Proximal arm non-use when reaching after a stroke

Bakhti, Karima; Mottet, Denis; Schweighofer, Nicolas; Froger, Jérôme; Laffont, I.

doi:10.1016/j.neulet.2017.07.055

Cited by 34 publications

(73 citation statements)

References 28 publications

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“…However, the learned non-use phenomenon is more general than this specific case: it applies to any cases when individuals “forget” how to use their affected body parts and instead overuse compensations [ 2 ]. Here, our reasoning is that the learned non-use phenomenon explains why some patients “forget” how to use the proximal joints of their paretic UL and instead overuse trunk compensation during reaching [ 3 ]. Long-term non-use of shoulder-elbow movements is suspected to be detrimental to optimal upper limb functional recovery, due the “Use it or lose it” principle of use-dependent neural plasticity [ 4 , 5 ].…”

Section: Introductionmentioning

confidence: 99%

“…In our previous study [ 3 ], the non-use of shoulder-elbow joints during reaching has been termed “proximal arm non-use” and was assessed with the PANU score. The PANU score was derived by the substraction of the spontaneous proximal arm-use (SPAU) from the maximal proximal arm-use (MPAU).…”

Section: Introductionmentioning

confidence: 99%

“…Higher PANU scores were moderately related to higher UL impairment (Fugl Meyer Assessment of the upper extremity-FM-UE) and to lower UL function (Box & Block Test-BBT). Moreover, 61% of patients with lower impairment (FM-UE proximal > 28/42) exhibited proximal arm non-use (PANU score > 6.5%) [ 3 ]. The PANU score provides information about the remaining functional motor reserve of the paretic arm (at the level of shoulder and elbow movements), which is not used by the post-stroke individual.…”

Section: Introductionmentioning

confidence: 99%

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Kinect-based assessment of proximal arm non-use after a stroke

Bakhti

Laffont

Muthalib

et al. 2018

J NeuroEngineering Rehabil

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BackgroundAfter a stroke, during seated reaching with their paretic upper limb, many patients spontaneously replace the use of their arm by trunk compensation movements, even though they are able to use their arm when forced to do so. We previously quantified this proximal arm non-use (PANU) with a motion capture system (Zebris, CMS20s). The aim of this study was to validate a low-cost Microsoft Kinect-based system against the CMS20s reference system to diagnose PANU.MethodsIn 19 hemiparetic stroke individuals, the PANU score, reach length, trunk length, and proximal arm use (PAU) were measured during seated reaching simultaneously by the Kinect (v2) and the CMS20s over two testing sessions separated by two hours.ResultsIntraclass correlation coefficients (ICC) and linear regression analysis showed that the PANU score (ICC = 0.96, r2 = 0.92), reach length (ICC = 0.81, r2 = 0.68), trunk length (ICC = 0.97, r2 = 0.94) and PAU (ICC = 0.97, r2 = 0.94) measured using the Kinect were strongly related to those measured using the CMS20s. The PANU scores showed good test-retest reliability for both the Kinect (ICC = 0.76) and CMS20s (ICC = 0.72). Bland and Altman plots showed slightly reduced PANU scores in the re-test session for both systems (Kinect: − 4.25 ± 6.76; CMS20s: − 4.71 ± 7.88), which suggests a practice effect.ConclusionWe showed that the Kinect could accurately and reliably assess PANU, reach length, trunk length and PAU during seated reaching in post stroke individuals. We conclude that the Kinect can offer a low-cost and widely available solution to clinically assess PANU for individualised rehabilitation and to monitor the progress of paretic arm recovery.Trial registrationThe study was approved by The Ethics Committee of Montpellier, France (N°ID-RCB: 2014-A00395–42) and registered in Clinical Trial (N° NCT02326688, Registered on 15 December 2014, https://clinicaltrials.gov/ct2/show/results/NCT02326688).

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Kinect-based assessment of proximal arm non-use after a stroke

Bakhti

Laffont

Muthalib

et al. 2018

J NeuroEngineering Rehabil

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“…We observed, first, that approximately 40% of patients with mild impairment on the upper extremity Fugl-Meyer (UEFM) nevertheless used their contralesional limb spontaneously less than half the time, broadly consistent with prior reports of NU frequency. 33 We showed, further, that non-use, as indexed by the disparity between spontaneous and forced limb use, was not related to demographic factors or hemisphere of lesion in this sample. On the other hand, scores on a common measure of sensory-motor impairment, the UEFM, predicted the degree of non-use, and also predicted spontaneous amount of use and quality of movement.…”

Section: Discussionmentioning

confidence: 44%

“…Thus, patients with greater sensory-motor impairment showed a larger relative disparity between their arm use in the forced use condition and the spontaneous use condition, again consistent with prior observations. 7,33 In other words, the disparity between capability and use is not monotonic, suggesting that non-use is a particularly important issue in moderatelyimpaired (as compared to mildly-impaired) individuals. Importantly, extending prior observations, we demonstrated that non-lateralized attention/arousal predicted both spontaneous use and non-use (but not forced use) above and beyond sensory-motor impairment.…”

Section: Discussionmentioning

confidence: 99%

Predictors of arm non-use in chronic stroke: a preliminary investigation

Buxbaum

Varghese

Stoll

et al. 2019

Preprint

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Background:The phenomenon of non-use after stroke is characterized by failure to use the contralesional arm despite adequate capacity. It has been suggested that nonuse is a consequence of the greater effort and/or attention required to use the affected limb, but such accounts have not been tested, and we have poor understanding of the characteristics of individuals who exhibit non-use.Objective: We aimed to provide preliminary evidence regarding the demographic, neuropsychological, and psychological factors that may influence non-use in chronic stroke.Methods: Twenty chronic stroke survivors (10 left and 10 right hemisphere stroke) with mild-to-moderate sensory-motor impairment on the upper extremity Fugl-Meyer (UEFM)were assessed with the Actual Amount of Use Test (AAUT), which measures the disparity between amount of use and quality of movement in "spontaneous" versus "forced" conditions. Participants were als assessed with measures of limb apraxia, spatial neglect, attention/arousal, and self-efficacy (confidence in arm movement).Using stepwise multiple regression, we determined whether demographic information and/or performance on these measures predicted AAUT non-use scores.Results: Scores on the UEFM as well as attention/arousal and self-efficacy predicted the degree of non-use. Attention/arousal predicted non-use above and beyond UEFM. Conclusions:Given the complexity of the non-use phenomenon, it follows that a combination of impairment, attention/arousal, and perceived confidence predicted nonuse behavior. That a measure of attention/arousal predicted non-use behavior above and beyond sensory-motor functioning highlights the importance of motivated Predictors of non-use in stroke 3 engagement to drive use of the paretic limb. Larger-scale studies incorporating additional measures (e.g., mental health, lesion volume and white matter connectivity, pain, motivation) will be important for future investigations.

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Improving Output Visualization of an Algorithm for the Automated Detection of the Perceived Origin of Movement

Gnecco,

Fausto,

Romano

et al. 2024

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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The perceived Origin of full-body human Movement (OoM), i.e., the part of the body that is perceived by an external observer as the joint from which movement originates, represents a relevant topic for movement analysis. Indeed, its automated detection is important to contribute to the automated analysis of full-body emotions and of non-verbal social signals, and has potential applications, among others, in dance and music teaching, cognitive and motor rehabilitation, sport, and entertainment. In this work, we further develop a recently proposed algorithm for the automated detection of the perceived OoM, by improving the visualization of its output. Specifically, the core of that algorithm relies on clustering a skeletal representation of the human body based on the values assumed by a movement-related feature on all its vertices, then finding those vertices that are at the boundary between any two resulting clusters. In the work, we improve the visualization of the clusters generated by that algorithm in successive frames, by “colouring” them by means of the resolution of a sequence of minimum cost bipartite matching subproblems. Finally, based on a real-world dataset, we show that the proposed modification of the algorithm provides, indeed, a better visualization of the clusters than its original version.

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Proximal arm non-use when reaching after a stroke

Cited by 34 publications

References 28 publications

Kinect-based assessment of proximal arm non-use after a stroke

Kinect-based assessment of proximal arm non-use after a stroke

Predictors of arm non-use in chronic stroke: a preliminary investigation

Improving Output Visualization of an Algorithm for the Automated Detection of the Perceived Origin of Movement

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