Monitoring arm recovery after stroke with a smartphone camera
Official title: Developing an Accessible, Cost-Effective Motion Analysis Tool for Arm Movement After Stroke
Run by King's College London · NCT07016295 · Data updated 2026-11-12
This project tests a smartphone app that uses the phone camera and pose-estimation AI to measure arm movement in people with mild-to-moderate arm weakness after stroke.
Quick facts
| Study type | Observational |
|---|---|
| Ages | 18 Years and up |
| Sex | All |
| Enrollment | 30 (estimated) |
| Sponsor | King's College London Academic / other |
| Locations | 1 site (London) |
| Status | Recruiting |
| Started | July 28, 2025 |
| Main results expected | September 30, 2026 |
| Study ends | April 30, 2027 |
| Registry record updated | August 11, 2025 |
| Trial ID | NCT07016295 on ClinicalTrials.gov |
Is this trial a fit for you?
✓Likely a good fit
Adults with a history of stroke and mild-to-moderate upper limb impairment (Fugl-Meyer Upper Limb score 9–60/66) who can sit independently, consent, understand instructions, and transfer to the lab chair as needed.
✕Probably not a fit
People with severe cognitive or language deficits, marked shoulder pain (>3/10), inability to sit independently, or who cannot transfer from their wheelchair (or have a non-collapsible wheelchair backrest) are unlikely to benefit or be eligible.
A plain-English reading of the study's rules. Only the study team can confirm whether you qualify.
What this trial studies
The investigators will recruit 12 stroke survivors with mild-to-moderate upper limb impairment for a single 2–3 hour in-person session at King's College London, Guy's Campus. Participants will perform a range of upper-limb tasks while video is captured and simultaneously recorded with gold-standard 3-D motion capture for comparison. Open-source pose-estimation models will be applied to the phone video to extract kinematic measures, and models will be optimized if discrepancies with the gold standard are found. A smartphone front-end and software back-end will be developed to record, analyse, and integrate movement metrics into clinical records for clinician and patient feedback.
Why it matters
Potential benefit: If successful, the app could provide a low-cost, easy way for clinicians to monitor arm recovery and tailor rehabilitation without needing expensive motion-capture labs.
How similar studies have gone: Pose-estimation approaches have shown promising agreement with lab motion-capture in healthy and some clinical populations, but direct validation against gold-standard kinematics in stroke survivors remains limited.
Who can join: the full rules
Show full inclusion / exclusion criteria
Stroke survivors Inclusion Criteria: * History of stroke * Arm impairment evidenced by Fugl-Meyer Upper Limb Assessment between 9-60/66. Exclusion Criteria: * Severe cognitive impairment preventing ability to consent to treatment and understand and follow research protocol * Severe language deficit preventing ability to consent to treatment and understand and follow research protocol * Shoulder pain \>3/10 on visual analog scale * Unable to maintain independent sitting balance without a high back support. * Wheelchair users that are unable to transfer with assistance of 1 to lab chair or whose wheelchair backrest cannot colapse.
Where it's running
-
Centre for Human and Applied Physiological Sciences
London, United Kingdom Recruiting
Study contacts
- Study coordinator: Ulrike Hammerbeck, PhD
- Email: ulrike.hammerbeck@kcl.ac.uk
- Phone: +44 (0) 20 7888 6292
Similar studies
Other studies for the same condition or treatment. Recruiting studies are listed first.
-
Deep phenotyping to explore recovery in young adults after stroke
This project will test whether a detailed health and sample-collection approach (deep phenotyping) is practical and acceptable for people aged 18–55 who have had a stroke.
-
Stem cell therapy for chronic stroke recovery
This study is testing whether using a patient's own stem cells can help people with long-term stroke recovery feel better and improve their daily functioning.
-
AI-based tools to help diagnose, treat and predict stroke outcomes across multiple hospitals
This study tests AI-powered tools on real-world stroke patients to see if they can improve pre-hospital triage, diagnosis, and outcome prediction while data stay securely within each hospital.
-
Web-based therapy using action observation and mirror therapy for arm weakness after stroke
This trial tests whether a mobile telerehabilitation app delivering action observation therapy (AOT) alone or combined AOT with mirror therapy (AOT-MT) can improve upper limb function in people with subacute stroke.
-
Remote Ischaemic Conditioning for Fatigue After Stroke 2
Aat least 12 weeks after stroke, adults with post-stroke fatigue will try a simple hand-arm cuff treatment at home to see if it helps reduce fatigue.
-
Dual-task balance training plus computer-based cognitive training after stroke
In adults who recently had a stroke, the study tests whether adding computerized cognitive exercises to balance training improves balance and other recovery outcomes.
How to take part
- Review the eligibility criteria above with your treating physician.
- Check the official listing on ClinicalTrials.gov for the latest contact details and recruitment status.
- Contact the study coordinator or principal investigator to ask for pre-screening. Pre-screening is free and never obligates you to enroll.