Pierre Slangen

Jacques Raynal, Pierre Slangen, Elsa Raynal et al.

Observable performance is commonly used to characterize biological systems. In adaptive systems, however, similar performances may arise from distinct organizations, and configurations that appear comparable at a given time may follow different longitudinal trajectories. This limitation motivates a methodological framework for moving beyond performance-based interpretation without assuming a complete mechanistic model in advance. This article proposes a bootstrap framework for latent-space representation learning in adaptive biological systems. Here, bootstrap is used in a methodological and epistemological sense: new analytical levels are introduced when the preceding representation becomes insufficient to account for observed adaptive dynamics. The framework is organized around five levels: observable performance, dynamic organization, latent organization, longitudinal viability, and internal predictive approximation. The framework is illustrated by three previously reported gait--occlusion studies, used here only as a methodological case sequence and not as new experimental evidence. The article formalizes how performance analysis led to latent organization, how static latent organization led to longitudinal viability, and how observed viability led to internal predictive approximation. The contribution is not a new learning algorithm, clinical protocol, or dataset, but a bootstrap framework for latent-space representation learning describing how increasingly informative representations can emerge from observational insufficiencies in adaptive biological data.

Jacques Raynal, Pierre Slangen, Elsa Raynal et al.

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.

Jacques Raynal, Pierre Slangen, Jacques Margerit

In biomechanical systems, observable performance is often used as a proxy for underlying system organization. However, this assumption implicitly presumes a correspondence between output metrics and internal system states that may not hold in adaptive systems. In this study, the vertical dimension of occlusion (VDO) is considered as a constraint applied to an adaptive neuromechanical system, enabling the exploration of system-level responses under controlled variations. A single-case design in a patient with Parkinson's disease allows an intra-individual analysis across repeated conditions.The analysis is structured across three complementary levels: (i) aggregated linear metrics describing observable performance, (ii) a dynamical systems framework describing temporal organization in state space, and (iii) a latent space representation obtained through unsupervised embedding. The results show that conditions with comparable observable performance may correspond to different organizations in both state space and latent space representations. This dissociation highlights a limitation of aggregated metrics and suggests that similar outputs may arise from non-equivalent system states. A fourth level is proposed as a purely conceptual extension describing potential relationships between system states. This level is not implemented and is not derived from experimental data. These observations are strictly exploratory and non-causal. The proposed framework does not establish mechanistic, predictive, or directional relationships, but provides a structured approach for analyzing constraint-driven systems across multiple levels of representation.

Yann Desmarais, Denis Mottet, Pierre Slangen et al.

Human pose estimation is a very active research field, stimulated by its important applications in robotics, entertainment or health and sports sciences, among others. Advances in convolutional networks triggered noticeable improvements in 2D pose estimation, leading modern 3D markerless motion capture techniques to an average error per joint of 20 mm. However, with the proliferation of methods, it is becoming increasingly difficult to make an informed choice. Here, we review the leading human pose estimation methods of the past five years, focusing on metrics, benchmarks and method structures. We propose a taxonomy based on accuracy, speed and robustness that we use to classify de methods and derive directions for future research.