GaitAI NeuroMotion
Quantify neurological movement patterns over time.
NeuroMotion supports objective monitoring of gait patterns associated with neurological movement difficulties — including Parkinsonian gait monitoring, stroke rehabilitation, ataxic gait, multiple-sclerosis-related mobility change and neuropathy-related gait change.
Problem
Small changes in shuffling, asymmetry, turning and walking stability can be difficult to quantify using observation alone.
What GaitAI does
Guided walking and turning assessments are converted into repeatable neuro-relevant movement indicators tracked against the person's own history.
Who uses it
- Neurologists
- Neurorehabilitation centres
- Hospitals
- Movement-disorder clinics
- Research labs
What they receive
Example report values — illustrative, not measured performance claims.
Indicators quantify how movement patterns are changing over time. NeuroMotion does not diagnose neurological conditions — it gives the clinical team a measured record to read alongside their assessment.
Why it matters
Provides a repeatable movement record that can complement clinical assessment — the same protocol, measured the same way, at every visit.
Example workflow
- 01Run a guided walk-and-turn protocol
- 02AI extracts neuro-relevant movement indicators
- 03Session is added to the person's longitudinal record
- 04Clinician reviews change since previous sessions
- 05Record complements the clinical assessment
Deployment
- Clinic-corridor capture with a phone or fixed camera
- Repeatable protocols for consistent session comparison
- Longitudinal record per patient
- Research-mode export for movement-disorder studies
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
- NeuroMotion does not diagnose Parkinson's disease, stroke, MS or any condition
- Indicators are movement descriptors; their clinical meaning is determined by the care team
- Session comparability depends on following the guided protocol
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