THERAPIST: Towards an Autonomous Socially Interactive Robot for Motor and Neurorehabilitation Therapies for Children
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Résumé
BACKGROUND: Neurorehabilitation therapies exploiting the use-dependent plasticity of our neuromuscular system are devised to help patients who suffer from injuries or diseases of this system. These therapies take advantage of the fact that the motor activity alters the properties of our neurons and muscles, including the pattern of their connectivity, and thus their functionality. Hence, a sensor-motor treatment where patients makes certain movements will help them (re)learn how to move the affected body parts. But these traditional rehabilitation processes are usually repetitive and lengthy, reducing motivation and adherence to the treatment, and thus limiting the benefits for the patients. OBJECTIVE: Our goal was to create innovative neurorehabilitation therapies based on THERAPIST, a socially assistive robot. THERAPIST is an autonomous robot that is able to find and execute plans and adapt them to new situations in real-time. The software architecture of THERAPIST monitors and determines the course of action, learns from previous experiences, and interacts with people using verbal and non-verbal channels. THERAPIST can increase the adherence of the patient to the sessions using serious games. Data are recorded and can be used to tailor patient sessions. METHODS: We hypothesized that pediatric patients would engage better in a therapeutic non-physical interaction with a robot, facilitating the design of new therapies to improve patient motivation. We propose RoboCog, a novel cognitive architecture. This architecture will enhance the effectiveness and time-of-response of complex multi-degree-of-freedom robots designed to collaborate with humans, combining two core elements: a deep and hybrid representation of the current state, own, and observed; and a set of task-dependent planners, working at different levels of abstraction but connected to this central representation through a common interface. Using RoboCog, THERAPIST engages the human partner in an active interactive process. But RoboCog also endows the robot with abilities for high-level planning, monitoring, and learning. Thus, THERAPIST engages the patient through different games or activities, and adapts the session to each individual. RESULTS: RoboCog successfully integrates a deliberative planner with a set of modules working at situational or sensorimotor levels. This architecture also allows THERAPIST to deliver responses at a human rate. The synchronization of the multiple interaction modalities results from a unique scene representation or model. THERAPIST is now a socially interactive robot that, instead of reproducing the phrases or gestures that the developers decide, maintains a dialogue and autonomously generate gestures or expressions. THERAPIST is able to play simple games with human partners, which requires humans to perform certain movements, and also to capture the human motion, for later analysis by clinic specialists. CONCLUSIONS: The initial hypothesis was validated by our experimental studies showing that interaction with the robot results in highly attentive and collaborative attitudes in pediatric patients. We also verified that RoboCog allows the robot to interact with patients at human rates. However, there remain many issues to overcome. The development of novel hands-off rehabilitation therapies will require the intersection of multiple challenging directions of research that we are currently exploring.
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Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle