Non-utilisation épaule-coude après un accident vasculaire cérébral

The use of compensatory strategies to perform activities of daily living after a stroke can reduce the upper limb recovery. In fact, post-stroke individuals with severe upper limb impairment that limits shoulder-elbow motion, have to use trunk compensation to achieve a reaching task within arms’ length, which is a form of mandatory/adaptive compensation strategy. Whereas, post-stroke individuals having adequately recovered shoulder-elbow motion, continue to use the trunk when they could use the proximal arm to achieve the reach; and this non-mandatory trunk compensation is considered maladaptive because it reflects proximal arm non-use or PANU, which is detrimental to true recovery of the paretic arm.In the first study, 45 post-stroke individuals and 45 age matched healthy controls performed a seated reaching task within arm’s length with the trunk free to move (spontaneous use) and trunk restrained (maximal use) to measure their PANU score. The analysis showed that PANU scores for the post-stroke individuals ranged between 1,9% and 40,7% with a median of 11,7%, and these PANU scores were a reliable and reproducible measure of the functional reserve of the upper limb. The PANU score threshold for clinical significance was set as 6.5% (upper limit in healthy subjects). The PANU score seems pertinent as a complement to usual clinical assessments of upper limb function and impairment (Box and Block test, Fugl-Meyer).The second study explored the applicability of the Kinect system to measure PANU scores in 19 post stroke individuals in comparison to the standard Zebris-CMS20s method. The analysis showed that the PANU score measured by the Kinect was valid and reliable, and therefore should be used as a tool to classify patients in order to propose specific upper limb rehabilitation with arm-forced use by trunk restraint or feedback.Study three was a review of innovative technologies applied to sensorimotor rehabilitation after a stroke suggesting that PANU scores could be implemented in virtual reality rehabilitation and be used as a tool to determine the efficacy of the specialised treatment.In conclusion, this thesis showed that i) objectively quantifying the proximal arm non-use (PANU score) during a reaching task using a 3D motion capture system is feasible and reliable, and ii) PANU scores are accurately determined also using a more widely available and less expensive Kinect-based motion sensor with the future aim of PANU being integrated in a Kinect-based upper limb virtual reality rehabilitation.Après un accident vasculaire cérébral (AVC), l’utilisation de stratégies compensatoires dans les activités de la vie quotidienne peut freiner la récupération motrice du membre supérieur parétique. En effet, lors d’une tâche d’atteinte, les individus post-AVC présentant un déficit sévère limitant les mouvements épaule-coude, doivent utiliser la flexion du tronc afin de réaliser une tâche d’atteinte. Dans ce cas, il s’agit d’une compensation obligatoire-adaptative. D’autre part, certains individus post-AVC ayant suffisamment récupéré la motricité épaule-coude, continuent de solliciter une flexion du tronc, et cette compensation non-obligatoire du tronc est dite maladaptative car elle reflète la non-utilisation épaule–coude (proximal arm non-use - PANU) ayant pour effet d’entraver la récupération du bras parétique.Dans la première étude, 45 sujets post-AVC et 45 sujets contrôles sains appariés en âge ont effectué une tâche d’atteinte, le tronc libre (utilisation spontanée du bras) et le tronc auto-fixé (utilisation maximale du bras). L’analyse a montré que les scores PANU des sujets post-AVC étaient compris entre 1,9% et 40,7% avec une médiane à 11,7%. La mesure du score PANU est reproductible, valide et représente la réserve motrice épaule-coude. Le seuil significatif du PANU a été fixé à 6,5% (limite supérieure chez les sujets sains). Enfin, le score PANU est complémentaire aux tests usuels de déficience et de fonction du membre supérieur (Box and Block test, Fugl-Meyer)La deuxième étude a montré la possibilité d’utiliser un système Kinect dans l’obtention du score PANU. Des mesures ont été effectuées simultanément avec les deux systèmes (Kinect et Zebris-CMS20s) chez 19 sujets post-AVC. Cette étude a montré que le score PANU mesuré avec la Kinect pourrait être utilisé comme un outil de diagnostic qui permettrait de proposer aux sujets post-AVC une rééducation spécifique d’utilisation forcée du bras par tronc bloqué ou bien par feedback.La troisième étude est une revue de la littérature sur les technologies innovantes appliquées à la rééducation sensorimotrice post-AVC suggérant que le score PANU puisse être intégré dans un traitement rééducatif par réalité virtuelle.En conclusion, ces travaux démontrent que quantifier objectivement la non-utilisation épaule-coude (score PANU) lors d’une tâche d’atteinte est possible et reproductible. Les scores PANU peuvent être déterminés également par un système très accessible (Kinect) ce qui permettrait d’intégrer le score PANU dans un jeu de rééducation par réalité virtuelle

Kinect as a reliable assessment tool to automatize individualized rehabilitation

National audienceMarker less motion capture systems such as the Kinect are increasingly used in clinical rehabilitation. Here, we aim to identify which measures are reliably assessed with the Kinect during seated hand reaching. Participants performed seated reaching tasks in both spontaneous and maximal (by minimizing trunk movement) way with. 3-dimensional hand movement and joint motions of the upper extremity and trunk were simultaneously recorded with the Kinect (Microsoft, USA) and with the VICON (Oxford Metrics, UK), the latter being the reference system. For each kinematic outcome, the validity of the Kinect was assessed with ICC, linear regression and Bland & Altmann plots. Instantaneous Cartesian and angular measures with the Kinect are not precise enough to reliably assess Cartesian and angular kinematics over time. Results showed that Kinect could accurately and reliably assess postural and angular measurement during seated reaching. It consolidates the idea that well designed Kinect-based protocols could be a wonderful way to easily assess specific information, further available for automatized quantitative diagnostics and individualized rehabilitation

Antigravity Muscle Weakness May Induce Proximal Arm Nonuse

National audienceTo overcome paretic upper limb reduced ability post-stroke, many patients use trunk compensations sometimes leading to proximal arm nonuse, but the reason why is not fully clear. 22 healthy participants held a weight (85% of Maximal Voluntary Contraction) during seated reaching in two conditions: spontaneous arm use (SAU) and maximal arm use (MAU, in which they were asked to voluntarily maximize arm use and minimize trunk use). We found that proximal arm nonuse is a movement strategy that healthy people spontaneously choose to cope with a lower force-over-weight ratio of their antigravity muscle of the upper limb, Yet, this suggests that proximal arm nonuse post stroke is largely due to antigravity muscle weakness, which in turn suggests that antigravity muscle reinforcement may result in less proximal arm nonuse post stroke, especially for patients with high PANU scores

La perte de force n’est pas seule responsable des caractéristiques du mouvement du membre supérieur parétique post-AVC

National audienceTo compensate for the reduced capacity of the post-stroke paretic upper limb, the anterior and lateral deltoid muscles are often maximally activated in post-stroke reaching, suggesting that paretic movement characteristics are related to antigravity muscles weakness. Here we test this causal relationship in healthy participants: we hypothesise that a lower force-over-weight ratio of the arm would underlie the movement characteristics in stroke patients. In a seated manual reaching task, we compared the movement kinematics of 22 stroke patients to those of 24 healthy participants with a reduced force-over-weight ratio of the arm (the hand being weighted at 85% of maximal voluntary contraction). For all participants, the side with the reduced force-over-weight ratio showed 1) excessive spontaneous trunk use (anterior flexion and axial rotation), reversible in the maximal condition and 2) longer movement time in all conditions. For stroke patients, there is a greater spatial end-error, in the spontaneous and maximal conditions and for both limbs. Healthy participants with the loaded arm show a reorganisation of coordination, an increase in movement time and non-use of the shoulder and elbow similar to that of post-stroke paretic movements. However, only the stroke patients show an increase in the segmentation of the paretic arm movement and a decrease in the accuracy of both limbs (paretic and non-paretic). Our results indicate that the characteristics of paretic movements are due to a weakness of the antigravity muscles, but also to a post-stroke control strategy that seems to rely more on feedback than on anticipation.Pour surmonter la capacité réduite du membre supérieur parétique post-AVC, les muscles deltoïdes antérieur et latéral sont souvent activés au maximum lors de l’atteinte post-AVC, ce qui suggère que les caractéristiques du mouvement parétique sont liées au manque de force des muscles antigravitaires par rapport au poids du membre supérieur parétique. Ici, nous testons cette relation de cause à effet chez des participants en bonne santé : nous émettons l’hypothèse qu’un rapport force / poids du bras plus bas serait à l’origine de caractéristiques du mouvement des personnes après un AVC. Dans une tache d’atteinte manuelle en position assise, nous avons comparé la cinématique des mouvements de 22 patients parétiques à ceux de 24 participants sains ayant un rapport force / poids réduit (la main est lestée à 85% de la contraction volontaire maximale). Pour tous les participants, le côté au faible ratio force / poids présente 1) une utilisation spontanée du tronc excessive (flexion antérieure et rotation axiale), réversible en condition maximale et 2) un temps de mouvement supérieur dans toutes les conditions. Pour les patients parétiques, on observe une plus grande erreur spatiale à l’arrivée, en condition spontanée comme maximale et pour les deux membres. Les participants sains au bras chargé montrent une réorganisation de la coordination, une augmentation du temps de mouvement et une non-utilisation épaule-coude similaire à celle des mouvements parétiques post-AVC. Cependant, seuls les patients atteints d’un AVC ont une augmentation de la segmentation du mouvement du bras parétique et une diminution de la précision des deux membres (parétique et non parétique). Nos résultats indiquent que les caractéristiques du mouvement parétique sont dues à une faiblesse des muscles antigravitaires, mais également à une stratégie de contrôle post-AVC qui semble reposer en plus grande partie sur les retours d’informations au détriment de l’anticipation

Correction to: Dissociating motor learning from recovery in exoskeleton training post-stroke.

The original article [1] contained an error whereby the co-author, Karima Bakhti's name was displayed incorrectly

Low cost NFC sensor-RFID wristband used to determine Metabolic-Equivalent-of-Task

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Dissociating motor learning from recovery in exoskeleton training post-stroke.

BackgroundA large number of robotic or gravity-supporting devices have been developed for rehabilitation of upper extremity post-stroke. Because these devices continuously monitor performance data during training, they could potentially help to develop predictive models of the effects of motor training on recovery. However, during training with such devices, patients must become adept at using the new "tool" of the exoskeleton, including learning the new forces and visuomotor transformations associated with the device. We thus hypothesized that the changes in performance during extensive training with a passive, gravity-supporting, exoskeleton device (the Armeo Spring) will follow an initial fast phase, due to learning to use the device, and a slower phase that corresponds to reduction in overall arm impairment. Of interest was whether these fast and slow processes were related.MethodsTo test the two-process hypothesis, we used mixed-effect exponential models to identify putative fast and slow changes in smoothness of arm movements during 80 arm reaching tests performed during 20 days of exoskeleton training in 53 individuals with post-acute stroke.ResultsIn line with our hypothesis, we found that double exponential models better fit the changes in smoothness of arm movements than single exponential models. In contrast, single exponential models better fit the data for a group of young healthy control subjects. In addition, in the stroke group, we showed that smoothness correlated with a measure of impairment (the upper extremity Fugl Meyer score - UEFM) at the end, but not at the beginning, of training. Furthermore, the improvement in movement smoothness due to the slow component, but not to the fast component, strongly correlated with the improvement in the UEFM between the beginning and end of training. There was no correlation between the change of peaks due to the fast process and the changes due to the slow process. Finally, the improvement in smoothness due to the slow, but not the fast, component correlated with the number of days since stroke at the onset of training - i.e. participants who started exoskeleton training sooner after stroke improved their smoothness more.ConclusionsOur results therefore demonstrate that at least two processes are involved in in performance improvements measured during mechanized training post-stroke. The fast process is consistent with learning to use the exoskeleton, while the slow process independently reflects the reduction in upper extremity impairment

Dissociating motor learning from recovery in exoskeleton training post-stroke

Abstract Background A large number of robotic or gravity-supporting devices have been developed for rehabilitation of upper extremity post-stroke. Because these devices continuously monitor performance data during training, they could potentially help to develop predictive models of the effects of motor training on recovery. However, during training with such devices, patients must become adept at using the new “tool” of the exoskeleton, including learning the new forces and visuomotor transformations associated with the device. We thus hypothesized that the changes in performance during extensive training with a passive, gravity-supporting, exoskeleton device (the Armeo Spring) will follow an initial fast phase, due to learning to use the device, and a slower phase that corresponds to reduction in overall arm impairment. Of interest was whether these fast and slow processes were related. Methods To test the two-process hypothesis, we used mixed-effect exponential models to identify putative fast and slow changes in smoothness of arm movements during 80 arm reaching tests performed during 20 days of exoskeleton training in 53 individuals with post-acute stroke. Results In line with our hypothesis, we found that double exponential models better fit the changes in smoothness of arm movements than single exponential models. In contrast, single exponential models better fit the data for a group of young healthy control subjects. In addition, in the stroke group, we showed that smoothness correlated with a measure of impairment (the upper extremity Fugl Meyer score - UEFM) at the end, but not at the beginning, of training. Furthermore, the improvement in movement smoothness due to the slow component, but not to the fast component, strongly correlated with the improvement in the UEFM between the beginning and end of training. There was no correlation between the change of peaks due to the fast process and the changes due to the slow process. Finally, the improvement in smoothness due to the slow, but not the fast, component correlated with the number of days since stroke at the onset of training – i.e. participants who started exoskeleton training sooner after stroke improved their smoothness more. Conclusions Our results therefore demonstrate that at least two processes are involved in in performance improvements measured during mechanized training post-stroke. The fast process is consistent with learning to use the exoskeleton, while the slow process independently reflects the reduction in upper extremity impairment