Intervention-Based Clustering

Abstract

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. For example, an intervention may cause some users to increase their level of activeness (they are called responders), some users to do the opposite (non-responders), and some to improve their activeness only temporarily (temporarily responders). In this manner, every user is characterized by a readiness score indicating how likely he/she is to respond to the proposed intervention. When a new user arrives, the system will be able to propose a behavior change adapted to his readiness score similar to a user that the system has already modelled, thus avoiding the risk of suggesting something harmful to him/her.

Keywords

Activities of daily living, machine learning, timeseries analysis, clustering

Year

2016-2020

Researchers

Igor Kulev and Pearl Pu

Sponsor

REACH

Publications

  • Igor Kulev, Pearl Pu and Boi Faltings. Discovering Persuasion Profiles Using Time Series Data. In NIPS Time Series Workshop 2016. Barcelona, Spain, 2016