Sponsored by: Medtronic
Project Background and Design Goal
Patients with chronic urinary issues currently struggle with manually logging bathroom usage data. Our team designed and prototyped an automated bathroom system that predicts urinary volume while prioritizing automation, privacy, and simple interfaces. To develop a system that automatically measures urination volume that maintains user privacy.
Design Constraints
- Estimate urine volume with≥80% accuracy
- No cameras or video recording allowed to respect privacy
- Log data only for the patient, not other users
- Include aBLE® 5 radio for data transfer from a microcontroller to a smartphone
- Automatically collect bathroom usage data using a microprocessor
- Software must support smartphones
Design Outcomes
- Experimental setup captures fluid flow audio
- Programmed BLE® capabilities to transfer data from microcontroller to mobile app
- Created a mobile app for neural network data sound to volume processing which converts to a corresponding flow rate to quantify volume
- Developed a neural network with at least 80% accuracy to urine flowrate corresponding volume predictions.
Download the project summary (PDF file).
System Electrical Enclosure Attachment
Members of the Project Team
Student Team:
- Shaakir Banow - Major: Mechanical Engineering
- Simon Shimek - Major: Electrical Engineering
- Cody Kyro - Major: Computer Engineering
- Zoe Thoma - Major: Computer Engineering
Industry Representative: Andrew Fried
Faculty Advisor: Lucas Koerner
Pictured left to right: Shaakir Banow, Simon Shimek, Cody Kyro, Zoe Thoma