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 hy- giene 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 hand- washing routines according to recommendations provided by the World Health Organization (WHO). We evaluated WristWash 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.

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