GaitAI AccessMotion
A passive second factor — your walk.
AccessMotion uses gait and movement consistency as an additional authentication signal alongside authorized primary methods — a passive second factor for high-security spaces.
Problem
High-security access can benefit from an additional passive consistency signal beyond a single credential.
What GaitAI does
Movement consistency between the presenting person and their enrolled movement profile produces an access-confidence signal that complements — never replaces — the primary credential.
Who uses it
- Data centres
- R&D laboratories
- Defence and government campuses
- High-security corporate sites
What they receive
Example report values — illustrative, not measured performance claims.
A low consistency score is a prompt for secondary verification — not a denial by itself. Policy decides what happens at each confidence band; AccessMotion supplies the signal.
Why it matters
A stolen badge walks differently than its owner. A passive consistency signal raises the bar at sensitive doors without adding user friction.
Example workflow
- 01Enrolled personnel consent to movement-profile enrolment
- 02At entry, primary credential is presented as usual
- 03Movement consistency is scored passively
- 04Low-consistency events flag for secondary verification
- 05All decisions log to the access audit trail
Deployment
- Consent-based enrolment for authorized personnel
- Integration with existing access-control systems
- Site-configured confidence thresholds
- Positioned as an additional factor, not sole authentication
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.
- Movement biometrics
- Gait recognition & biometrics
- Edge inference
- Edge gait analytics (Patent 402202)
- Privacy-aware analytics
- Privacy-preserving gait data
- 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 Covariate-Based Gait Recognition System and Method for Edge Analytics Using Optimized Deep Learning PipelinePatent — Government of India · 2022 · Patent 402202
- Protection of gait data set for preserving its privacy in deep learning pipelineIET 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 foundationAvailable
- 7 published records directly address a capability this module is built on (Gait recognition & biometrics, Privacy-preserving gait data, Edge gait analytics (Patent 402202)).
View evidence for Research foundation
Claim / capability: 7 published records directly address a capability this module is built on (Gait recognition & biometrics, Privacy-preserving gait data, Edge gait analytics (Patent 402202)).
Status: Available
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.
- 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
- publicationProtection of gait data set for preserving its privacy in deep learning pipeline · IET Biometrics, 2022
- publicationA Covariate-Based Gait Recognition System and Method for Edge Analytics Using Optimized Deep Learning Pipeline · Patent — Government of India, 2022
Inventory reviewed .
- Architectural research relationshipNot yet published
- No further shared-platform research relationship is recorded beyond the research foundation above.
View evidence for Architectural research relationship
Claim / capability: No further shared-platform research relationship is recorded beyond the research foundation above.
Status: Not yet published
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.
No supporting source is published for this status.
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. Movement analytics are designed to run without identifying individuals unless a deployment lawfully and explicitly requires it, and identity-related capabilities are intended only for lawful, authorized deployments with appropriate governance, access control and auditability. Outputs are decision support for trained operators — not autonomous enforcement. Watchlist, ReID, AccessMotion and ForensicSearch require documented lawful authority, consent where applicable, purpose-limited access, audit logs and human review. Candidate associations are not proof of identity and must not trigger automatic denial, enforcement or adverse decisions.
Context & limitations
- An additional factor — never sole authentication
- Requires consented enrolment of authorized personnel
- Capture conditions at the entry zone affect signal quality
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