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Video-AI-based functional assessment

Objective measurement of how patients move.

TinyMoves helps neurorehab teams quantify motor change from research-grade video — starting with stroke recovery, Parkinson's, and pediatric developmental milestones — enabling clinician review, evidence generation, and low-friction deployment.

Built with clinical and research collaborators
University of Pennsylvania Children's Hospital of Philadelphia Brooks Rehabilitation University of California, San FranciscoMemorial Sloan Kettering Cancer Center
Shirley Ryan AbilityLabNorthwestern University

How it works

From functional assessment to clinician-reviewable measurements

Designed to fit existing visits and at-home follow-up without dedicated motion-capture hardware.

01

Record

Capture a short, standardized movement task on a supported phone or tablet, in clinic or at home.

02

Measure

The pipeline extracts kinematic features and prepares privacy-controlled measurement outputs.

03

Review

Clinicians review structured measurements over time to inform assessment and research workflows.

A parent captures a short movement video of their baby on a smartphone, with movement tracking shown on screen

Capture can fit into real life: a patient at home, a caregiver nearby, or a child's parent.

Why TinyMoves

Built like a study. Designed for the clinic.

Objective by design

Repeatable quantitative measures add context to episodic clinical observation.

Fits existing workflows

Designed around familiar devices, short tasks, and clinician review.

Privacy-controlled

Access control, auditability, and retention settings are designed for deployment-specific clinical policies.

Explainable outputs

Movement measures are visible, inspectable, and longitudinal rather than reduced to a standalone verdict.

Evidence-led roadmap

Validation, usability, and regulatory planning are built into the product path before stronger clinical claims.

Built to expand

One functional assessment workflow can support adjacent movement conditions as real-world evidence base grows.

Platform

From consumer device to measurements clinicians can review

A short functional assessment is captured on a phone or tablet, at home or in clinic. TinyMoves turns the video into structured motor measurements organized by patient and visit for ongoing clinician review.

2 min
Typical capture time
Mobile
No dedicated motion-capture hardware
Instant
Structured review output after accepted capture
Gait assessment with movement-tracking overlay during a clinic walk test

Scaling across care

One video workflow, expanding by indication

The first focus is neurorehabilitation, where clinicians need practical ways to quantify recovery between visits. Research and biopharma collaborations can build on the same measurement workflow as the evidence base grows.

Beachhead

Stroke, Parkinson's, and Cerebral Palsy

Recovery tracking, follow-up, and measurement-backed functional assessments of daily living.

Team

Clinicians, scientists, and serial entrepreneurs

Built by operators across the integration gap — MedTech engineering, clinical neuroscience, AI/ML, functional assessment, FDA regulatory, and commercial translation — building movement measurement that is prospectively validated.

Shobi Ahmed

Shobi Ahmed, MD, PhD

CEO & Founder

Physician-scientist and neuro-AI venture studio founder, trained at Johns Hopkins, Yale, MIT, and Harvard.

#1 on the BrainClinics Biomarker Challenge, beating dozens of industry and academic teams · APA Innovation Award · Historian-anthropologist · MAPFRE Social Innovation Award · SXSW Pitch finalist · AARP AgeTech finalist · Endless Frontier Labs · PharmStars · MIT/NIDA venture fellow · Advanced pipelines at nference (AI-driven target ID) and Biohaven Pharma · Founding Director of Clinical & Scientific Operations at Transcend Therapeutics, development strategy and patent portfolio for a neuroplastogen/PTSD asset acquired by Otsuka for $1.2B.

Konrad Kording

Konrad Kording, PhD

SAB Chair & Founder

Pioneer of video-based movement measurement and Penn neuro-AI professor with 35,000+ citations.

Inaugural Nathan Francis Mossell University Professor at Penn, across Bioengineering, Neuroscience, and Computer Science · Director of CIFAR Learning in Machines & Brains · ETH Zurich physics PhD and former Heisenberg Fellow at MIT · Wrote the field-defining case for movement-specific pose tracking · Creator of a top-five motion-labs consortium and global research institutions · $15M+ in NIH-funded neuromotor research relevant to TinyMoves · Co-founder of Neuromatch Academy.

Olivier Delrieu

Olivier Delrieu, MD, MBA

Chief Medical Officer

Neurologist and clinical-AI entrepreneur, ex-GlaxoSmithKline executive with numerous FDA/EMA approvals.

Board-certified neurologist with 20+ years across big pharma, biotech, and digital health · At GlaxoSmithKline, helped win EU approval and launch of ropinirole XR for Parkinson’s (Gold Award for regulatory presentation) · Designed a Phase II Alzheimer’s protocol that earned positive EMEA scientific advice and presented to a joint FDA–EMA–PMDA panel · Founded and exited Adorial (acquired by C4X Discovery), inventing a proprietary genomic analytics engine.

Ben Wensley-Stock

Ben Wensley-Stock, MPhil

Chief Technology Officer

Medical-device regulatory executive with world-first video-AI “Software as Medical Device” FDA clearance.

Drove the world-first Software as Medical Device approval for camera-based vital-sign monitoring at Oxehealth, an Oxford University spinout · Chief Technology Officer at Syntrillo, an AI stroke-prevention platform · Expert in Residence for Medical Devices at the University of Oxford · Has led regulatory across six venture-backed companies, spanning ISO 13485 quality systems, CE marking, and FDA clearance · Device “Responsible Person” and Importer under U.K. MDR, EC-REP · Exited Oxehealth (now LIO Health).

Eric Zhang

Eric Zhang

Founding Platform Engineer

Full-stack engineer with 8+ years experience, building the capture, analysis, and EHR-integrated video-AI pipeline.

Architecting the full cloud infrastructure for TinyMoves' HIPAA-aware clinical movement-analysis platform · Built CI/CD pipelines and multi-tenant architecture with cross-tenant data isolation · Integrated modern vision and video models into the movement-analysis pipeline with GPU cost optimization · Led security-vulnerability remediation with contribution to ISO 13485 / IEC 62304 regulatory documentation · 8+ years across SaaS, real-time systems, and applied AI, including agentic LLM workflows at Virtual Hatch and real-time marketplace platforms · B.Sc. in Information & Communication Technology, Singapore University.

Karim El Kanbi

Karim El Kanbi, PhD

Head of AI/ML Data Engineering

Production AI engineer architecting neural- and movement-biomarker pipelines from raw signal to deployed inference.

ESPCI Paris-PSL PhD in Neurobiology and Behavior, with deep multimodal and sleep-physiology research · former Head of Data at INBRAIN Neuroelectronics and Senior Data Scientist at Dreem, designing deep-learning models for polysomnography event detection on large clinical and preclinical animal datasets · École Centrale Paris engineer fluent across signal processing, deep learning, and production MLOps · co-architect of the TinyMoves video-analysis platform, owning the path from neural and movement signals through to scalable deployed inference.

Maxpol Le Brun

Maxpol Le Brun, MS

VP Platform Engineering

Neurotech platform engineer with 10+ years spanning biosignal hardware-software and embedded systems.

École Centrale Paris engineer who helped bring the Dreem sleep-physiology headband from prototype to shipped and FDA-cleared neurotechnology, working across embedded firmware, signal acquisition, and product · a decade building neuro-AI systems end to end — capture hardware, data infrastructure, and the analytics layer on top · co-architect of the TinyMoves clinical platform, driving reliability, deployment, and developer velocity across the full stack.

Mark Hiatt

Mark Hiatt, MD, MBA

Chief Business Officer

Physician-executive who turns clinical evidence into payer coverage across precision medicine and diagnostics.

Physician-executive who has led market access, medical affairs, and health economics across precision medicine and diagnostics · Senior Vice President of Market Access and Strategy at BostonGene · former Vice President of Medical Affairs at Guardant Health, Executive Medical Director at Regence BlueCross BlueShield, and Chief Medical Officer at HealthHelp · MD and MBA from Wake Forest, master's in health evaluation sciences from the University of Virginia, and a cardiovascular imaging fellowship at Stanford · Founder of Hiatt Advisory Services, advising health-technology companies on coverage and reimbursement.

Laura Prosser

Laura Prosser, PT, PhD

Clinical Advisor · CHOP / Penn

CHOP/Penn clinician-scientist in pediatric motor development and neurorehabilitation.

Professor of Pediatrics at the University of Pennsylvania and research scientist at the Children's Hospital of Philadelphia · NIH Principal Investigator leading the iMOVE trial on altering motor-development trajectories in toddlers with cerebral palsy · $15M+ in NIH-funded neuromotor research relevant to TinyMoves · Leads the PANDA and PLAY projects, building large video and sensor datasets of infant movement · Co-author with Konrad Kording on video-based motion tracking for early cerebral palsy risk · Contributor to the motor measures of the NIH Baby Toolbox.

Simon Little

Simon Little, MD, PhD

Clinical Advisor · UCSF

UCSF movement-disorders neurologist working on adaptive neurostimulation and Parkinson's care.

Associate Professor of Neurology at UCSF and member of the Weill Institute for Neurosciences · Oxford PhD under Peter Brown · Pioneer of adaptive, closed-loop deep brain stimulation for Parkinson's — work underpinning one of the first adaptive DBS algorithms cleared by the FDA in 2025 · Published in Nature Communications on real-time, machine-learning-driven neurostimulation.

Melanie Segado

Melanie Segado, PhD

AI/ML Advisor · UPenn

UPenn neuromotor science and AI/ML researcher working on pediatric video-based movement modeling.

Applied AI researcher in Konrad Kording's lab at the University of Pennsylvania · McGill PhD in neuroscience · Lead author on the open, pre-registered pipeline for early cerebral palsy risk assessment from infant video, published in GigaScience · Works on self-supervised movement foundation models and multimodal AI — vision transformers and large language models — for clinical movement analysis.

Tyler Moore

Tyler Moore, PhD

AI/ML Advisor · UPenn

UPenn professor of statistical machine learning, focused on multimodal measurement of brain and behavior.

Professor at the University of Pennsylvania, with 14,000+ citations across psychometrics and quantitative neuroscience · Specialist in item response theory, factor analysis, and adaptive testing in the Brain Behavior Laboratory · Served as quantitative lead on the cognitive arm of NASA's landmark Twins Study, published in Science, comparing one identical twin through year-long spaceflight against his genetically matched twin on Earth · His work on cognition as a precise, longitudinal biomarker converges directly with TinyMoves' movement measurement.

Clarity for clinicians

Common questions

TinyMoves extracts quantitative movement features from short research-grade videos so clinicians can track change over time. It is designed to support clinical review and research workflows; it does not diagnose, replace clinician judgment, or provide emergency assessment.
After a clip is captured and accepted, structured measurements are available for clinician review in minutes. Each result is filed against the relevant patient record and visit, so a clinician can open a patient and see their measurements over time rather than working from loose, unattributed clips.
Wearables can support continuous real-world monitoring, and motion labs can provide high-precision biomechanics. TinyMoves is designed for short, standardized video tasks that fit clinical and at-home workflows without dedicated motion-capture hardware.
TinyMoves is designed around controlled access, auditability, and deployment-specific privacy (anonymization, etc), and retention policies. Approved privacy and clinical-use materials are linked from this page.
TinyMoves is being developed and piloted as a clinician-reviewed movement measurement workflow. Stronger clinical and regulatory claims depend on the intended use, validation evidence, and clearance strategy approved for each deployment.
No. TinyMoves gives clinicians objective measurements to inform their assessments and care decisions — the clinician remains in control.

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into your practice or clinical trial

Request a walkthrough of the platform, or talk to us about a research, clinical, or biopharma collaboration.