Build a solution that helps detect urinary incontinence in nursing homes by leveraging machine learning and digital sensor data. In this 8-week challenge, a collaborative team of 50 AI engineers from all around the world collaborated. 

This challenge requires experience in Data Analysis and Machine Learning.

The problem

Without monitoring technology in nursing homes, the staff cannot determine the status of the seniors and whether they have been lying down with a wet diaper for hours. Consequently, this issue impacts their quality of life while placing them at significant risk for skin breakdown and life-threatening urinary tract infections. Impact-driven startup Driq Health´s non-contact sensor monitoring can solve this by allowing real-time notifications for timely diaper changes.

The project goals

The goal is to develop a more accurate wet vs. dry state determination by examining digital sensor data from passive RFID moisture sensing tags logged via IoT onto the cloud. In addition to moisture state code, the AI solution would have access to diaper temperature data as well as the signal strength of the sensor. Both signal strength and temperature of the diaper will change during an incontinence event making them a good target for the AI-enabled algorithm.

The Data

The partner for this project will provide the data.

Why join? The uniqueness of Omdena AI Challenges

A collaborative experience you never had in your working life! For the next eight weeks, you will not only build AI solutions to make a real-world impact but also go through an entire data science project lifecycle. This covers problem scoping, data collection, and preparation, as well as modeling for deployment.

And the best part is that you will join a global and collaborative team of changemakers. Omdena AI Challenges are not a competition or hackathon but a real-world project that will take your experience of what is possible through collaboration to a new level.

Find more information on how an Omdena project works