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 Machine learning 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
Human-exoskeleton dynamics and control
| 735 – Hip exoskeleton to assist walking - multiple projects |
| Category: | semester project, master project (full-time), bachelor semester project, internship | |
| Keywords: | Bio-inspiration, C, C++, Communication, Compliance, Control, Data Processing, Dynamics Model, Electronics, Experiments, Inverse Dynamics, Kinematics Model, Learning, Locomotion, Machine learning, Online Optimization, Optimization, Programming, Python, Robotics, Treadmill | |
| Type: | 30% theory, 35% hardware, 35% software | |
| Responsible: | (MED 3 1015, phone: 31153) | |
| Description: | Exoskeletons have experienced an unprecedented growth in recent years and hip-targeting active devices have demonstrated their potential in assisting walking activities. Portable exoskeletons are designed to provide assistive torques while taking off the added weight, with the overall goal of increasing the endurance, reducing the energetic expenditure and increase the performance during walking. The design of exoskeletons involves the development of the sensing, the actuation, the control, and the human-robot interface. In our lab, a hip-joint active hip orthosis (“eWalk”) has been prototyped and tested in recent years. Currently, multiple projects are available to address open research questions. Does the exoskeleton reduce the effort while walking? How can we model human-exoskeleton interaction? How can we design effective controls? How can we optimize the interfaces and the control? Which movements can we assist with exoskeletons? To address these challenges, the field necessitates knowledge in biology, mechanics, electronics, physiology, informatics (programming, learning algorithms), and human-robot interaction. If you are interested in collaborating in one of these topics, please send an email to giulia.ramella@epfl.ch with (1) your CV+transcripts, (2) your previous experiences that could be relevant to the project, and (3) what interests you the most about this research topic (to be discussed during the interview). Last edited: 21/07/2025 | |
Miscellaneous
| 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 | |
3 projects found.