ArticleJournal of neuroengineering and rehabilitation2023
Novel evaluation of upper-limb motor performance after stroke based on normal reaching movement model.
Article in Journal of neuroengineering and rehabilitation, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- A novel seamless magnetic-based actuating mechanism for end-effector-based robotic rehabilitation platforms.IEEE robotics and automation letters · 2025Article
- Research Status and Prospect of Finger Rehabilitation Machinery.Medical devices (Auckland, N.Z.) · 2024Review
- Automatic rehabilitation assessment method of upper limb motor function based on posture and distribution force.Frontiers in neuroscience · 2024Article
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundUpper-limb rehabilitation robots provide repetitive reaching movement training to post-stroke patients. Beyond a pre-determined set of movements, a robot-aided training protocol requires optimization to account for the individuals' unique motor characteristics. Therefore, an objective evaluation method should consider the pre-stroke motor performance of the affected arm to compare one's performance relative to normalcy. However, no study has attempted to evaluate performance based on an individual's normal performance. Herein, we present a novel method for evaluating upper limb motor performance after a stroke based on a normal reaching movement model.
methodsTo represent the normal reaching performance of individuals, we opted for three candidate models: (1) Fitts' law for the speed-accuracy relationship, (2) the Almanji model for the mouse-pointing task of cerebral palsy, and (3) our proposed model. We first obtained the kinematic data of healthy (n = 12) and post-stroke (n = 7) subjects with a robot to validate the model and evaluation method and conducted a pilot study with a group of post-stroke patients (n = 12) in a clinical setting. Using the models obtained from the reaching performance of the less-affected arm, we predicted the patients' normal reaching performance to set the standard for evaluating the affected arm.
resultsWe verified that the proposed normal reaching model identifies the reaching of all healthy (n = 12) and less-affected arm (n = 19; 16 of them showed an R
conclusionsThe proposed method can be used to evaluate an individual's reaching characteristics based on an individuals normal reaching model. It has the potential to provide individualized training by prioritizing a set of reaching movements.
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