Snakes use undulatory swimming to move efficiently through water, and different species show distinct patterns depending on their morphology and environment. With datasets available from live swimming tests and access to an undulatory robot, we can combine data analysis and physical experiments to explore their performance systematically and address biological questions.

Project Description:

The goal of this project is to analyse and replicate the diversity of swimming gaits in snakes using kinematic data from different species and a robot to explore insights into energy consumption. We’ll use reduced-order modelling to extract dominant motion patterns and look for structure in the data, for example, clustering gaits by species or lifestyle. Based on these results, we’ll test a few representative cases on a robotic platform to evaluate how well the robot can replicate biological gaits and how performance (e.g. speed, efficiency) varies with different strategies.

Specific Goals:

• Analyse snake swimming data to extract key gait parameters (e.g. curvature, amplitude, frequency)
• Perform reduced-order modelling to identify dominant gait modes.
• Cluster the gaits by species, morphology, or habitat.
• Select a few representative gaits and implement them on an undulatory robot.
• Run experiments to evaluate robotic performance across gaits.
• Compare biological and robotic results to identify general trends.

• Drop Jump
• Squat Jump
• Velocity-Based Training

• Data Collection: Acquire athlete performance data in a motion analysis lab under standardized conditions.
• Data Preprocessing: Clean, structure, and prepare the dataset for analysis.
• Statistical Analysis: Apply statistical methods to assess Optizone’s algorithms against the gold-standard reference system

• Estimating workload metrics (e.g., jump count, movement intensity, acceleration patterns)
• Classifying basketball actions based on IMU-derived motion signatures.
Video recordings will be used solely to verify and annotate the IMU data, serving as a ground truth for validating the accuracy of the developed classification and workload estimation models. This project will result in a practical and sport-specific tool for coaches, trainers, and sports scientists to monitor performance and manage training loads using compact wearable technology, without relying on complex camera setups or external tracking systems. Data collection is a part of project

Last edited: 29/08/2025