Learning, Retention, and Slacking: A Model of the Dynamics of Recovery in Robot Therapy

被引：47

作者：

Casadio, Maura ^{[1
]}

Sanguineti, Vittorio ^{[2
,3
]}

机构：

[1] Rehabil Inst Chicago, Chicago, IL 60611 USA

[2] Univ Genoa, Dept Informat Syst & Telemat, I-16145 Genoa, Italy

[3] Ist Italiano Tecnol, Robot Brain & Cognit Sci Dept, I-16163 Genoa, Italy

来源：

IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING | 2012年 / 20卷 / 03期

关键词：

Assistance; linear dynamical systems; robot therapy; state space model; stroke; MOVEMENT REPRESENTATIONS; MOTOR ADAPTATION; INTERNAL-MODELS; STROKE; ARM; REORGANIZATION; EXPERIENCE; IMPACT; CORTEX; ERROR;

D O I：

10.1109/TNSRE.2012.2190827

中图分类号：

R318 [生物医学工程];

学科分类号：

0831 ;

摘要：

Quantitative descriptions of the process of recovery of motor functions in impaired subjects during robot-assisted exercise might help to understand how to use these devices to make recovery faster and more effective. Linear dynamical models have been used to describe the dynamics of sensorimotor adaptation. Here, we extend this formalism to characterize the neuromotor recovery process. We focus on a robot therapy experiment that involved chronic stroke survivors, based on a robot-assisted arm extension task. The results suggest that modeling the recovery process with dynamical models is feasible, and could allow predicting the long-term outcome of a robot-assisted rehabilitation treatment.

引用

收藏

页码：286 / 296

页数：11

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