Washing hands is one of the easiest yet most effective ways to prevent spreading illnesses and diseases. However, not adhering to thorough handwashing routines is a substantial problem worldwide. For example, in hospital operations lack of hygiene leads to healthcare associated infections. We present WristWash, a wrist-worn sensing platform that integrates an inertial measurement unit and a Hidden Markov Model-based analysis method that enables automated assessments of handwashing routines according to recommendations provided by the World Health Organization (WHO). We evaluated Wrist-Wash in a case study with 12 participants. WristWash is able to successfully recognize the 13 steps of the WHO handwashing procedure with an average accuracy of 92% with user-dependent models, and with 85% for user-independent modeling. We further explored the system's robustness by conducting another case study with six participants, this time in an unconstrained environment, to test variations in the hand-washing routine and to show the potential for real-world deployments.
We are interested in ubiquitous computing and the research issues involved in building and evaluating ubicomp applications and services that impact our lives. Much of our work is situated in settings of everyday activity, such as the classroom, the office and the home. Our research focuses on several topics including, automated capture and access to live experiences, context-aware computing, applications and services in the home, natural interaction, software architecture, technology policy, security and privacy issues, and technology for individuals with special needs.