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.
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Amphibious robotics
Computational Neuroscience
Dynamical systems
Human-exoskeleton dynamics and control
Humanoid robotics
Miscellaneous
Mobile robotics
Modular robotics
Neuro-muscular modelling
Quadruped robotics
Miscellaneous
| 765 – Validity and Reliability of IMU-Based Jump Test Analysis |
| Category: | master project (full-time) | |
| Keywords: | Data Processing, Motion Capture, Programming, Python, Statistical analysis | |
| Type: | 10% theory, 90% software | |
| Responsible: | (MED 0 1016, phone: 32468) | |
| Description: | Optizone has created a cutting-edge suite of algorithms that estimate athletes’ fitness levels through widely recognized performance tests such as the drop jump, squat jump, repetitive jump, hop test, and velocity-based training. These algorithms have the potential to transform athletic monitoring by providing fast, data-driven insights into performance and readiness. This project focuses on putting these algorithms to the test. Their accuracy and consistency will be rigorously evaluated against a gold-standard motion analysis system in a controlled laboratory setting. Using a structured protocol, athlete performance data will be collected, preprocessed, and subjected to in-depth statistical analysis to determine both the reliability (how consistent the results are) and validity (how well the algorithms reflect true performance). By bridging advanced algorithm development with scientific validation, this study aims to strengthen confidence in Optizone’s technology and lay the foundation for smarter, evidence-based training and injury-prevention strategies. Jump Tests:
Last edited: 29/08/2025 | |
| 763 – Workload Estimation and Action Classification in Basketball Using IMU Sensors |
| 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:
Last edited: 29/08/2025 | |
| 762 – Multimodal sensor fusion for enhanced biomechanical profiling in football: integrating imu and video data from vertical jump tests |
| 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 | |
| 761 – Developing an IMU-based algorithm to quantify the workload of soccer goalkeepers |
| Category: | master project (full-time) | |
| Keywords: | Motion Capture, Programming, Python, sensor | |
| Type: | 20% theory, 80% software | |
| Responsible: | (MED 0 1016, phone: 32468) | |
| Description: | Workload monitoring is a fundamental component in designing and optimizing training sessions for athletes. In football, several established methods exist to assess the workload during training and matches—particularly for outfield players. However, these methods often fall short when applied to goalkeepers, whose movements and physical demands differ significantly. As a result, there is currently no widely accepted or accurate approach for quantifying goalkeeper workload. This project aims to bridge that gap by developing a reliable method for monitoring and estimating the workload of football goalkeepers. Data will be collected during structured goalkeeper training sessions using a combination of video recordings and Inertial Measurement Unit (IMU) sensors, following a carefully designed protocol. The dataset will capture key movement patterns specific to goalkeeping, such as jumping, diving, lateral shuffling, and rapid direction changes. Using this data, the project will involve the development of an algorithm capable of analysing these movements and estimating the overall workload of a session. The algorithm will classify and quantify various types of activities, providing objective metrics that can inform training design, load management, and performance evaluation tailored specifically to goalkeepers. * Data collection is a part of project Last edited: 29/08/2025 | |
4 projects found.