An AI-based assessment of musculoskeletal health
Motion Age
A cross-sectional study of 1,139 adults examining the biomechanical features that contribute most strongly to an age-equivalent movement index — measured entirely from markerless motion analysis.
1,139
Participants
2 × 3
Tasks × repetitions
Markerless
Capture
Why Motion Age
Movement is a whole-system signal.
Movement is a whole-system expression of joint range, balance, coordination, neuromuscular control and strength. Conventional assessments can be subjective or isolate a single parameter. Motion Age combines multiple kinematic features and expresses the resulting model output as an age-equivalent value that is easier to communicate.
Motion Age is an objective marker for age based on an individual’s movement.
Working definition — what it is, and what it is not
The output
An age-equivalent index generated by a supervised model trained on markerless movement features.
The evidence
Feasibility, feature attribution and age-estimation performance within this cross-sectional dataset.
Not established
Diagnosis, prediction of future decline, treatment response or a direct measure of biological ageing.
A Short, Standardised Protocol
Two everyday movements, captured cleanly.
Participants completed three sit-to-stand repetitions and three squats in a controlled indoor environment. From standard video, MAI Motion generated markerless 3D kinematics, a supervised model then estimated Motion Age and ranked feature attribution.
Sit-to-stand
Natural, self-selected pace from a chair with backrest and armrests.
Three consecutive repetitions
Squat
Shoulder-width stance and descent to a comfortable, self-selected depth.
Three consecutive repetitions
Record
Standard RGB camera; 1920 × 1080 resolution at 60 frames per second.
Extract
MAI Motion® generated markerless 3D joint and segment kinematics.
Characterise
Angles, range of motion, ab/adduction, smoothness and asymmetry were calculated.
Model
Google AutoML estimated Motion Age and ranked relative feature attribution.
What Drove The Model
Knee mechanics carried the strongest signal.
Knee-specific mechanics — particularly frontal-plane range and asymmetry — carried the strongest attribution scores. Scores describe relative influence inside this model. They are not clinical thresholds, causal effects or measures of disease severity.
Leading feature attribution
- Right knee ab/adduction — average RoM7
- Left knee angle — average RoM6
- Knee asymmetry4
- Left shoulder–knee segment — average RoM3
- Trunk–core smoothness / core asymmetry2
Signal, Error And Calibration
A real trend, reported with honest limits.
The model captured a broad age-related trend but retained substantial unexplained variation across the full cohort.
Age-band calibration
Prediction error was lowest in densely represented mid-life and early older-age bands. The reported MAE in the 60–65-year band was approximately 3.3 years.
Evidence Status
A research index.
Motion Age is a feasibility and feature-attribution study. It demonstrates that a supervised model can express markerless movement patterns as an age-equivalent value, and identifies which biomechanical features drive that output.
Prospective validation against clinical outcomes is required before any diagnostic, prognostic or treatment-response use.
Motion Age
An AI-based assessment of musculoskeletal health from markerless motion analysis.
Background
Functional mobility declines with age and is associated with frailty, falls and poor health outcomes. This study evaluated the biomechanical features most predictive of an age-equivalent Motion Age index using MAI Motion.
Methods
This cross-sectional study analysed 1,139 adults performing three sit-to-stand and three squat repetitions. Markerless 3D kinematic features were extracted and a supervised AI model estimated Motion Age and ranked feature attribution.
Results
Full-cohort MAE was 13.4 years (RMSE 16.1; R² 0.37). Knee ab/adduction range, knee angle range and knee asymmetry were the leading contributors. Error was lowest in densely represented mid-life bands, but predictions were compressed towards the sample mean.
Conclusion
Motion Age is a feasible, interpretable movement index with potential for screening and longitudinal monitoring. Prospective validation against clinical outcomes is required before diagnostic or treatment-response use.
From Age Estimation To Meaningful Change
The research agenda ahead.
The next phase is to establish whether Motion Age is reliable, responsive and associated with outcomes that matter to patients.
External validation
Test performance in independent and demographically balanced cohorts.
Reliability
Quantify repeatability across sessions, settings, cameras and assessors.
Clinical validity
Relate Motion Age to pain, function, falls, frailty and imaging phenotypes.
Responsiveness
Determine whether meaningful change follows rehabilitation or treatment.
Bias correction
Address regression towards the mean and improve calibration at age extremes.
Motion Age
An interpretable movement index, measured objectively.
Motion Age combines markerless movement features into an age-equivalent value with potential for screening and longitudinal monitoring, with prospective clinical validation ahead.