Project Database

This page contains the database of possible research projects for master and bachelor students in the Biorobotics Laboratory (BioRob). Visiting students are also welcome to join BioRob, but it should be noted that no funding is offered for those projects (see https://biorob.epfl.ch/students/ for instructions). To enroll for a project, please directly contact one of the assistants (directly in his/her office, by phone or by mail). Spontaneous propositions for projects are also welcome, if they are related to the research topics of BioRob, see the BioRob Research pages and the results of previous student projects.

Search filter: only projects matching the keyword Sensor Fusion are shown here. Remove filter

Amphibious robotics
Computational Neuroscience
Dynamical systems
Human-exoskeleton dynamics and control
Humanoid robotics
Miscellaneous
Mobile robotics
Modular robotics
Neuro-muscular modelling
Quadruped robotics


Miscellaneous

763 – Workload Estimation and Action Classification in Basketball Using IMU Sensors
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Category:master project (full-time)
Keywords:Data Processing, Machine learning, Motion Capture, Programming, Python, Sensor Fusion
Type:10% theory, 90% software
Responsible: (MED 0 1016, phone: 32468)
Description:n modern basketball, accurately monitoring player workload and identifying specific movement patterns are critical for optimizing performance, reducing injury risk, and tailoring individualized training programs. However, many existing workload assessment tools are not fine-tuned to capture the complex and explosive actions typical in basketball. This project aims to develop a sensor-based system that can estimate physical workload and classify basketball-specific movements using only Inertial Measurement Unit (IMU) sensors. Data will be collected from athletes during structured training sessions, with a focus on high-intensity basketball actions such as rebounds, layups, jump shots, sprints, direction changes, and defensive movements. The primary objective is to create algorithms capable of:
  • 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
762 – Multimodal sensor fusion for enhanced biomechanical profiling in football: integrating imu and video data from vertical jump tests
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Category:master project (full-time)
Keywords:Data Processing, Image Processing, Machine learning, Motion Capture, Programming, Python, Sensor Fusion
Type:100% software
Responsible: (MED 0 1016, phone: 32468)
Description:Raw video shows the motion. IMUs reveal the accelerations, orientation. Combined, they unlock new biomechanical precision. This project focuses on developing a sensor fusion framework that synchronizes video recordings and inertial measurement unit (IMU) data to compute enhanced biomechanical metrics from jump tests (bilateral and unilateral CMJ, drop jump). The core aim is to overcome the limitations of each modality alone, combining the spatial richness of video with the temporal and acceleration precision of IMUs. You have access to a dataset consist of 25 players collected inside the lab with an infrared motion tracker system. Traditional biomechanical analysis in sport often relies on expensive lab equipment and manual video inspection. Your work could lay the foundation for next-generation performance monitoring systems that are low-cost, field-deployable, and data-rich.

Last edited: 29/08/2025

2 projects found.