Abstract:Adaptive biomechanical systems may show similar observable gait performance while differing in latent organization and longitudinal behavior. This study examines whether an observed longitudinal transformation of gait organization can be approximated within a predictive latent-space framework, without claiming clinical prediction or causal occlusal effects.
Using an exploratory single-subject design in a Parkinsonian participant, gait was recorded with instrumented insoles during two sessions separated by eleven weeks. Six occlusal observational probes were tested: natural occlusion, open-mouth disengagement, strong clenching, two vertical-dimension increases in centric relation, and one vertical-dimension increase with mandibular protrusion. Principal Component Analysis was used to construct a PC1--PC2 latent representation. A simplified supervised machine-learning model, implemented as a feed-forward neural network, was trained to approximate the observed M1--M2 transformation.
The primary analysis focused on the three centric-relation conditions and tested whether the displacement hierarchy could be reproduced. The model preserved the ordering OC3 < ONL < OC2.5. The extended six-probe analysis also preserved the global structure of the exploratory displacement pattern, with OC3 and OC3P closely grouped and the highest displacements associated with OC2.5 and open-mouth disengagement. Held-out M2 and leave-condition-out analyses showed condition-dependent approximation variability.
These findings do not establish generalizable prediction, therapeutic superiority, causal occlusal effects, or clinical viability forecasting. They support only the restricted conclusion that observed longitudinal latent transformations can be internally approximated within this single-subject dataset, providing a methodological bridge toward future multi-subject predictive viability models.
| Comments: | 31 pages, 1 figure, 9 tables. Exploratory single-subject study combining gait analysis, occlusal observational probes, PCA-based latent-space modeling, and supervised predictive approximation |
| Subjects: | Machine Learning (cs.LG); Neurons and Cognition (q-bio.NC) |
| Cite as: | arXiv:2605.15862 [cs.LG] |
| (or arXiv:2605.15862v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2605.15862 arXiv-issued DOI via DataCite (pending registration) |
From: Jacques Raynal [view email]
[v1]
Fri, 15 May 2026 11:23:44 UTC (982 KB)
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