Self-adaptive robot training of stroke survivors for continuous tracking movements
Identifieur interne : 003556 ( Main/Exploration ); précédent : 003555; suivant : 003557Self-adaptive robot training of stroke survivors for continuous tracking movements
Auteurs : Elena Vergaro [Italie] ; Maura Casadio [Italie] ; Valentina Squeri [Italie] ; Psiche Giannoni [Italie] ; Pietro Morasso [Italie] ; Vittorio Sanguineti [Italie]Source :
- Journal of NeuroEngineering and Rehabilitation [ 1743-0003 ] ; 2010.
English descriptors
- KwdEn :
- MESH :
- instrumentation : Robotics, Therapy, Computer-Assisted.
- methods : Robotics.
- rehabilitation : Stroke.
- Adult, Aged, Artificial Intelligence, Feasibility Studies, Female, Humans, Male, Middle Aged, Survivors, User-Computer Interface.
Abstract
Although robot therapy is progressively becoming an accepted method of treatment for stroke survivors, few studies have investigated how to adapt the robot/subject interaction forces in an automatic way. The paper is a feasibility study of a novel self-adaptive robot controller to be applied with continuous tracking movements.
The haptic robot Braccio di Ferro is used, in relation with a tracking task. The proposed control architecture is based on three main modules: 1) a force field generator that combines a non linear attractive field and a viscous field; 2) a performance evaluation module; 3) an adaptive controller. The first module operates in a continuous time fashion; the other two modules operate in an intermittent way and are triggered at the end of the current block of trials. The controller progressively decreases the gain of the force field, within a session, but operates in a non monotonic way between sessions: it remembers the minimum gain achieved in a session and propagates it to the next one, which starts with a block whose gain is greater than the previous one. The initial assistance gains are chosen according to a minimal assistance strategy. The scheme can also be applied with closed eyes in order to enhance the role of proprioception in learning and control.
The preliminary results with a small group of patients (10 chronic hemiplegic subjects) show that the scheme is robust and promotes a statistically significant improvement in performance indicators as well as a recalibration of the visual and proprioceptive channels. The results confirm that the minimally assistive, self-adaptive strategy is well tolerated by severely impaired subjects and is beneficial also for less severe patients.
The experiments provide detailed information about the stability and robustness of the adaptive controller of robot assistance that could be quite relevant for the design of future large scale controlled clinical trials. Moreover, the study suggests that including continuous movement in the repertoire of training is acceptable also by rather severely impaired subjects and confirms the stabilizing effect of alternating vision/no vision trials already found in previous studies.
Url:
DOI: 10.1186/1743-0003-7-13
PubMed: 20230610
PubMed Central: 2850909
Affiliations:
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Le document en format XML
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<term>Robotics (instrumentation)</term>
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<front><div type="abstract" xml:lang="en"><sec><title>Background</title>
<p>Although robot therapy is progressively becoming an accepted method of treatment for stroke survivors, few studies have investigated how to adapt the robot/subject interaction forces in an automatic way. The paper is a feasibility study of a novel self-adaptive robot controller to be applied with continuous tracking movements.</p>
</sec>
<sec><title>Methods</title>
<p>The haptic robot Braccio di Ferro is used, in relation with a tracking task. The proposed control architecture is based on three main modules: 1) a force field generator that combines a non linear attractive field and a viscous field; 2) a performance evaluation module; 3) an adaptive controller. The first module operates in a continuous time fashion; the other two modules operate in an intermittent way and are triggered at the end of the current block of trials. The controller progressively decreases the gain of the force field, within a session, but operates in a non monotonic way between sessions: it remembers the minimum gain achieved in a session and propagates it to the next one, which starts with a block whose gain is greater than the previous one. The initial assistance gains are chosen according to a minimal assistance strategy. The scheme can also be applied with closed eyes in order to enhance the role of proprioception in learning and control.</p>
</sec>
<sec><title>Results</title>
<p>The preliminary results with a small group of patients (10 chronic hemiplegic subjects) show that the scheme is robust and promotes a statistically significant improvement in performance indicators as well as a recalibration of the visual and proprioceptive channels. The results confirm that the minimally assistive, self-adaptive strategy is well tolerated by severely impaired subjects and is beneficial also for less severe patients.</p>
</sec>
<sec><title>Conclusions</title>
<p>The experiments provide detailed information about the stability and robustness of the adaptive controller of robot assistance that could be quite relevant for the design of future large scale controlled clinical trials. Moreover, the study suggests that including continuous movement in the repertoire of training is acceptable also by rather severely impaired subjects and confirms the stabilizing effect of alternating vision/no vision trials already found in previous studies.</p>
</sec>
</div>
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<name sortKey="Giannoni, Psiche" sort="Giannoni, Psiche" uniqKey="Giannoni P" first="Psiche" last="Giannoni">Psiche Giannoni</name>
<name sortKey="Morasso, Pietro" sort="Morasso, Pietro" uniqKey="Morasso P" first="Pietro" last="Morasso">Pietro Morasso</name>
<name sortKey="Morasso, Pietro" sort="Morasso, Pietro" uniqKey="Morasso P" first="Pietro" last="Morasso">Pietro Morasso</name>
<name sortKey="Morasso, Pietro" sort="Morasso, Pietro" uniqKey="Morasso P" first="Pietro" last="Morasso">Pietro Morasso</name>
<name sortKey="Sanguineti, Vittorio" sort="Sanguineti, Vittorio" uniqKey="Sanguineti V" first="Vittorio" last="Sanguineti">Vittorio Sanguineti</name>
<name sortKey="Sanguineti, Vittorio" sort="Sanguineti, Vittorio" uniqKey="Sanguineti V" first="Vittorio" last="Sanguineti">Vittorio Sanguineti</name>
<name sortKey="Sanguineti, Vittorio" sort="Sanguineti, Vittorio" uniqKey="Sanguineti V" first="Vittorio" last="Sanguineti">Vittorio Sanguineti</name>
<name sortKey="Squeri, Valentina" sort="Squeri, Valentina" uniqKey="Squeri V" first="Valentina" last="Squeri">Valentina Squeri</name>
</country>
</tree>
</affiliations>
</record>
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