Abstract
In this paper, an ensemble gentle boost decision tree classification algorithm is trained to classify handwashing from similar activities such as applying lotion to hands. Data is collected using a 3-axis accelerometer and gyroscope worn on the wrist. First, the data collection procedure is described. Then, feature identification is discussed. Once the feature matrix was created, the MATLAB classification learner app was used to classify the data based on the identified features. The overall classification rate achieved was 91.6% using an optimized boosted ensemble classifier. Clinical Relevance- The spreading of germs could be prevented by simple activities such as proper handwashing and wearing masks during the pandemic. This research shows that wearable sensors with machine learning algorithms can alert the users and guide users to wash their hands properly.