Faster Reaching in Chronic Spastic Stroke Patients Comes at the Expense of Arm-Trunk Coordination

Mandon, L.; Boudarham, Julien; Robertson, Johanna; Bensmaïl, Djamel; Roche, Nicolás; Roby-Brami, Agnès

doi:10.1177/1545968315591704

Cited by 37 publications

(21 citation statements)

References 69 publications

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“…Consistent with the results of previous studies [50][51][52], stroke survivors exhibited significantly higher trunk movements during seated reaching tasks with their affected arm than with the healthy arm. Excessive use of compensatory movements can result in muscle contractures, joint misalignment, pain, limb disuse, and increased energy expenditure.…”

Section: Future Worksupporting

confidence: 91%

Detecting compensatory movements of stroke survivors using pressure distribution data and machine learning algorithms

Cai

Zhang

et al. 2019

J NeuroEngineering Rehabil

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Background: Compensatory movements are commonly employed by stroke survivors during seated reaching and may have negative effects on their long-term recovery. Detecting compensation is useful for coaching the patient to reduce compensatory trunk movements and improving the motor function of the paretic arm. Sensor-based and camera-based systems have been developed to detect compensatory movements, but they still have some limitations, such as causing object obstructions, requiring complex setups and raising privacy concerns. To overcome these drawbacks, this paper proposes a compensatory movement detection system based on pressure distribution data and is unobtrusive, simple and practical. Machine learning algorithms were applied to classify compensatory movements automatically. Therefore, the purpose of this study was to develop and test a pressure distribution-based system for the automatic detection of compensation movements of stroke survivors using machine learning algorithms. Methods: Eight stroke survivors performed three types of reaching tasks (back-and-forth, side-to-side, and up-anddown reaching tasks) with both the healthy side and the affected side. The pressure distribution data were recorded, and five features were extracted for classification. The k-nearest neighbor (k-NN) and support vector machine (SVM) algorithms were applied to detect and categorize the compensatory movements. The surface electromyography (sEMG) signals of nine trunk muscles were acquired to provide a detailed description and explanation of compensatory movements. Results: Cross-validation yielded high classification accuracies (F1-score>0.95) for both the k-NN and SVM classifiers in detecting compensation movements during all the reaching tasks. In detail, an excellent performance was achieved in discriminating between compensation and noncompensation (NC) movements, with an average F1score of 0.993. For the multiclass classification of compensatory movement patterns, an average F1-score of 0.981 was achieved in recognizing the NC, trunk lean-forward (TLF), trunk rotation (TR) and shoulder elevation (SE) movements. Conclusions: Good classification performance in detecting and categorizing compensatory movements validated the feasibility of the proposed pressure distribution-based system. Reliable classification accuracy achieved by the machine learning algorithms indicated the potential to monitor compensation movements automatically by using the pressure distribution-based system when stroke survivors perform seated reaching tasks.

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Section: Future Worksupporting

confidence: 91%

Detecting compensatory movements of stroke survivors using pressure distribution data and machine learning algorithms

Cai

Zhang

et al. 2019

J NeuroEngineering Rehabil

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“…A high degree of manual dexterity represents a hallmark of human (and primate) upper limb function. Upper arm control, such as in reach, i.e., transporting the hand to the object, is a prerequisite for dexterity and this emphasizes the need to combine evaluation of proximal arm control with the assessment of distal manual dexterity [ 26 ]. Coordinated hand and finger movements and versatile use, comprising functional opposites such as strength vs. precision, synergistic vs. individuated finger movements, or stability (e.g.…”

Section: Discussionmentioning

confidence: 99%

Upper Limb Outcome Measures Used in Stroke Rehabilitation Studies: A Systematic Literature Review

et al. 2016

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BackgroundEstablishing which upper limb outcome measures are most commonly used in stroke studies may help in improving consensus among scientists and clinicians.ObjectiveIn this study we aimed to identify the most commonly used upper limb outcome measures in intervention studies after stroke and to describe domains covered according to ICF, how measures are combined, and how their use varies geographically and over time.MethodsPubmed, CinHAL, and PeDRO databases were searched for upper limb intervention studies in stroke according to PRISMA guidelines and477 studies were included.ResultsIn studies 48different outcome measures were found. Only 15 of these outcome measures were used in more than 5% of the studies. The Fugl-Meyer Test (FMT)was the most commonly used measure (in 36% of studies). Commonly used measures covered ICF domains of body function and activity to varying extents. Most studies (72%) combined multiple outcome measures: the FMT was often combined with the Motor Activity Log (MAL), the Wolf Motor Function Test and the Action Research Arm Test, but infrequently combined with the Motor Assessment Scale or the Nine Hole Peg Test. Key components of manual dexterity such as selective finger movements were rarely measured. Frequency of use increased over a twelve-year period for the FMT and for assessments of kinematics, whereas other measures, such as the MAL and the Jebsen Taylor Hand Test showed decreased use over time. Use varied largely between countries showing low international consensus.ConclusionsThe results showed a large diversity of outcome measures used across studies. However, a growing number of studies used the FMT, a neurological test with good psychometric properties. For thorough assessment the FMT needs to be combined with functional measures. These findings illustrate the need for strategies to build international consensus on appropriate outcome measures for upper limb function after stroke.

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“…In the time-based BART, the time constraint puts pressure on movement time as well as decision time. It is known that faster reaching is more challenging than reaching at preferred speed ( 21 ). Our results therefore indicate that, under time constraint, individuals with stroke decrease use of their more affected arm to maximize success with the task.…”

Section: Discussionmentioning

confidence: 99%

Measuring Habitual Arm Use Post-stroke With a Bilateral Time-Constrained Reaching Task

et al. 2018

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Background: Spontaneous use of the more-affected arm is a meaningful indicator of stroke recovery. The Bilateral Arm Reaching Test (BART) was previously developed to quantify arm use by measuring arm choice to targets projected over a horizontal hemi-workspace. In order to improve clinical validity, we constrained the available movement time, thereby promoting more spontaneous decision making when selecting between the more-affected and less affected arm during the BART.Methods: Twenty-two individuals with mild to moderate hemiparesis were tested with the time-based BART in three time-constraint conditions: no-time constraint, medium, and fast conditions. Arm use was measured across three sessions with a 2-week interval in a spontaneous choice block, in which participants were instructed to use either the more-affected or the less-affected arm to reach targets. We tested the effect of time-constraint condition on the more-affected arm use, external validity of the BART with the Actual Amount of Use Test (AAUT), and test-retest reliability across the three test sessions.Results: The fast condition in the time-based BART showed reduced use of the more-affected arm compared to the no-time constraint condition P < 0.0001) and the medium condition P = 0.0006; Tukey post hoc analysis after mixed-effect linear regression). In addition, the fast condition showed strong correlation with the AAUT r = 0.829, P < 0.001), and excellent test-retest reliability (ICC = 0.960, P < 0.0001).Conclusion: The revised BART with a time-restricted fast condition provides an objective, accurate, and repeatable measure of spontaneous arm use in individuals with chronic stroke hemiparesis.

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Faster Reaching in Chronic Spastic Stroke Patients Comes at the Expense of Arm-Trunk Coordination

Abstract: Faster speed may encourage some patients to use compensation. Individual indications for therapy could be based on a quantitative analysis of reaching coordination.

Cited by 37 publications

References 69 publications

Detecting compensatory movements of stroke survivors using pressure distribution data and machine learning algorithms

Detecting compensatory movements of stroke survivors using pressure distribution data and machine learning algorithms

Upper Limb Outcome Measures Used in Stroke Rehabilitation Studies: A Systematic Literature Review

Measuring Habitual Arm Use Post-stroke With a Bilateral Time-Constrained Reaching Task

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