GaitAI SportsMotion
Athlete movement measured, compared and tracked.
SportsMotion analyzes walking and running mechanics to surface asymmetry, imbalance, fatigue-related change and recovery trends.
Problem
Return-to-play and movement-quality decisions are frequently made from short visual assessments, athlete feedback and isolated tests.
What GaitAI does
AI-based motion analysis creates repeatable measurements that can be compared with the athlete's own baseline.
Who uses it
- Sports academies
- Performance centres
- Physiotherapists
- Strength and conditioning teams
- Rehabilitation teams
What they receive
Example report values — illustrative, not measured performance claims.
A widening left/right gap or fatigue-related drift is read as a movement-risk indicator worth investigating — it informs return-to-play conversations rather than deciding them.
Why it matters
Baselining every athlete makes deviation visible: post-injury sessions are compared against the athlete's own healthy movement, not a generic norm.
Example workflow
- 01Baseline the athlete pre-season
- 02Capture walking / running sessions over time
- 03AI compares against the individual baseline
- 04Asymmetry and fatigue-related change are flagged
- 05Coach and physio review the return-to-play summary
Deployment
- Pitch-side or treadmill capture from a phone or fixed camera
- Squad-level dashboards for academies and performance centres
- Integrates with RehabTrack for injury-rehabilitation blocks
- Optional wearable signals for training-load context
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.
- Pose estimation
- Pose-based gait analysis
- 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
- A robust covariate-invariant gait recognition based on pose featuresIET Biometrics · 2022
- Intra-class variations with deep learning-based gait analysis: A comprehensive survey of covariates and methodsNeurocomputing · 2022
Evidence status
GaitAI Evidence Index
4 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
- 1 published record directly addresses a capability this module is built on (Pose-based gait analysis).
View evidence for Research foundation
Claim / capability: 1 published record directly addresses a capability this module is built on (Pose-based gait analysis).
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.
- publicationA robust covariate-invariant gait recognition based on pose features · IET Biometrics, 2022
Inventory reviewed .
- Architectural research relationshipAvailable
- 5 published records inform shared platform capabilities this module uses (Gait recognition & biometrics). The research does not address this module's application specifically.
View evidence for Architectural research relationship
Claim / capability: 5 published records inform shared platform capabilities this module uses (Gait recognition & biometrics). 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
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 demoAvailable
- A sample output is available to explore. Illustrative demo — example values, not a measured result.
View evidence for Interactive demo
Claim / capability: A sample output is available to explore. Illustrative demo — example values, not a measured result.
Status: Available
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.
- prototypeExplore illustrative output
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 →
Sample output
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 movement-risk indicators, not injury predictions
- Camera placement and frame rate constrain joint-marker precision
- Return-to-play remains a clinical and coaching decision
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