|Dates:||2016 – 2020|
|Researchers:||Igor Kulev and Pearl Pu|
|Keywords:||activities of daily living, machine learning, timeseries analysis, clustering|
While an increase in physical activity levels has been shown to be the most effective and important intervention strategy to improve health in elderly populations, it is notoriously difficult to increase physical activity, mobilization, and rehabilitation among older adults. We propose a lifestyle recommender system to help senior adults with sedentary habits to become more active. In that context methods are developed to cluster existing users based on how a given intervention of behavior change affects their current habits modelled as time series data.