Ontology-based Deep Learning for Human Behavior Prediction with Explanations in Health Social Networks.

Human behavior modeling is a key component in application domains such as healthcare and social behavior research. In addition to accurate prediction, having the capacity to understand the roles of human behavior determinants and to provide explanations for the predicted behaviors is also important. Having this capacity increases trust in the systems and the likelihood that the systems actually will be adopted, thus driving engagement and loyalty.

A deep learning approach for human behavior prediction with explanations in health social networks: social restricted Boltzmann machine (SRBM).

Human behavior modeling is a key component in application domains such as healthcare and social behavior research. In addition to accurate prediction, having the capacity to understand the roles of human behavior determinants and to provide explanations for the predicted behaviors is also important. Having this capacity increases trust in the systems and the likelihood that the systems will be actually adopted, thus driving engagement and loyalty.

Dynamic Socialized Gaussian Process Models for Human Behavior Prediction in a Health Social Network.

Modeling and predicting human behaviors, such as the level and intensity of physical activity, is a key to preventing the cascade of obesity and helping spread healthy behaviors in a social network. In our conference paper, we have developed a social influence model, named Socialized Gaussian Process (SGP), for socialized human behavior modeling. Instead of explicitly modeling social influence as individuals' behaviors influenced by their friends' previous behaviors, SGP models the dynamic social correlation as the result of social influence.

Topic-Aware Physical Activity Propagation with Temporal Dynamics in a Health Social Network.

Modeling physical activity propagation, such as activity level and intensity, is a key to preventing obesity from cascading through communities, and to helping spread wellness and healthy behavior in a social network. However, there have not been enough scientific and quantitative studies to elucidate how social communication may deliver physical activity interventions. In this work, we introduce a novel model named opic-aware ommunity-level hysical Activity Propagation with emporal Dynamics (TCPT) to analyze physical activity propagation and social influence at different granularities (i.

The impact of an online social network with wireless monitoring devices on physical activity and weight loss.

Background: Online social networks (OSNs) are a new, promising approach for catalyzing health-related behavior change. To date, the empirical evidence on their impact has been limited.

Purpose: Using a randomized trial, we assessed the impact of a health-oriented OSN with accelerometer and scales on participant's physical activity, weight, and clinical indicators.