The full evidence record.
Every published record behind the platform, mapped to the capabilities it informs and the modules built on them. Filter by year or record type.
Research establishes the methodological foundation. Product-specific validation establishes fitness for a particular use.
Filter the published record, and trace what it informs.
Every chain below runs published record → research foundation → capability informed → module built on that capability → and then stops, at product-specific validation, which is a separate step.
Published journal research on deep-learning gait recognition with covariates (Springer, Elsevier).
- Deep learning pipelines for recognition of gait biometrics with covariates: a comprehensive reviewArtificial Intelligence Review · 2023 · Journal
- Intra-class variations with deep learning-based gait analysis: A comprehensive survey of covariates and methodsNeurocomputing · 2022 · Journal
- Advancements in Artificial Intelligence for Biometrics: A Deep Dive into Model-based Gait Recognition TechniquesEngineering Applications of Artificial Intelligence · 2024 · Journal
- Journey into Gait Biometrics: Integrating Deep Learning for Enhanced Pattern RecognitionDigital Signal Processing · 2024 · Journal
- Data Preprocessing and Feature Selection Techniques in Gait Recognition: A Comparative Study of Machine Learning and Deep Learning ApproachesPattern Recognition Letters · 2023 · Journal
- Comparative Study of Machine Learning and Deep Learning Techniques for Gait Recognition: Advances in Feature Extraction, Reduction, Transformation, and ClassificationImage and Vision Computing · 2023 · Journal
- Gait recognition
- Biometrics
- Covariates
- Deep learning
- Computer vision
- Video surveillance
- Movement biometricsIdentity-bearing movement signatures beyond face or fingerprint.
- Gait analysisCadence, stride rhythm, speed, asymmetry, posture, balance, variability.
- Person re-identificationExploring movement-based associations across camera views; associations need validation and human review.
Built on a capability this record addresses directly.
Reached only through a broad platform capability. Listed for traceability — this record does not address these modules, and must not be read as evidence for them.
A separate step, carried out per product and per deployment. See research foundation ≠ product validation.
Covariate-invariant gait recognition built on pose features (IET Biometrics).
- A robust covariate-invariant gait recognition based on pose featuresIET Biometrics · 2022 · Journal
- Biometrics
- Covariates
- Deep learning
- Gait recognition
- Pose estimation
- Pose estimationEstimates visible body landmarks from video; throughput and reliability depend on the model and capture conditions.
- Gait analysisCadence, stride rhythm, speed, asymmetry, posture, balance, variability.
Built on a capability this record addresses directly.
Reached only through a broad platform capability. Listed for traceability — this record does not address these modules, and must not be read as evidence for them.
A separate step, carried out per product and per deployment. See research foundation ≠ product validation.
Protecting gait datasets inside deep-learning pipelines (IET Biometrics).
- Protection of gait data set for preserving its privacy in deep learning pipelineIET Biometrics · 2022 · Journal
- Privacy-aware data handling
- Privacy-preserving gait-analysis principles
- Minimising unnecessary identity exposure
The record establishes the research principles. The implementation controls are a separate GaitAI build, configured per deployment — this work does not demonstrate them.
- Privacy-aware analyticsPrivacy-aware data handling that minimises unnecessary identity exposure.
Built on a capability this record addresses directly.
None.
Reached only through a broad platform capability. Listed for traceability — this record does not address these modules, and must not be read as evidence for them.
A separate step, carried out per product and per deployment. See research foundation ≠ product validation.
Granted Indian patent: covariate-based gait recognition for edge analytics.
- A Covariate-Based Gait Recognition System and Method for Edge Analytics Using Optimized Deep Learning PipelinePatent — Government of India · 2022 · Patent
- Edge inferenceOptimized on-device movement analytics pipelines.
Reached only through a broad platform capability. Listed for traceability — this record does not address these modules, and must not be read as evidence for them.
A separate step, carried out per product and per deployment. See research foundation ≠ product validation.
What the record establishes, and what it does not.
- Peer-reviewed researchPublished with Springer, Elsevier and Wiley · IET.
- A granted patentPatent 402202, covering the edge-analytics pipeline.
- Capability traceabilityEach capability maps back to the records behind it.
- Methodological foundationThe methods the platform's movement layer is built on.
- Clinical validationPer product, per intended use, against a clinical reference.
- Sensitivity & specificityNo accuracy or error-rate figures are established here.
- Multi-site datasetsEvidence that a result holds beyond one setting.
- Named deployment evidenceDocumented outcomes from a specific deployment.
Research establishes the methodological foundation. Product-specific validation establishes fitness for a particular use — a separate step, carried out per product and per deployment.