An observational study in Stroke, sponsored by King's College London. Recruiting at 1 site in United Kingdom. Open to participants aged 18 Years and older, including healthy volunteers. Per ClinicalTrials.gov, last updated 2025-08-11.
Sponsored by King's College London · Observational
People who have a stroke often find it hard to do the things they did before. This can be caused by problems with arm movement. One in five people do not get any arm movement back after a stroke.
Arm movements can be measured accurately in a laboratory, but it is very expensive and not easy to do in hospital. That means it is hard to tell if the arm is recovering to move like it did before the stroke or adapting to perform tasks in other ways.
To tell if a treatment is working, the investigators are making a phone app to record arm movement, using the camera. The recordings will be turned into data showing movement difficulties and sent to hospital records for clinicians. Clinicians will see if movement changes, to help choose the best treatment.
The investigators are looking for twelve stroke survivors to help test this app.
This will show us if our app can measure arm movement as well as laboratory tests. If they do, the investigators will know the app is accurate.
In future this technology can improve recovery by correcting stroke survivors when they perform home exercises.
Upper limb recovery after stroke remains poor and 20% of stroke survivors do not recover arm movement. To improve outcome and advance insights to recovery mechanisms, an international collaboration has proposed a standardised set of outcome measures, including movement kinematics. Kinematics are moresensitive to change than clinical measures and can differentiate whether recovery is achieved by compensating to impairment or true recovery. However, kinematic assessments are not performed in clinical practice as 3-D motion capture requires expensive equipment and expertise for set-up and analysis.
The investigators therefore aim to develop a low-cost tool to measure kinematics. Open-source Artificial Intelligence models can detect positions and orientations on video and are called pose estimation models. The objectives will be to deploy and test these models in stroke survivors. The investigators will invite 12 stroke survivors with mild to moderate upper limb impairment and compare the accuracy of the models to gold-standard kinematic analysis when performing a variety of upper limb tasks. The investigators will optimise the models in case of any discrepancies. The investigators will develop a front-end smartphone app to instruct, record and provide feedback of arm movement performance to clinicians and stroke survivors. The investigators will develop the software back-end performing analysis of recorded movements and integrating these findings into electronic healthcare records for longitudinal performance tracking.
This accessible technology will provide clinicians kinematic analyses. Kinematics can guide treatment modifications and progression to improve upper limb movement.
7,286 studies on the registry are indexed under Stroke; 2,007 are open to participants now.
This study's planned enrollment of 30 is below the median of 160 across 1,692 observational studies indexed under Stroke.
Browse Stroke studies →King's College London is the lead sponsor of 506 studies on the registry; 125 are open to participants now.
Counted across the registry records on this site, refreshed daily.
18 stroke survivors 8 Healthy age-matched controls
Stroke survivors
Inclusion Criteria:
Exclusion Criteria:
Behavioral: Biomechanical analysis of arm movement · Behavioral: Pose estimation of arm movement
Behavioral: Biomechanical analysis of arm movement · Behavioral: Pose estimation of arm movement
Marker based kinematic analysis
Marker free kinematic analysis
Pose estimation model accuracy
Agreement between pose estimation and biomechanics measurements
Time frame: During intervention
Fugl-Meyer Upper limb Assessment
Time frame: baseline, before intervention
Motricity Index
Strength
Time frame: baseline, before intervention
Cancellation OCS
neglect
Time frame: baseline, before intervention
Box and block
dexterity
Time frame: during the intervention
ARAT-2
Time frame: during the intervention
Plan to share: No — The main data includes videos of participants which are impossible to anonymise. Therefore it is not appropriate for sharing.
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