Indoor Smart Tracking of Hospital instrumentation
Infections acquired in hospital settings are currently a significant issue in public health and place a sizeable burden on health care systems. Understanding and monitoring the transmission of infection in a hospital setting offers the insight necessary to predict the spread among individuals and develop effective countermeasures. In cases of acute outbreak, it is crucial to act quickly to identify and remove sources of contamination. One pathway of infection is through medical instrumentation that is shared amongst patients, such as blood pressure cuffs. Tracing the history of all cuffs used in a hospital and correlating this with cases of infection would immediately suggest an individual cuff as a source if its use is linked to the infected patients. Moreover, in case of a single incidence of infection, equipment that has come into contact with the patient can be temporarily put out of service or undergo additional sterilization to prevent an outbreak.
In this project, the student will conduct a pilot study on tracking of hospital instrumentation investigating on several promising radio frequencies technologies, for instance, using radio-frequency identification (RFID) badges for passive detection or active solution such as WiFi, Bluetooth low energy or the novel UWB. Compared to barcodes or other tracking systems, radio frequency solution would allow data to be automatically collected without placing additional demands on hospital staff. The goal of the project is set-up a whole system that includes readers and mobile tags. For example in the case of the RFID, while all instruments will be assigned with a passive RFID tag, active readers will need to be laid out in the hospital, such as connected to patient beds and other relevant stations. After a feasibility study on different technologies the student will model, design and implement the complete solution with the best technology in terms of lifetime and precision,and he will evaluate it in University Hospital Zurich.
Depending on the applicant's profile and project type, his tasks may involve some of the following:
- lab. testing/characterization of RF localization modules 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
-  Isella L, Romano M, Barrat A, Cattuto C, Colizza V, et al. (2011) Close Encounters in a Pediatric Ward: Measuring Face-to-Face Proximity and Mixing
- Patterns with Wearable Sensors. PLoS ONE 6(2): e17144. doi:10.1371/journal.pone.0017144
-  Jeong, Seol Young, Hyeong Gon Jo, and Soon Ju Kang. "Fully distributed monitoring architecture supporting multiple trackees and trackers in indoor mobile asset management application." Sensors 14.3 (2014): 5702-5724.
Figure Source: ref 
- 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..
- knowledge of microcontroller programming and PC programming (C/C++, preferably microcontroller with Bluetooth Low Energy but it is not mandatory)
- basic knowledge or interests on signal processing, wireless communication for localization and machine learning is a plus
- plus is also knowledge on printed circuit board (PCB) using Altium.
- 35% Theory
- 45% Implementation
- 20% Testing