Tesi magistrale internship presso Italian Institute of Technology (IIT) Genova XoLab – Advanced Robotics group

Presso il gruppo XoLab – Advanced Robotics group, IIT Genova, è disponibile una tesi magistrale di 6 mesi su Wearable Robotics tema: Biometric Signals for Fall Prediction.

Mission of the lab
At our lab in collaboration with the Italian National Institute for Insurance against Accidents at Work (INAIL), we are currently developing a wearable device for industrial workers to mitigate the harm caused by falls. In the context of this research, we are implementing real-time algorithms for detecting an ongoing fall and predicting future falls, so an appropriate prevention mechanism can be activated and save the worker. The detection/prediction is made with the use of microelectromechanical systems (MEMS), such as Inertial Measurement Units (IMUs) and biometric sensors. More details are here.


Position description
The student will get hands-on experience using real biometric sensors, conducting experiments with subjects, and will work with an international and interdisciplinary team of experts in various fields. The main task would be identifying psychophysiological effects that can be correlated to the risk of falling (e.g., heat stroke, fainting or high mental load). An example of such cases is the symptoms or signs of syncope, which requires detecting abnormal measurements in various biological functions, such as a sudden drop in blood pressure or a low heartbeat rate.

The internship is a no-salary position.


We have already purchased a variety of sensors such as the BiosignalsPlux sensor hub with EMG, EEG, SpO2, EDA, Temperature, Respiration and HRV sensors; with the capability of buying more if required. This set of biometric sensors is to be used for detecting a broad spectrum of psychophysiological effects that can be correlated to the risk of falling. The successful candidate will have access to the Xolab (Exoskeletons Lab) and several wearable robots and will also have the opportunity to come into contact with other types of robots.  More detail here.

Required skills

  • Temporal availability of at least 6 months (not necessarily full-time all the time).
  • Master’s student in Medical Engineering, Electric Engineering, or related degrees.
  • Background in human physiology and engineering.
  • Basic programming skills in MATLAB or Python.
  • Excellent English communication/writing and team working skills.
  • We don’t care about grades and letters; we are interested in motivation and passion.



  • Background Research: investigate which are the most important signs that can predict or happen during a fall. Identify different causes, which are the signals that indicate these causes, and which sensors could be used to detect the symptoms.
  • Experiment with real biometric sensors and data acquisition systems (with the support of professional engineers).
  • Help in organizing experiments with real subjects wearing a series of biometric sensors, extract and interpret results.
  • Collaborate with other students and experts in various fields.


Start Date
The evaluation of the received applications starts immediately and will continue until the position is filled. Interested students are strongly encouraged to apply early, as the hiring of successful candidates will take place on a first-come-first-served basis.

To Apply
Please send, as a single email:

a) detailed Curriculum Vitae (please include: nationality, English level, scientific publications if any, programming skills, and hobbies).

b) (if possible) a copy of academic records/transcript and most recent diploma.

c) name and contact information of one reference.

d) use “Biometric Signals for Fall Prediction” in the email subject.

Applications submitted in other ways cannot be processed!

The application files should be sent to: