Highly Accurate Bathroom Activity Recognition Using Infrared Proximity Sensors

IEEE J Biomed Health Inform. 2020 Aug;24(8):2368-2377. doi: 10.1109/JBHI.2019.2963388. Epub 2019 Dec 31.


Among elderly populations over the world, a high percentage of individuals are affected by physical or mental diseases, greatly influencing their quality of life. As it is a known fact that they wish to remain in their own home for as long as possible, solutions must be designed to detect these diseases automatically, limiting the reliance on human resources. To this end, our team developed a sensors platform based on infrared proximity sensors to accurately recognize basic bathroom activities such as going to the toilet and showering. This article is based on the body of scientific literature which establish evidences that activities relative to corporal hygiene are strongly correlated to health status and can be important signs of the development of eventual disorders. The system is built to be simple, affordable and highly reliable. Our experiments have shown that it can yield an F-Score of 96.94%. Also, the durations collected by our kit are approximately 6 seconds apart from the real ones; those results confirm the reliability of our kit.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Activities of Daily Living / classification*
  • Adult
  • Equipment Design
  • Female
  • Health Status
  • Home Care Services*
  • Humans
  • Infrared Rays
  • Machine Learning
  • Male
  • Pattern Recognition, Automated / methods*
  • Remote Sensing Technology* / instrumentation
  • Remote Sensing Technology* / methods
  • Toilet Facilities*
  • Young Adult