Mixture Hidden Markov models to detect engagement dynamics of mobile based Health program participants


MHealth interventions relating to physical activity are gaining increasing popularity. However, these mHealth programs provide only limited information regarding overall statistics and visualisation of tracked performance data. Most of these programs consider only cross sectional data in order to group participants depending on engagement levels. However, to provide a truly dynamic personalisd environment what is needed are methods for identifying different engagement trajectories. In this current study, multichannel engagement trajectories for the main module will be considered. The engagement trajectory for each participant will be comprised of two parallel channels, namely the number of weekly logins and step entries. In the literature there are many ways for clustering multichannel sequences using Hidden Markov Models. In the current study the authors have used Mixture Hidden Markov Models.

Aug 14, 14140 1:00 PM — 2:00 PM
Doshisha University, Imadegawa Campus
S. Sandun M. Silva
S. Sandun M. Silva

My research interests include biostatistics, data science and genome-wide association studies (GWAS)