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A Wearable Wireless Kidney Function Monitoring System For BioMedical Applications

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Short Description

Patients in acute care settings often receive urinary catheters. Unfortunately, as with any device inserted into the body, the use of these catheters carries a significant risk of infection. By introducing organisms into the bladder, providing a surface for bacterial adhesion, and causing mucosal irritation, the use of a catheter can result in urinary tract infections (UTI). Indeed, the presence of a urinary catheter is the largest risk factor for bacteriuria. Approximately one-third of urinary catheters are applied not in the context of enabling the release of urine, but instead, are used to monitor kidney performance by measuring the urine release into the bladder.

In this project, we aim to develop an alternative, non-invasive method to measure kidney performance via an external sensor system to avoid the use of urinary catheters for this subgroup of patients, thereby eliminating the risk of infections and improving patient comfort. The sensor principle is based on local impedance measurements associated with the filling of the bladder and changes of volume and conductivity. These bioimpedance measurements are based on the injection of a small alternating electric current into a biological tissue through two electrodes and the corresponding voltage drop is measured across another pair of electrodes. Eventually, the sensor will be incorporated into a form factor that can be comfortably worn by patients during their hospitalization, continuously monitoring their kidney performance

The final goal of the project is to design a low power wearable device that includes the sensor and it is able to perform signal processing and send data in a wireless manner. Thus, the candidate will work with micro-controllers, sensors, wireless communication at firmware level as well as data analysis tools and training tools on the PC/cloud. The hardware and software load of the thesis will be balanced according to the skills and preferences of the candidate students when the details task description will be provided before the student project will start. In the field measurements of the system will be performed from the students as an important activity in order to evaluate power consumption, reliability, functionality, classification accuracy, and energy efficiency and to further optimize the system. The work includes the modeling and design of a suited impedance sensor, its incorporation into flexible electronics, and acquisition and analysis of data from patients at the University Hospital Zurich.

Depending on the applicant's profile and project type, his tasks may involve some of the following:

  • lab. testing/characterization of sensors and embedded systems: verification of the prototype's characteristics w.r. design specification (simulations), measuring power-consumption, and assessing detection performance in lab. conditions
  • High-level software programming, signal processing, machine learning, wireless communication
  • programming the circuit for a specific application, field testing, data acquisition
  • Machine Learning for microcontrollers.
  • PCB design to build a working prototype which includes all the subsystems

Suggested references:

  • [1] J Microw Power. 1983 Sep;18(3):305-9.:
  • [2] Li, Rihui, et al. "Preliminary study of assessing bladder urinary volume using electrical impedance tomography." Journal of Medical and Biological Engineering 36.1 (2016): 71-79.
  • [3] Li, Rihui, Jinwu Gao, Hongbin Wang, and Qing Jiang. "Design of a noninvasive bladder urinary volume monitoring system based on bio-impedance." Engineering 5, no. 10 (2013): 321.

Figure Source: ref [3] & “Lilium® α-200 from Otsuka.

Status: Available

  • Looking for Semester and Master Project Students
Supervisors: Michele Magno; Prof. Simone Schürle (RBSL-ETH), Prof. Dr. med. Hugo Sax (UZH)


(not all need to be met by the single candidate)

  • Experience using the laboratory instrumentation - signal generators, oscilloscopes, DAQ cards, Matlab etc.
  • analog electronics and signal conditioning with operational amplifiers: amplifiers, filters, integrators etc.
  • knowledge of microcontroller programming and PC programming (C/C++, preferably microcontroller with Bluetooth Low Energy but it is not mandatory)
  • basic knowledge on signal processing and machine learning is a plus.
  • plus is knowledge on printed circuit board (PCB) using Altium.


35% Theory
45% Implementation
20% Testing

IIS Professor

Luca Benini

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Detailed Task Description


Practical Details