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Rezende AR, Alves CM, Marques IA, de Souza LAPS, Naves ELM. Muscle Tone Assessment by Machine Learning Using Surface Electromyography. SENSORS (BASEL, SWITZERLAND) 2024; 24:6362. [PMID: 39409402 PMCID: PMC11479050 DOI: 10.3390/s24196362] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/09/2024] [Revised: 09/25/2024] [Accepted: 09/27/2024] [Indexed: 10/20/2024]
Abstract
Muscle tone is defined as the resistance to passive stretch, but this definition is often criticized for its ambiguity since some suggest it is related to a state of preparation for movement. Muscle tone is primarily regulated by the central nervous system, and individuals with neurological disorders may lose the ability to control normal tone and can exhibit abnormalities. Currently, these abnormalities are mostly evaluated using subjective scales, highlighting a lack of objective assessment methods in the literature. This study aimed to use surface electromyography (sEMG) and machine learning (ML) for the objective classification and characterization of the full spectrum of muscle tone in the upper limb. Data were collected from thirty-nine individuals, including spastic, healthy, hypotonic and rigid subjects. All of the classifiers applied achieved high accuracy, with the best reaching 96.12%, in differentiating muscle tone. These results underscore the potential of the proposed methodology as a more reliable and quantitative method for evaluating muscle tone abnormalities, aiming to address the limitations of traditional subjective assessments. Additionally, the main features impacting the classifiers' performance were identified, which can be utilized in future research and in the development of devices that can be used in clinical practice.
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Affiliation(s)
- Andressa Rastrelo Rezende
- Assistive Technology Laboratory, Faculty of Electrical Engineering, Federal University of Uberlandia, Uberlandia 38400-902, Brazil; (C.M.A.); (I.A.M.)
| | - Camille Marques Alves
- Assistive Technology Laboratory, Faculty of Electrical Engineering, Federal University of Uberlandia, Uberlandia 38400-902, Brazil; (C.M.A.); (I.A.M.)
| | - Isabela Alves Marques
- Assistive Technology Laboratory, Faculty of Electrical Engineering, Federal University of Uberlandia, Uberlandia 38400-902, Brazil; (C.M.A.); (I.A.M.)
| | | | - Eduardo Lázaro Martins Naves
- Assistive Technology Laboratory, Faculty of Electrical Engineering, Federal University of Uberlandia, Uberlandia 38400-902, Brazil; (C.M.A.); (I.A.M.)
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Levin MF, Piscitelli D, Khayat J. Tonic stretch reflex threshold as a measure of disordered motor control and spasticity - A critical review. Clin Neurophysiol 2024; 165:138-150. [PMID: 39029274 DOI: 10.1016/j.clinph.2024.06.019] [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: 01/12/2024] [Revised: 05/07/2024] [Accepted: 06/15/2024] [Indexed: 07/21/2024]
Abstract
The Tonic Stretch Reflex Threshold (TSRT) is the joint angle or muscle length (λ) at which muscle activation begins. In spasticity, the TSRT abnormally lies inside the biomechanical joint range. It is determined by measuring the Dynamic Stretch Reflex Thresholds (DSRTs) by stretching the resting muscle at different velocities. The metric μ, characterizes the velocity-sensitivity of the DSRTs and is expressed as the time required to lengthen the passive muscles from DSRT to TSRT at the respective stretch velocity. The original formulation of the TSRT, DSRT and μ is summarized. Then, a thorough search of literature prior to December 2023 was conducted that returned 25 papers that have used the technique. Eleven of these papers come from the research group of the authors, including 1 reporting on treatment effects. Of the remaining 14 papers, 11 report variations of the methodology with different populations and 3 report on the effects of an intervention. The review discusses how specific modifications to data collection and analysis procedures have either improved the methodology or, in some cases, led to uninterpretable results. The influence of modifications to the data collection and analysis procedures is discussed.
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Affiliation(s)
- Mindy F Levin
- School of Physical and Occupational Therapy, Faculty of Medicine and Health Sciences, McGill University, Montreal, Quebec H3G 1Y5, Canada; Center for Interdisciplinary Research in Rehabilitation (CRIR), Montreal, Quebec, Canada.
| | - Daniele Piscitelli
- Doctor of Physical Therapy Program, Department of Kinesiology, University of Connecticut, Storrs, CT, USA.
| | - Joy Khayat
- School of Physical and Occupational Therapy, Faculty of Medicine and Health Sciences, McGill University, Montreal, Quebec H3G 1Y5, Canada; Center for Interdisciplinary Research in Rehabilitation (CRIR), Montreal, Quebec, Canada.
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He J, Luo A, Yu J, Qian C, Liu D, Hou M, Ma Y. Quantitative assessment of spasticity: a narrative review of novel approaches and technologies. Front Neurol 2023; 14:1121323. [PMID: 37475737 PMCID: PMC10354649 DOI: 10.3389/fneur.2023.1121323] [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: 03/11/2023] [Accepted: 06/19/2023] [Indexed: 07/22/2023] Open
Abstract
Spasticity is a complex neurological disorder, causing significant physical disabilities and affecting patients' independence and quality of daily lives. Current spasticity assessment methods are questioned for their non-standardized measurement protocols, limited reliabilities, and capabilities in distinguishing neuron or non-neuron factors in upper motor neuron lesion. A series of new approaches are developed for improving the effectiveness of current clinical used spasticity assessment methods with the developing technology in biosensors, robotics, medical imaging, biomechanics, telemedicine, and artificial intelligence. We investigated the reliabilities and effectiveness of current spasticity measures employed in clinical environments and the newly developed approaches, published from 2016 to date, which have the potential to be used in clinical environments. The new spasticity scales, taking advantage of quantified information such as torque, or echo intensity, the velocity-dependent feature and patients' self-reported information, grade spasticity semi-quantitatively, have competitive or better reliability than previous spasticity scales. Medical imaging technologies, including near-infrared spectroscopy, magnetic resonance imaging, ultrasound and thermography, can measure muscle hemodynamics and metabolism, muscle tissue properties, or temperature of tissue. Medical imaging-based methods are feasible to provide quantitative information in assessing and monitoring muscle spasticity. Portable devices, robotic based equipment or myotonometry, using information from angular, inertial, torque or surface EMG sensors, can quantify spasticity with the help of machine learning algorithms. However, spasticity measures using those devices are normally not physiological sound. Repetitive peripheral magnetic stimulation can assess patients with severe spasticity, which lost voluntary contractions. Neuromusculoskeletal modeling evaluates the neural and non-neural properties and may gain insights into the underlying pathology of spasticity muscles. Telemedicine technology enables outpatient spasticity assessment. The newly developed spasticity methods aim to standardize experimental protocols and outcome measures and enable quantified, accurate, and intelligent assessment. However, more work is needed to investigate and improve the effectiveness and accuracy of spasticity assessment.
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Affiliation(s)
- Jian He
- Research Academy of Grand Health, Faculty of Sports Sciences, Ningbo University, Ningbo, China
| | - Anhua Luo
- Research Academy of Grand Health, Faculty of Sports Sciences, Ningbo University, Ningbo, China
| | - Jiajia Yu
- Research Academy of Grand Health, Faculty of Sports Sciences, Ningbo University, Ningbo, China
| | - Chengxi Qian
- Research Academy of Grand Health, Faculty of Sports Sciences, Ningbo University, Ningbo, China
| | - Dongwei Liu
- School of Information Management and Artificial Intelligence, Zhejiang University of Finance and Economics, Hangzhou, China
| | - Meijin Hou
- National Joint Engineering Research Centre of Rehabilitation Medicine Technology, Fujian University of Traditional Chinese Medicine, Fuzhou, China
- Key Laboratory of Orthopaedics and Traumatology of Traditional Chinese Medicine and Rehabilitation (Fujian University of TCM), Ministry of Education, Fuzhou, China
| | - Ye Ma
- Research Academy of Grand Health, Faculty of Sports Sciences, Ningbo University, Ningbo, China
- National Joint Engineering Research Centre of Rehabilitation Medicine Technology, Fujian University of Traditional Chinese Medicine, Fuzhou, China
- Key Laboratory of Orthopaedics and Traumatology of Traditional Chinese Medicine and Rehabilitation (Fujian University of TCM), Ministry of Education, Fuzhou, China
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Muscle Tonus Evaluation in Patients with Neurological Disorders: A Scoping Review. J Med Biol Eng 2023. [DOI: 10.1007/s40846-023-00773-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/29/2023]
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Guo X, Wallace R, Tan Y, Oetomo D, Klaic M, Crocher V. Technology-assisted assessment of spasticity: a systematic review. J Neuroeng Rehabil 2022; 19:138. [PMID: 36494721 PMCID: PMC9733065 DOI: 10.1186/s12984-022-01115-2] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2022] [Accepted: 11/23/2022] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND Spasticity is defined as "a motor disorder characterised by a velocity dependent increase in tonic stretch reflexes (muscle tone) with exaggerated tendon jerks". It is a highly prevalent condition following stroke and other neurological conditions. Clinical assessment of spasticity relies predominantly on manual, non-instrumented, clinical scales. Technology based solutions have been developed in the last decades to offer more specific, sensitive and accurate alternatives but no consensus exists on these different approaches. METHOD A systematic review of literature of technology-based methods aiming at the assessment of spasticity was performed. The approaches taken in the studies were classified based on the method used as well as their outcome measures. The psychometric properties and usability of the methods and outcome measures reported were evaluated. RESULTS 124 studies were included in the analysis. 78 different outcome measures were identified, among which seven were used in more than 10 different studies each. The different methods rely on a wide range of different equipment (from robotic systems to simple goniometers) affecting their cost and usability. Studies equivalently applied to the lower and upper limbs (48% and 52%, respectively). A majority of studies applied to a stroke population (N = 79). More than half the papers did not report thoroughly the psychometric properties of the measures. Analysis identified that only 54 studies used measures specific to spasticity. Repeatability and discriminant validity were found to be of good quality in respectively 25 and 42 studies but were most often not evaluated (N = 95 and N = 78). Clinical validity was commonly assessed only against clinical scales (N = 33). Sensitivity of the measure was assessed in only three studies. CONCLUSION The development of a large diversity of assessment approaches appears to be done at the expense of their careful evaluation. Still, among the well validated approaches, the ones based on manual stretching and measuring a muscle activity reaction and the ones leveraging controlled stretches while isolating the stretch-reflex torque component appear as the two promising practical alternatives to clinical scales. These methods should be further evaluated, including on their sensitivity, to fully inform on their potential.
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Affiliation(s)
- Xinliang Guo
- grid.1008.90000 0001 2179 088XUoM and Fourier Intelligence Joint Robotics Laboratory, Mechanical Engineering Department, The University of Melbourne, Melbourne, Australia
| | - Rebecca Wallace
- grid.416153.40000 0004 0624 1200Allied Health Department, The Royal Melbourne Hospital, Melbourne, Australia
| | - Ying Tan
- grid.1008.90000 0001 2179 088XUoM and Fourier Intelligence Joint Robotics Laboratory, Mechanical Engineering Department, The University of Melbourne, Melbourne, Australia
| | - Denny Oetomo
- grid.1008.90000 0001 2179 088XUoM and Fourier Intelligence Joint Robotics Laboratory, Mechanical Engineering Department, The University of Melbourne, Melbourne, Australia
| | - Marlena Klaic
- grid.1008.90000 0001 2179 088XSchool of Health Sciences, The University of Melbourne, Melbourne, Australia
| | - Vincent Crocher
- grid.1008.90000 0001 2179 088XUoM and Fourier Intelligence Joint Robotics Laboratory, Mechanical Engineering Department, The University of Melbourne, Melbourne, Australia
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Virtual reality and serious game therapy for post-stroke individuals: A preliminary study with humanized rehabilitation approach protocol humanized rehabilitation approach. Complement Ther Clin Pract 2022; 49:101681. [DOI: 10.1016/j.ctcp.2022.101681] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2022] [Revised: 09/26/2022] [Accepted: 10/29/2022] [Indexed: 11/06/2022]
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Alves CM, Rezende AR, Marques IA, Mendes LC, de Sá AAR, Vieira MF, Júnior EAL, Pereira AA, Oliveira FHM, de Souza LPS, Bourhis G, Pino P, Andrade ADO, Morère Y, Naves ELM. Wrist Rigidity Evaluation in Parkinson's Disease: A Scoping Review. Healthcare (Basel) 2022; 10:2178. [PMID: 36360519 PMCID: PMC9690338 DOI: 10.3390/healthcare10112178] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2022] [Revised: 10/21/2022] [Accepted: 10/26/2022] [Indexed: 08/13/2024] Open
Abstract
(1) Background: One of the main cardinal signs of Parkinson's disease (PD) is rigidity, whose assessment is important for monitoring the patient's recovery. The wrist is one of the joints most affected by this symptom, which has a great impact on activities of daily living and consequently on quality of life. The assessment of rigidity is traditionally made by clinical scales, which have limitations due to their subjectivity and low intra- and inter-examiner reliability. (2) Objectives: To compile the main methods used to assess wrist rigidity in PD and to study their validity and reliability, a scope review was conducted. (3) Methods: PubMed, IEEE/IET Electronic Library, Web of Science, Scopus, Cochrane, Bireme, Google Scholar and Science Direct databases were used. (4) Results: Twenty-eight studies were included. The studies presented several methods for quantitative assessment of rigidity using instruments such as force and inertial sensors. (5) Conclusions: Such methods present good correlation with clinical scales and are useful for detecting and monitoring rigidity. However, the development of a standard quantitative method for assessing rigidity in clinical practice remains a challenge.
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Affiliation(s)
- Camille Marques Alves
- Assistive Technology Laboratory (NTA), Faculty of Electrical Engineering, Federal University of Uberlândia, Uberlândia 38400-902, Brazil
- Laboratoire de Conception, d’Optimisation et de Modélisation des Systèmes (LCOMS), Université de Lorraine, 57070 Metz, France
| | - Andressa Rastrelo Rezende
- Assistive Technology Laboratory (NTA), Faculty of Electrical Engineering, Federal University of Uberlândia, Uberlândia 38400-902, Brazil
| | - Isabela Alves Marques
- Laboratoire de Conception, d’Optimisation et de Modélisation des Systèmes (LCOMS), Université de Lorraine, 57070 Metz, France
- Centre for Innovation and Technology Assessment in Health (NIATS), Faculty of Electrical Engineering, Federal University of Uberlândia, Uberlândia 38400-902, Brazil
| | - Luanne Cardoso Mendes
- Laboratoire de Conception, d’Optimisation et de Modélisation des Systèmes (LCOMS), Université de Lorraine, 57070 Metz, France
- Centre for Innovation and Technology Assessment in Health (NIATS), Faculty of Electrical Engineering, Federal University of Uberlândia, Uberlândia 38400-902, Brazil
| | - Angela Abreu Rosa de Sá
- Assistive Technology Laboratory (NTA), Faculty of Electrical Engineering, Federal University of Uberlândia, Uberlândia 38400-902, Brazil
| | - Marcus Fraga Vieira
- Bioengineering and Biomechanics Laboratory (Labioeng), Federal University of Goiás, Goiânia 74690-900, Brazil
| | - Edgard Afonso Lamounier Júnior
- Computer Graphics Laboratory (CG), Faculty of Electrical Engineering, Federal University of Uberlândia, Uberlândia 38400-902, Brazil
| | - Adriano Alves Pereira
- Centre for Innovation and Technology Assessment in Health (NIATS), Faculty of Electrical Engineering, Federal University of Uberlândia, Uberlândia 38400-902, Brazil
| | | | | | - Guy Bourhis
- Laboratoire de Conception, d’Optimisation et de Modélisation des Systèmes (LCOMS), Université de Lorraine, 57070 Metz, France
| | - Pierre Pino
- Laboratoire de Conception, d’Optimisation et de Modélisation des Systèmes (LCOMS), Université de Lorraine, 57070 Metz, France
| | - Adriano de Oliveira Andrade
- Centre for Innovation and Technology Assessment in Health (NIATS), Faculty of Electrical Engineering, Federal University of Uberlândia, Uberlândia 38400-902, Brazil
| | - Yann Morère
- Laboratoire de Conception, d’Optimisation et de Modélisation des Systèmes (LCOMS), Université de Lorraine, 57070 Metz, France
| | - Eduardo Lázaro Martins Naves
- Assistive Technology Laboratory (NTA), Faculty of Electrical Engineering, Federal University of Uberlândia, Uberlândia 38400-902, Brazil
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Guo X, Tang J, Crocher V, Klaic M, Oetomo D, Xie Q, Galea MP, Niu CM, Tan Y. A Practical Post-Stroke Elbow Spasticity Assessment Using an Upper Limb Rehabilitation Robot: A Validation Study. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2022; 2022:4159-4162. [PMID: 36086384 DOI: 10.1109/embc48229.2022.9871423] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Spasticity is a motor disorder characterised by a velocity-dependent increase in muscle tone, which is critical in neurorehabilitation given its high prevalence and potential negative influence among the post-stroke population. Accurate measurement of spasticity is important as it guides the strategy of spasticity treatment and evaluates the effectiveness of spasticity management. However, spasticity is commonly measured using clinical scales which may lack objectivity and reliability. Although many technology-assisted measures have been developed, showing their potential as accurate and reliable alternatives to standard clinical scales, they have not been widely adopted in clinical practice due to their low usability and feasibility. This paper thus introduces an easy-to-use robotic based measure of elbow spasticity and its evaluation protocol. Preliminary results collected with one post-stroke patient and one healthy control subject are presented and demonstrate the feasibility of the approach.
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Rezende AR, Marques IA, Alves CM, Morais Shinosaki JS, Martins Naves EL. Effect of botulinum toxin on spasticity level assessed by tonic stretch reflex threshold: a feasibility pilot study. Ing Rech Biomed 2022. [DOI: 10.1016/j.irbm.2022.02.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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