Dysmetria, Hemiparesis, Spasticity as Sequela of Stroke, Stroke
Conditions
Keywords
Rehabilitation, Artificial intelligence, Computer vision, Motion capture, Assistive technology
Brief summary
To investigate the impact of algorithms utilizing artificial intelligence technology and computer vision on the recovery of motor functions within the context of rehabilitation practice for patients who have experienced a cerebral stroke.
Detailed description
Progress in artificial intelligence (AI) technologies and their practical application across various fields, notably in medicine, showcases their potential in solutions such as automated diagnostic systems, unstructured medical record recognition, natural language understanding, event analysis and prediction, information classification, automatic patient support via chatbots, and movement analysis through video. Currently, diverse AI-based software systems are being developed, designed to solve intellectual problems akin to human thinking. AI's widespread applications encompass prediction, evaluation of digital information (including unstructured data), and pattern recognition (data mining). 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.
Interventions
DEVICEAssistI 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.
DEVICEHabilect 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.
Sponsors
Moscow Technical University of Communications and Informatics
Federal Center of Cerebrovascular Pathology and Stroke, Russian Federation Ministry of Health
Study design
Allocation
RANDOMIZED
Intervention model
PARALLEL
Primary purpose
TREATMENT
Masking
DOUBLE (Subject, Outcomes Assessor)
Eligibility
Sex/Gender
ALL
Age
18 Years to 80 Years
Healthy volunteers
Yes
Inclusion criteria
Recent hemispheric stroke (ischemic or hemorrhagic): * 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
* Rankin scale of 4 points and higher. * 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.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Fugl-Meyer Assessment Scale for upper extremity assessment (FMA-UE) | Change from baseline at 3 weeks | In this study, we wiil use 36 items of the upper arm (proximal musculature, FMA-UA), 24 items of wrist and hand (distal musculature, FMA-W/H), 6 items of aspects of coordination, 12 items of aspects of sensation, 24 items of aspects of passive joint movement, 24 items of joint pain. So the maximum total score on this FMA-UE scale was 126 points. |
| Muscle strength was assessed using the MRC (Medical Research Council Weakness Scale) | Change from baseline at 3 weeks | MRC is a commonly used scale for assessing muscle strength from Grade 5 (normal) to Grade 0 (no visible contraction). Paresis is defined as light at compliance with strength 4 points, moderate - 3 points, pronounced - 2 points, rough - 1 point and with - 0 points. |
| The Action Research Arm Test (ARAT) | Change from baseline at 3 weeks | Is a 19 item observational measure used by physical therapists and other health care professionals to assess upper extremity performance (coordination, dexterity and functioning) in stroke recovery, brain injury and multiple sclerosis populations. Scores on the ARAT may range from 0-57 points, with a maximum score of 57 points indicating better performance. MCID has been suggested as 5.7 points |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| The correctness of the exercises | Change from baseline at 3 weeks | Exercise correctness: Number of compensatory actions like shoulder elevation or torso bend. |
| The speed of movement of the upper limb | Change from baseline at 3 weeks | Upper limb movement speed: Time to reach the target (sec). |
| The number of exercises not completed | Change from baseline at 3 weeks | Incorrect repetition count: Number of attempts with compensatory actions, e.g., shoulder lift or torso bend. |
| The number of exercises completed | Change from baseline at 3 weeks | Correct repetition count: Number of attempts without compensation, e.g., shoulder or torso movements. |
| Accuracy of performed movements | Change from baseline at 3 weeks | Movement accuracy: Precision in touching guided points (angles). |
| Total number of repetitions | Change from baseline at 3 weeks | Repetition count: Number of motor attempts for the goal. |
Contacts
Primary ContactDanila Lobunko
Backup ContactBogdan Ragulin
Outcome results
None listed