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ML, Human Motion & Social Behavior

Research – Machine Learning Social Behavior

Modeling physiological signals and human motion with machine learning and time-series methods.

Human motion, modeled

Physiological signals and motion capture analyzed with 1D CNNs, RNNs and Riemannian geometry

Project Details

Client

Scalab/CRISTAL Lab

Sector

Research – Machine Learning Social Behavior

Year

2017–2020

Key Technologies

TensorFlowKerasPythonRMATLAB

Mission

Research Scientist - Machine Learning & Time Series

Machine Learning Social Behavior

Academic research (12 months), Lille, France.

Predictive modeling: building ML models to analyze human physical and physiological behavior (notably using 1D CNN and RNN). Motion capture data processing: signal extraction and transformation for behavioral analysis. Riemannian projections: application of advanced geometric techniques to represent movement dynamics. Analysis in social contexts: identification of behavioral patterns and physical responses in various situations.

Contexte & Environnement

Interdisciplinary project: at the crossroads of behavioral science, AI and social neuroscience. Capture technologies: Motion Capture systems for recording human movement. Scientific collaboration: teamwork with data researchers, psychologists and physiologists.

Team

Interdisciplinary work with data researchers, psychologists and physiologists

Environment

Lille, France – Python, R, MATLAB, TensorFlow/Keras, motion capture systems

Objectifs Cles

1In-depth understanding: precisely model the dynamics of movement and physiological states.

2Insight generation: produce quantitative analyses of human social interactions and responses.

3Scientific validation: ensure model robustness through experiments and cross-validation.

4Academic contribution: advance knowledge of human interaction through publications and presentations.

Technologies & Infrastructure

PythonRMATLABTensorFlowKerasMotion Capture SystemsNumPyPandasscikit-learnMatplotlibSeaborn

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