An AutismAI Lab application
Objective motor phenotyping, in the browser.
Volitiq turns any device with a camera into a research-grade motor assessment tool. It captures skeletal landmarks on-device with MediaPipe,[Lugaresi et al., 2019] computes interpretable kinematic features, and lets researchers and clinicians track how people move over months and years — with no installation, no specialist hardware, and raw video staying on the device by default.
MediaPipe extracts face, body and hand landmarks at ~30 fps directly in the browser, on consumer laptops, tablets and phones — no plug-in, no lab.
Every recording yields 40+ named kinematic features across seven motor-control domains — each with a definition and a formula, not a black-box score.
The same feature framework applies to autism research, physiotherapy progress, elderly mobility and fall risk, and inclusive education.
The challenge
Motor differences are well documented — but measuring them objectively is gated behind specialist labs.
Motor coordination differences appear in a large share of autistic individuals — meta-analysis puts the effect size well above the threshold for clinical significance.[Fournier et al., 2010] The "micro-movement" perspective argues these fine-grained motor signals carry information that standard observation misses.[Torres et al., 2013]
Yet objective motor assessment has traditionally required force transducers or marker-based motion capture, confined to a small set of research clinics. The same gating applies to aging, rehabilitation and disability research. Volitiq removes the apparatus: the skeletal-pose features that drive modern motor research are recoverable from ordinary webcam video.
What Volitiq measures
Seven motor-control domains.
The 40+ features are organised into seven domains grounded in sensorimotor-control research.[Wolpert et al., 2011] Movement smoothness, for example, is quantified with established, validated measures.[Balasubramanian et al., 2015]
How movement starts — reaction time and time-to-peak-velocity from the moment a task begins.
How fluid a movement is, via jerk-based measures and spectral arc length (SPARC).
Trial-to-trial consistency of a repeated movement — the stability of a motor pattern.
Phase relationships between joints and limbs, recovered with Hilbert-transform phase coupling.
The timing regularity of repetitive or cyclic movement.
Signal complexity of the movement trace, via approximate entropy and fractal dimension.
Economy of movement — path efficiency between start and target.
How the analysis works
Transparent statistics, not a black box.
From the landmark stream, Volitiq computes velocity, acceleration and jerk with central-difference derivatives and Savitzky-Golay smoothing, then derives the domain features. Group comparisons use classical, inspectable statistics — every number can be traced back to a formula.
Unequal-variance group comparison across features.
Effect size, so differences are reported by magnitude, not just significance.
A transparent ranking that combines significance and effect size.
Frequency-domain analysis for rhythm and longitudinal change.
Volitiq is a capture-and-analysis platform. It does not ship trained diagnostic models. Research teams that have validated their own classifiers can apply them to new recordings through the platform; the classifier, its training data, and its claims remain the researcher's own.
Tracking change over time
Three dashboards, one secure backend.
Per-participant, session-over-session views of how velocity, acceleration, jerk and derived features change across weeks and months.
Cohort comparisons with statistical effect sizes, so a group difference is quantified, not just visualised.
Distribution and correlation views within a single group, for exploratory analysis before a study is scaled.
Studies are organised as user → studies → participants → sessions, with per-study data isolation enforced at the storage layer. By default only derived landmarks and features are stored; raw video stays on the device.
Under the hood
Markerless pose capture, in the browser.
Volitiq captures with MediaPipe Tasks Vision — face-mesh, body-pose and hand-tracking models running client-side on the GPU.[Lugaresi et al., 2019] Markerless pose estimation from ordinary video is the same research direction pioneered by systems like OpenPose,[Cao et al., 2019] now efficient enough to run live in a web page.
Guided recording gives non-expert operators real-time distance and lighting feedback, so a caregiver or teacher can collect usable data without training. A serverless AWS backend handles feature extraction and storage, keeping the running cost low enough for population-scale studies.
What Volitiq does not claim
A research platform, not a diagnostic medical device.
Volitiq began as the autism-focused E-MotionSpec research project and now generalises across populations. It produces research-grade measurements — nothing more, and nothing hidden.
Volitiq is a research platform, not a medical device, and does not provide a diagnostic claim about any condition.
For sub-millimetre precision, marker-based motion capture remains the gold standard. Volitiq optimises for accessibility at research-useful accuracy.
The platform surfaces features and statistics. Any predictive model is one a research team supplies and validates itself.
Sources
References
External references are peer-reviewed. The lab's own manuscripts on this work are listed by status on the science page and the Publications page.
- Fournier, K.A., Hass, C.J., Naik, S.K., Lodha, N., & Cauraugh, J.H. (2010). Motor Coordination in Autism Spectrum Disorders: A Synthesis and Meta-Analysis. Journal of Autism and Developmental Disorders 40(10), 1227–1240. doi:10.1007/s10803-010-0981-3
- Torres, E.B., Brincker, M., Isenhower, R.W., Yanovich, P., Stigler, K.A., Nurnberger, J.I., et al. (2013). Autism: The Micro-Movement Perspective. Frontiers in Integrative Neuroscience 7, 32. doi:10.3389/fnint.2013.00032
- Wolpert, D.M., Diedrichsen, J., & Flanagan, J.R. (2011). Principles of Sensorimotor Learning. Nature Reviews Neuroscience 12(12), 739–751. doi:10.1038/nrn3112
- Balasubramanian, S., Melendez-Calderon, A., Roby-Brami, A., & Burdet, E. (2015). On the Analysis of Movement Smoothness. Journal of NeuroEngineering and Rehabilitation 12, 112. doi:10.1186/s12984-015-0090-9
- Lugaresi, C., Tang, J., Nash, H., McClanahan, C., Uboweja, E., Hays, M., et al. (2019). MediaPipe: A Framework for Building Perception Pipelines. arXiv:1906.08172
- Cao, Z., Hidalgo, G., Simon, T., Wei, S.-E., & Sheikh, Y. (2019). OpenPose: Realtime Multi-Person 2D Pose Estimation Using Part Affinity Fields. IEEE Transactions on Pattern Analysis and Machine Intelligence 43(1), 172–186. doi:10.1109/TPAMI.2019.2929257
Try Volitiq, or bring it to your cohort.
Researchers can launch the app and capture a session in minutes. Labs and clinics interested in deploying Volitiq across a study cohort can reach the team through the collaboration page.