Bayesian optimization is a powerful statistical technique that's increasingly being applied to various optimization problems, including hyperparameter tuning for machine learning models. While I couldn't find a specific reference to its use in overground gait analysis using a motion capture system and focusing on time delay and embedding dimension selection, it's a highly plausible application given its advantages.

Why Bayesian Optimization is a Good Fit for Gait Analysis

Potential Benefits of Using Bayesian Optimization in Gait Analysis

• Improved Model Performance: By finding optimal time delay and embedding dimension values, Bayesian optimization can enhance the accuracy and generalization of gait analysis models.
• Reduced Experimentation Time: By efficiently exploring the parameter space, Bayesian optimization can accelerate the development and tuning of gait analysis systems.
• Enhanced Understanding: Bayesian optimization can provide insights into the sensitivity of the model to time delay and embedding dimension, aiding in the interpretation of results.

While I couldn't find a direct reference, the potential benefits and the applicability of Bayesian optimization to gait analysis make it a promising avenue for future research.

If you're interested in exploring this further, I recommend:

• Searching for research papers or conference proceedings related to gait analysis, machine learning, and Bayesian optimization.
• Consulting experts in the field of gait analysis or machine learning.
• Experimenting with Bayesian optimization tools and libraries (e.g., Hyperopt, Optuna) to see how they can be applied to your specific gait analysis problem.