton for rehabilitation of hand injuries using remotely administered physical therapy regimens. The design builds upon previous work done on cable actuated exoskeleton designs by implementing the same kinematic functionality, but with the focus shifted to ease of assembly and cost effectiveness as to allow patients and physicians to manufacture and assemble the hardware necessary to implement treatment. The exoskeleton was constructed solely from 3D-printed and widely available off-the-shelf components. Control of the actuators was realized using an Arduino microcontroller, with a custom-designed shield to facilitate ease of wiring. Tests were conducted to verify that the range of motion of the digits and the forces exerted at the fingertip coincided with those of a healthy human hand.
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Abstract
The development of robotic devices for rehabilitation is a fast-growing field. Nowadays, thanks to novel technologies that have improved robots’ capabilities and offered more cost-effective solutions, robotic devices are increasingly being employed during clinical practice, with the goal of boosting patients’ recovery. Robotic rehabilitation is also widely used in the context of neurological disorders, where it is often provided in a variety of different fashions, depending on the specific function to be restored. Indeed, the effect of robot-aided neurorehabilitation can be maximized when used in combination with a proper training regimen (based on motor control paradigms) or with non-invasive brain machine interfaces. Therapy-induced changes in neural activity and behavioral performance, which may suggest underlying changes in neural plasticity, can be quantified by multimodal assessments of both sensorimotor performance and brain/muscular activity pre/post or during intervention. Here, we provide an overview of the most common robotic devices for upper and lower limb rehabilitation and we describe the aforementioned neurorehabilitation scenarios. We also review assessment techniques for the evaluation of robotic therapy. Additional exploitation of these research areas will highlight the crucial contribution of rehabilitation robotics for promoting recovery and answering questions about reorganization of brain functions in response to disease.
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