Recovery of Motor Skills With the Use of Artificial Intelligence and Computer Vision
NCT ID: NCT06183970
Last Updated: 2023-12-28
Study Results
The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.
Basic Information
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UNKNOWN
NA
90 participants
INTERVENTIONAL
2024-02-29
2025-02-28
Brief Summary
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Detailed Description
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Amid rapid advancements in deep machine learning, particularly in image and pattern recognition, medical image analysis has gained prominence within automated diagnostic systems, particularly in radiation diagnostics. With the burgeoning field's rapid growth, curating medical datasets for AI-based diagnostic system training and validation is crucial.
AI's success in radiation diagnostics and its recognition as promising within scientific circles pave the way for video analysis and machine learning's integration into medical rehabilitation practice. Collaborating, researchers at the Federal Medical Research Center of the FMBA of Russia and MTUCI devised a plan to develop specialized algorithms based on video movement analysis and machine learning for stroke patients undergoing medical rehabilitation.
These algorithms monitor patients' movements and promptly notify them of deviations, amplitude reductions, or compensatory patterns, aiding them in correcting their movements. All session data is archived electronically, accessible to medical professionals responsible for individualized lesson plans. This enables assessment of patient progress and necessary adjustments to the home rehabilitation program.
Incorporating AI-driven video analysis and machine learning into medical rehabilitation holds great potential for enhancing patient outcomes and personalizing treatment strategies.
Conditions
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Keywords
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Study Design
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RANDOMIZED
PARALLEL
TREATMENT
DOUBLE
Study Groups
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AssistI patients
Patients will receive rehabilitation training using the AsistI software package in conjunction with standard upper limb rehabilitation interventions.
AssistI patients
The AsistI software package rehabilitation involves tailored upper limb exercises under an individual program. The regimen consists of 10-12 sessions, each lasting 30 minutes. Patients execute 10 exercises sequentially with their unaffected and affected limbs, involving tasks like touching mouth, forehead, and trunk parts with hand's brush, and amplitude movements in upper limb joints. AsistI assesses exercise accuracy, prevents unfavorable patterns, and logs target achievement, considering speed, accuracy, and repetitions.
Habilect patients
Patients will receive rehabilitation training using the Habilect software and hardware complex, in addition to standard rehabilitation interventions for the upper limb.
Habilect patients
The Habilect rehab program involves 10-12 sessions using software and hardware. Patients perform upper limb exercises for 30 minutes individually, focusing on specific movements. They repeat 10 exercises, first with the healthy limb, then the affected one. Tasks include touching mouth, forehead, and trunk, along with joint movements like shoulder flexion. Habilect assesses exercise accuracy, preventing wrong moves, and tracks progress, considering speed, accuracy, repetitions.
Conventional therapy patients
Patients will undergo standard upper limb rehabilitation interventions without the utilization of additional methods.
No interventions assigned to this group
Interventions
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AssistI patients
The AsistI software package rehabilitation involves tailored upper limb exercises under an individual program. The regimen consists of 10-12 sessions, each lasting 30 minutes. Patients execute 10 exercises sequentially with their unaffected and affected limbs, involving tasks like touching mouth, forehead, and trunk parts with hand's brush, and amplitude movements in upper limb joints. AsistI assesses exercise accuracy, prevents unfavorable patterns, and logs target achievement, considering speed, accuracy, and repetitions.
Habilect patients
The Habilect rehab program involves 10-12 sessions using software and hardware. Patients perform upper limb exercises for 30 minutes individually, focusing on specific movements. They repeat 10 exercises, first with the healthy limb, then the affected one. Tasks include touching mouth, forehead, and trunk, along with joint movements like shoulder flexion. Habilect assesses exercise accuracy, preventing wrong moves, and tracks progress, considering speed, accuracy, repetitions.
Eligibility Criteria
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Inclusion Criteria
* Rankin scale: 3
* Within 6 months post stroke.
* Upper limb hemiparesis with strength ≤3 points proximally.
* Muscle tone rise (≤3 points) on Ashford scale.
* Complex sensitivity preserved per neuro examination
Exclusion Criteria
* 6 months or more after undergoing stroke.
* Structural changes in the joints of the upper extremities that limit joint mobility (contractures, ankylosis, metal structures that limit mobility).
* Severe pain syndrome in the paretic upper limb at rest or when moving, preventing exercise (7 points or more on the scale).
* Gross cognitive disorders, psychoemotional arousal, signs of hysteria, pseudobulbar syndrome (violent laughter, crying), aphasic disorders that prevent understanding of the task.
* Visual disturbances that prevent the perception of information (neglect, hemianopia, myopia, diplopia).
* Thrombosis of the veins in the upper and lower extremities without signs of recanalization, or arterial thrombosis.
* Parkinsonism and other types of tremor.
18 Years
80 Years
ALL
Yes
Sponsors
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Moscow Technical University of Communications and Informatics
UNKNOWN
Federal Center of Cerebrovascular Pathology and Stroke, Russian Federation Ministry of Health
OTHER
Responsible Party
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Principal Investigators
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Michael Gorodnichev
Role: STUDY_CHAIR
Moscow Technical University of Communication and Informatics (MTUCI)
Galina Ivanova, Prof
Role: STUDY_CHAIR
Federal Center of Cerebrovascular Pathology and Stroke, Russian Federation Ministry of Health
Central Contacts
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Other Identifiers
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AssistI01
Identifier Type: -
Identifier Source: org_study_id