GaitAI · MobilityCare

GaitAI ProstheticFit

Name: GaitAI ProstheticFit
Brand: GaitAI

Measure how an assistive device changes the walk.

ProstheticFit compares walking mechanics with different device configurations or across fitting and rehabilitation sessions.

Prosthetic & orthotic clinics

Request a pilot

Input

Before / after walks

Analysis

Movement comparison

Output

Movement evidence

User

Prosthetist

Problem

The movement effect of a fitting change is often judged from a brief visual check, leaving symmetry and loading changes invisible during fitting decisions.

What GaitAI does

Walks captured before and after an adjustment are compared side by side, so the movement effect of each fitting change is measured rather than assumed.

Who uses it

Prosthetic clinics
Orthotic clinics
Rehabilitation teams
Assistive-device researchers

What they receive

Movement comparison per configurationWalking symmetryLoading-asymmetry proxyStep patternWalking speedMobility improvement trend

+7%

Symmetry after adjustment

−11%

Loading asymmetry proxy

+0.06 m/s

Walking-speed change

Configurations compared

Example report values — illustrative, not measured performance claims.

Comparisons show how each configuration changed symmetry, loading proxies and speed for this person. They inform the fitting decision alongside comfort and clinical judgement.

Why it matters

Makes the effect of fitting changes visible rather than relying only on subjective comfort and visual observation.

Example workflow

01Capture a baseline walk
02Adjust the device
03Repeat the walk
04AI compares the two sessions
05Clinician reviews the measured change

Deployment

In-clinic before / after capture during fitting sessions
Configuration metadata attached to each session
Longitudinal record across fitting and rehabilitation
Research mode for device 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

Research foundation · 6 records

Full evidence map

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 2026-09-13.
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 2026-09-13.
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 2026-09-13.
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 2026-09-13.
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 2026-09-13.
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 2026-09-13.
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 2026-09-13.
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 2026-09-13.
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 2026-09-13.
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 2026-09-13.

Inventory reviewed 2026-09-13How 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

Loading proxies are movement-derived, not direct force measurements
Comfort and residual-limb health remain clinical inputs outside video data
Comparisons describe movement change, not device safety or certification

GaitAI · ProstheticFit

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