Personal tools

Wearable Ultrasound for Artery monitoring

From iis-projects

Revision as of 22:34, 14 March 2022 by Cosandre (talk | contribs) (Created page with "thumb|right|600px == Short Description == Ultrasound (US) imaging is a non-invasive imaging technique that provides visible inform...")
(diff) ← Older revision | Latest revision (diff) | Newer revision → (diff)
Jump to: navigation, search
Ultrasound artery monitoring.jpg

Short Description

Ultrasound (US) imaging is a non-invasive imaging technique that provides visible information on the structure of musculoskeletal tissues, organs, and vascular system. Recent research trends include the development of wearable US probes, with minimal power consumption and reduced count of piezoelectric elements. In fact, meaningful physiological information can be derived also from single-element transducers [1]. Within this framework, at IIS we have recently developed an ultra-low power wearable US probe, operating with a single US channel and consuming less than 20mW [2]. The scope of this project is to showcase the application of such probe to different physiological problems. As a main target, we consider the monitoring of artery walls [1]. The project will include the design of experiments, microcontroller implementation of imaging strategies, data collection, subsequent data analyses, and implementation of the feature extraction directly on the probe.

Goal & Tasks

In this project, you will work on the application of a novel wearable US probe [2], targeting the monitoring of movements of artery walls. The main tasks are:

  • design of experiments (taking inspiration from [1])
  • implementing the needed control mechanisms in the firmware of the probe
  • data analyses
  • implementing automatic feature extraction directly on-the-probe


  • [1] Continuous Artery Monitoring Using a Flexible and Wearable Single-Element Ultrasonic Sensor
  • [2] Ultra low power wearable ultrasound probe


  • C, Python
  • Microcontrollers

Status: Available

Looking for Interested Students
Supervision: Sergei Vostrikov, Andrea Cossettini


10% Literature Study
30% Microcontrollers
60% Data collection and analysis


Luca Benini

Practical Details

↑ top