Summary
Injuries and diseases affecting the nervous system pose significant challenges for patients and impose substantial socioeconomic burdens. Digital technologies, such as artificial intelligence (AI) and robotics, hold great promise in revolutionizing recovery processes owing to their robustness, adaptability, and ability to assimilate diverse patient information. Despite this potential, current evidence suggests that these technologies have not fully met expectations. We propose two primary reasons for this discrepancy: (1) the absence of robot-based therapy in established clinical guidelines that therapists follow systematically, and (2) the suboptimal implementation of crucial features in therapeutic interventions—specifically, tasks should be tailored, intensive, optimally challenging, allow movement variability, and foster high patient engagement. In essence, interventions should be personalized to each patient's unique condition and requirements.
TAILOR aims to bridge this gap by offering invaluable insights and knowledge to the next generation of researchers. This knowledge will empower them to comprehend the core principles of robotic technology, leading to the development of highly personalized therapies that deliver long-term functional effects and significantly enhance treatment outcomes. This shift is pivotal for the widespread and definitive integration of robotics into rehabilitation practices.
TAILOR represents a unique system for collecting comprehensive data on the therapy process, and using AI methods to extract knowledge and to design controllers for automated therapy. We expect that these AI-driven controllers will enable rehabilitation to be tailored to a neuromotor status of a specific patient to a degree not possible before, automatically adjusting to all the aforementioned factors. The result will be a more efficient, comfortable, and optimally challenging therapy, which will allow a boost to the functional recovery of these patients.
TAILOR aims to bridge this gap by offering invaluable insights and knowledge to the next generation of researchers. This knowledge will empower them to comprehend the core principles of robotic technology, leading to the development of highly personalized therapies that deliver long-term functional effects and significantly enhance treatment outcomes. This shift is pivotal for the widespread and definitive integration of robotics into rehabilitation practices.
TAILOR represents a unique system for collecting comprehensive data on the therapy process, and using AI methods to extract knowledge and to design controllers for automated therapy. We expect that these AI-driven controllers will enable rehabilitation to be tailored to a neuromotor status of a specific patient to a degree not possible before, automatically adjusting to all the aforementioned factors. The result will be a more efficient, comfortable, and optimally challenging therapy, which will allow a boost to the functional recovery of these patients.
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More information & hyperlinks
| Web resources: | https://cordis.europa.eu/project/id/101168724 |
| Start date: | 01-10-2024 |
| End date: | 30-09-2028 |
| Total budget - Public funding: | - 2 096 033,00 Euro |
Cordis data
Original description
Injuries and diseases affecting the nervous system pose significant challenges for patients and impose substantial socioeconomic burdens. Digital technologies, such as artificial intelligence (AI) and robotics, hold great promise in revolutionizing recovery processes owing to their robustness, adaptability, and ability to assimilate diverse patient information. Despite this potential, current evidence suggests that these technologies have not fully met expectations. We propose two primary reasons for this discrepancy: (1) the absence of robot-based therapy in established clinical guidelines that therapists follow systematically, and (2) the suboptimal implementation of crucial features in therapeutic interventions—specifically, tasks should be tailored, intensive, optimally challenging, allow movement variability, and foster high patient engagement. In essence, interventions should be personalized to each patient's unique condition and requirements.TAILOR aims to bridge this gap by offering invaluable insights and knowledge to the next generation of researchers. This knowledge will empower them to comprehend the core principles of robotic technology, leading to the development of highly personalized therapies that deliver long-term functional effects and significantly enhance treatment outcomes. This shift is pivotal for the widespread and definitive integration of robotics into rehabilitation practices.
TAILOR represents a unique system for collecting comprehensive data on the therapy process, and using AI methods to extract knowledge and to design controllers for automated therapy. We expect that these AI-driven controllers will enable rehabilitation to be tailored to a neuromotor status of a specific patient to a degree not possible before, automatically adjusting to all the aforementioned factors. The result will be a more efficient, comfortable, and optimally challenging therapy, which will allow a boost to the functional recovery of these patients.
Status
SIGNEDCall topic
HORIZON-MSCA-2023-DN-01-01Update Date
18-12-2024
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Organisations
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| AGENCIA ESTATAL CONSEJO SUPERIOR DE INVESTIGACIONES CIENTIFICAS (Coördinator)
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| ABLE HUMAN MOTION S.L.
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| EIDGENOESSISCHE TECHNISCHE HOCHSCHULE ZUERICH
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| FONDAZIONE SANTA LUCIA
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| HOCOMA AG
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| ROESSINGH RESEARCH AND DEVELOPMENT BV
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| SILVIA BEATRIZ SL
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| UNIVERSIDAD POLITECNICA DE MADRID
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| UNIVERSITA CAMPUS BIO MEDICO DI ROMA
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| UNIVERSITAT POLITECNICA DE CATALUNYA
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| UNIVERSITEIT TWENTE