Prediction of environmental variables using machine learning during community ambulation in chronic stroke: A pilot study

Not Applicable

Conditions: Diseases of the circulatory system

Registration Number: KCT0007200

Lead Sponsor: ational Rehabilitation Center

Brief Summary: Not available

Detailed Description: Not available

Recruitment & Eligibility

Status: ot yet recruiting

Sex: All

Target Recruitment: 60

Inclusion Criteria

· Chronic stroke disorder with first onset more than 3 months
· Able to walk 10m independently using a walking aid (Functional Ambulation Classification (FAC) score of 3 or higher)
· Those with gait disturbance due to symptoms such as manual muscle test score of ankle dorsiflexor=3 and foot drop
· Appropriate language and cognitive ability to understand and implement the training content (Mini-Mental Status Examination = 24)
· Those who agree to the research contents and wish to voluntarily participate in the research

Exclusion Criteria

· Those who have contractures or pain in the lower extremities that may affect walking
· Those with a history of musculoskeletal system in the ankle (fracture, amputation, sprain, etc.)

Study & Design

Study Type: Observational Study

Study Design: Not specified

Primary Outcome Measures

Name	Time	Method
Machine learning analysis variables: Angular velocity, acceleration, Mucsle activation (Tibialis anterior, Medial gastrocnemius, Rectus femoris, Biceps femoris), Plantar force, Galvanic Skin response

Secondary Outcome Measures

Name	Time	Method
Gait analysis variables–Spatiotemporal variables (walking speed, step length, step width, stance time, swing time, symmetry ratio), Joint kinematics (joint range of motion, joint velocity), Muscle activity (tibalis anterior, medial gastrocnemius, quadriceps, hamstrings);Clinical evaluation parameters: Ankle Range of motion (ROM), Manual muscle test (MMT), Plantar cutaneous sensation, Ankle Proprioception, Ankle stiffness, Ankle anthropometry, Berg Balance Scale (BBS), Fugl-Meyer Assessment Lower Extremity (FM-L) ), Fall efficancy scale (FES), Stroke Impact Scale (SIS), Beck Depression Inventory, Brain activity, Metabolic cost