Upper Limb Obstacle Avoidance Behavior in Individuals With Stroke

Baniña, Melanie C.; Mullick, Aditi A.; McFadyen, Bradford J.; Levin, Mindy F.

doi:10.1177/1545968316662527

Cited by 28 publications

(31 citation statements)

References 46 publications

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“…3, Point 9) for each participant for each of the 18 RTG movements. The wrist marker was chosen as it is common to use this marker for kinematic data analysis [34,35].…”

Section: Energy Ratio By Segments -The Sum Of Squaresmentioning

confidence: 99%

A Machine-Learning Model for Automatic Detection of Movement Compensations in Stroke Patients

Kashi

Polak

Lerner

et al. 2021

IEEE Trans. Emerg. Topics Comput.

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During the process of rehabilitation after stroke, it is important that patients know how well they perform their exercise, so they can improve their performance in future repetitions. Standard clinical rating conducted by human observation is the prevailing way today to monitor motor recovery of the patient. Therefore, patients cannot know whether they are performing a movement properly while exercising by themselves. Adhering to the exercise regime makes the rehabilitation process more effective and efficient, and thus a system that can give the patients feedback on their performance is of great value. Here, we build a machine-learning-based automated model that give patients accurate information on the compensatory (undesirable) movements that they make. To construct the model, we recorded movements from 30 stroke patients, who each performed 18 movements, used to identify the presence of six types of compensatory movements in stroke patients' movement trajectories. We used the random-forest algorithm for training this multi-label classification model. We achieved 85% average precision across the six movement compensations. This is the first study to automatically identify movement compensations based on stroke patients' data. This model can be adapted for use in in-clinic and at-home exercise programs for patients after stroke.

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“…3, Point 9) for each participant for each of the 18 RTG movements. The wrist marker was chosen as it is common to use this marker for kinematic data analysis [34,35].…”

Section: Energy Ratio By Segments -The Sum Of Squaresmentioning

confidence: 99%

A Machine-Learning Model for Automatic Detection of Movement Compensations in Stroke Patients

Kashi

Polak

Lerner

et al. 2021

IEEE Trans. Emerg. Topics Comput.

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“…Anticipating the potential for fatigue from participation in repeated sessions and the large number of reaching trials, we accounted for a 33% dropout rate. Individuals with stroke were included if they (1) had sustained a stroke ≥6 months previously, (2) were aged 40 to 80 years, (3) scored 3-7/7 on the arm subscale of the Chedoke McMaster Stroke Assessment, 25 and (4) had no stroke-related dementia (>22/30 on Montreal Cognitive Assessment). 26 Exclusion criteria were the following: (1) cerebellar lesions influencing coordination; (2) other neurological, orthopedic, or muscular disorders interfering with reaching ability; and (3) dizziness (Dizziness Handicap Inventory ≥30) 27 to avoid confounding influences of vestibular deficits on movement performance.…”

Section: Participantsmentioning

confidence: 99%

“…1 An important underlying deficit that may impact effective UL use is an impaired ability to adapt movement to unexpected changes in the environment. For example, Baniña et al 2 showed that, compared with healthy controls, individuals with mild poststroke UL impairments used less elbow extension and more trunk movements (ie, flexion) when reaching rapidly toward a target within arm's reach while avoiding unexpected obstacles in the hand path. Although these individuals were considered to be well recovered based on standard clinical assessment, higher order motor function (eg, obstacle avoidance involving rapid adaptation of arm movement) was impaired and resulted in subtle motor deficits (ie, critical timing deficits) that were related to decreased self-reported activities of daily living such as opening a drawer or a car door.…”

Section: Introductionmentioning

confidence: 99%

Motor-Equivalent Intersegmental Coordination Is Impaired in Chronic Stroke

Subramanian

Baniña

Sambasivan

et al. 2020

Neurorehabil Neural Repair

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Background. Kinematic abundance permits using different movement patterns for task completion. Individuals poststroke may take advantage of abundance by using compensatory trunk displacement to overcome upper limb (UL) movement deficits. However, movement adaptation in tasks requiring specific intersegment coordination may remain limited. Objective. We tested movement adaptation in both arms of individuals with chronic stroke (n = 16) and nondominant arms of controls (n = 12) using 2 no-vision reaching tasks involving trunk movement (40 trials/arm). Methods. In the “stationary hand task” (SHT), subjects maintained the hand motionless over a target while leaning the trunk forward. In the “reaching hand task” (RHT), subjects reached to the target while leaning forward. For both tasks, trunk movement was unexpectedly blocked in 40% of trials to assess the influence of trunk movement on adaptive arm positioning or reaching. UL sensorimotor impairment, activity, and sitting balance were assessed in the stroke group. The primary outcome measure for SHT was gain ( g), defined as the extent to which trunk displacement contributing to hand motion was offset by appropriate changes in UL movements ( g = 1: complete compensation) and endpoint deviation for RHT. Results. Individuals poststroke had lower gains and greater endpoint deviation using the more-affected compared with less-affected UL and controls. Those with less sensorimotor impairment, greater activity levels, and better sitting balance had higher gains and smaller endpoint deviations. Lower gains were associated with diminished UL adaptability. Conclusions. Tests of condition-specific adaptability of interjoint coordination may be used to measure UL adaptability and changes in adaptability with treatment.

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“…However, within a session, the Score stabilized quickly after 1 trial. It was shown to correlate with wrist function [40] and reflects movement efficacy [5]. Consequently, these two parameters should be used to assess patient performance/impairment and motor recovery at a given time or over time.…”

Section: Learning Effectmentioning

confidence: 99%

Kinematic parameters obtained with the ArmeoSpring for upper-limb assessment after stroke: a reliability and learning effect study for guiding parameter use

Brihmat

Loubinoux

Castel-Lacanal

et al. 2020

J NeuroEngineering Rehabil

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Background After stroke, kinematic measures obtained with non-robotic and robotic devices are highly recommended to precisely quantify the sensorimotor impairments of the upper-extremity and select the most relevant therapeutic strategies. Although the ArmeoSpring exoskeleton has demonstrated its effectiveness in stroke motor rehabilitation, its interest as an assessment tool has not been sufficiently documented. The aim of this study was to investigate the psychometric properties of selected kinematic parameters obtained with the ArmeoSpring in post-stroke patients. Methods This study involved 30 post-stroke patients (mean age = 54.5 ± 16.4 years; time post-stroke = 14.7 ± 26.7 weeks; Upper-Extremity Fugl-Meyer Score (UE-FMS) = 40.7 ± 14.5/66) who participated in 3 assessment sessions, each consisting of 10 repetitions of the ‘horizontal catch’ exercise. Five kinematic parameters (task and movement time, hand path ratio, peak velocity, number of peak velocity) and a global Score were computed from raw ArmeoSpring’ data. Learning effect and retention were analyzed using a 2-way repeated-measures ANOVA, and reliability was investigated using the intra-class correlation coefficient (ICC) and minimal detectable change (MDC). Results We observed significant inter- and intra-session learning effects for most parameters except peak velocity. The measures performed in sessions 2 and 3 were significantly different from those of session 1. No additional significant difference was observed after the first 6 trials of each session and successful retention was also highlighted for all the parameters. Relative reliability was moderate to excellent for all the parameters, and MDC values expressed in percentage ranged from 42.6 to 102.8%. Conclusions After a familiarization session, the ArmeoSpring can be used to reliably and sensitively assess motor impairment and intervention effects on motor learning processes after a stroke. Trial registration The study was approved by the local hospital ethics committee in September 2016 and was registered under number 05-0916.

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Upper Limb Obstacle Avoidance Behavior in Individuals With Stroke

Abstract: Arm movement deficits can be identified when complex tasks are evaluated. Deficits in higher-order motor function such as obstacle avoidance behavior may decrease actual arm use in individuals with mild-to-moderate hemiparesis and should be evaluated in routine clinical practice.

Cited by 28 publications

References 46 publications

A Machine-Learning Model for Automatic Detection of Movement Compensations in Stroke Patients

A Machine-Learning Model for Automatic Detection of Movement Compensations in Stroke Patients

Motor-Equivalent Intersegmental Coordination Is Impaired in Chronic Stroke

Kinematic parameters obtained with the ArmeoSpring for upper-limb assessment after stroke: a reliability and learning effect study for guiding parameter use

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