GaitAI ClinicalTrials
Structured, repeatable movement-analysis outputs for research, clinical-study and device-evaluation workflows.
ClinicalTrials provides structured gait and movement measurements that can be collected repeatedly across participants and study visits.
Problem
Human-movement endpoints can be expensive, inconsistent and difficult to standardise across visits and sites.
What GaitAI does
Protocol-guided captures are processed through a consistent pipeline with quality control, producing participant-level metrics and cohort-level exports suitable for downstream analysis.
Who uses it
- Clinical research organisations
- Academic studies
- Pharma research
- Medical-device studies
- Rehabilitation studies
- Digital-biomarker research
What they receive
Example report values — illustrative, not measured performance claims.
Outputs are research endpoints — digital movement biomarker candidates collected under a consistent protocol. Their validity as surrogate endpoints is a question for each study's own analysis.
Why it matters
Standardised capture and processing make movement endpoints repeatable across visits and sites — the consistency layer studies usually have to build themselves.
Example workflow
- 01Define the capture protocol per study
- 02Sites capture protocol-guided walks per visit
- 03QC gates every capture
- 04Metrics accumulate per participant
- 05Cohort trends and exports are generated for analysis
Deployment
- Multi-site capture with a shared protocol
- Participant / visit identifier scheme per study
- Study dashboard with QC visibility
- Structured exports for statistical pipelines
Research basis
Published work is the research foundation for the capabilities this product is built on. That record covers the movement-analysis methods themselves — it is not a validation of this product's outputs, and there is no clinical validation study, accuracy benchmark or trial result behind them.
- Gait analysis
- Gait recognition & biometrics · Pose-based gait analysis
- Advancements in Artificial Intelligence for Biometrics: A Deep Dive into Model-based Gait Recognition TechniquesEngineering Applications of Artificial Intelligence · 2024
- Journey into Gait Biometrics: Integrating Deep Learning for Enhanced Pattern RecognitionDigital Signal Processing · 2024
- Deep learning pipelines for recognition of gait biometrics with covariates: a comprehensive reviewArtificial Intelligence Review · 2023
- Data Preprocessing and Feature Selection Techniques in Gait Recognition: A Comparative Study of Machine Learning and Deep Learning ApproachesPattern Recognition Letters · 2023
- Intra-class variations with deep learning-based gait analysis: A comprehensive survey of covariates and methodsNeurocomputing · 2022
- A robust covariate-invariant gait recognition based on pose featuresIET Biometrics · 2022
Evidence status
GaitAI Evidence Index
2 categories documented
What is documented for this module, and what is not. Research foundation is not product validation — the two are listed separately on purpose.
- Research foundationNot yet published
- No published record in this library directly addresses a capability this module is built on.
View evidence for Research foundation
Claim / capability: No published record in this library directly addresses a capability this module is built on.
Status: Not yet published
Applicability: The cited work is specifically about a method or movement signal this module draws on.
Limitation: Research relevance is not evidence that this product's outputs have been validated.
No supporting source is published for this status.
Inventory reviewed .
- Architectural research relationshipAvailable
- 6 published records inform shared platform capabilities this module uses (Gait recognition & biometrics, Pose-based gait analysis). The research does not address this module's application specifically.
View evidence for Architectural research relationship
Claim / capability: 6 published records inform shared platform capabilities this module uses (Gait recognition & biometrics, Pose-based gait analysis). The research does not address this module's application specifically.
Status: Available
Applicability: Shared capabilities such as pose estimation or temporal modelling are used by many modules.
Limitation: A shared-platform relationship is the weakest research link and must not be read as validating this module.
- publicationAdvancements in Artificial Intelligence for Biometrics: A Deep Dive into Model-based Gait Recognition Techniques · Engineering Applications of Artificial Intelligence, 2024
- publicationJourney into Gait Biometrics: Integrating Deep Learning for Enhanced Pattern Recognition · Digital Signal Processing, 2024
- publicationDeep learning pipelines for recognition of gait biometrics with covariates: a comprehensive review · Artificial Intelligence Review, 2023
- publicationData Preprocessing and Feature Selection Techniques in Gait Recognition: A Comparative Study of Machine Learning and Deep Learning Approaches · Pattern Recognition Letters, 2023
- publicationIntra-class variations with deep learning-based gait analysis: A comprehensive survey of covariates and methods · Neurocomputing, 2022
- publicationA robust covariate-invariant gait recognition based on pose features · IET Biometrics, 2022
Inventory reviewed .
- Product specificationAvailable
- The module has a documented specification: inputs, pipeline, outputs and stated limitations.
View evidence for Product specification
Claim / capability: The module has a documented specification: inputs, pipeline, outputs and stated limitations.
Status: Available
Applicability: Documented inputs, intended workflow and output design.
Limitation: Documentation does not establish a released implementation or deployment readiness.
- specificationProduct documentation
Inventory reviewed .
- Product implementation evidenceNot yet published
- No module-specific release or deployment record is published here.
View evidence for Product implementation evidence
Claim / capability: No module-specific release or deployment record is published here.
Status: Not yet published
Applicability: Production implementation is distinct from this website's interactive prototypes.
Limitation: The browser pose explorer is not the full clinical or operational product pipeline.
- Context · not validation evidenceProduct and deployment context
Inventory reviewed .
- Interactive demoNot yet published
- No sample output has been built for this module yet.
View evidence for Interactive demo
Claim / capability: No sample output has been built for this module yet.
Status: Not yet published
Applicability: An interactive explanation of the intended output and workflow.
Limitation: Sample outputs contain illustrative values and are not measurements of a visitor or product performance.
No supporting source is published for this status.
Inventory reviewed .
- Benchmark validationNot yet published
- Validation pending publication. No product benchmark numbers are supplied.
View evidence for Benchmark validation
Claim / capability: Validation pending publication. No product benchmark numbers are supplied.
Status: Not yet published
Applicability: A benchmark needs a defined dataset, sample count, environment, hardware and evaluation method.
Limitation: Accuracy, sensitivity, specificity, latency and FPS cannot be inferred from a demonstration.
- Context · not validation evidenceResearch evidence inventory
Inventory reviewed .
- Product-specific validationNot yet published
- No study in the published record evaluates this module's output for a particular intended use.
View evidence for Product-specific validation
Claim / capability: No study in the published record evaluates this module's output for a particular intended use.
Status: Not yet published
Applicability: Validation must match the module, population and intended decision.
Limitation: A product description or related research paper does not establish validated performance.
- Context · not validation evidencePublished evidence inventory
Inventory reviewed .
- Pilot & deployment evidenceNot yet published
- No named pilot, deployment result or customer outcome is documented.
View evidence for Pilot & deployment evidence
Claim / capability: No named pilot, deployment result or customer outcome is documented.
Status: Not yet published
Applicability: Pilot and deployment evidence is assessed separately from research.
Limitation: Use-case journeys describe intended workflows, not customer results.
- Context · not validation evidenceTrust Center
Inventory reviewed .
- Product-specific clinical validationNot yet published
- No product-specific clinical validation study is published in this library.
View evidence for Product-specific clinical validation
Claim / capability: No product-specific clinical validation study is published in this library.
Status: Not yet published
Applicability: Clinical applicability depends on the intended use; safety modules are not presented as clinical tools.
Limitation: No output replaces clinician judgement or establishes a diagnosis.
- Context · not validation evidenceResponsible use and intended context
Inventory reviewed .
- Regulatory clearanceNot claimed
- No medical-device clearance, regulatory approval or compliance status is claimed.
View evidence for Regulatory clearance
Claim / capability: No medical-device clearance, regulatory approval or compliance status is claimed.
Status: Not claimed
Applicability: Regulatory applicability depends on the intended use and deployment jurisdiction.
Limitation: Research publications, patents and prototypes are not regulatory clearances.
- Context · not validation evidenceWhat GaitAI does not claim
Inventory reviewed .
Inventory reviewed How GaitAI states evidence →
Privacy & responsible use
Designed to support consent-based capture, encrypted transfer, configurable retention and role-based access, with activity logging and skeleton-only processing modes. Deployment requirements depend on the applicable environment, organization and jurisdiction. Privacy-aware architecture does not automatically mean anonymity: pose, gait and trajectories may still carry identifying information. GaitAI outputs are AI-generated movement metrics intended as decision support — they do not diagnose medical conditions and do not replace clinical judgement.
Context & limitations
- Outputs are digital movement biomarker candidates — validated surrogate endpoints require study-level evidence
- Cross-site consistency depends on protocol adherence
- GaitAI provides measurements and exports; study design and analysis remain with the research team
Related MobilityCare products
All products
Turn a walking video into a clinician-ready movement report.
Analyzes a short walking video and turns it into gait and mobility metrics — ready as a downloadable PDF.
Continuous movement intelligence from the wrist.
Combines smartwatch, mobile and wearable sensor data to estimate daily mobility and activity patterns, gait variability and longitudinal fall-risk indicators for clinician, caregiver or remote-care review.
Monitor Parkinsonian, stroke, ataxia & MS gait patterns.
Supports ongoing monitoring of gait patterns linked to neurological movement difficulties, including freezing, shuffling and turning instability.