WEARABLE SENSOR-BASED CHRONIC CONDITION SEVERITY ASSESSMENT: AN ADVERSARIAL ATTENTION-BASED DEEP MULTISOURCE MULTITASK LEARNING APPROACH

Abstract

Advancing the quality of healthcare for senior citizens with chronic conditions is of great social relevance. To better manage chronic conditions, objective, convenient, and inexpensive wearable sensor-based information systems (IS) have been increasingly used by researchers and practitioners. However, existing models often focus on a single aspect of chronic conditions and are often “black boxes” with limited interpretability. In this research, we adopt the computational design science paradigm and propose a novel adversarial attention-based deep multisource multitask learning (AADMML) framework. Drawing upon deep learning, multitask learning, multisource learning, attention mechanism, and adversarial learning, AADMML addresses limitations with existing wearable sensor-based chronic condition severity assessment methods. Choosing Parkinson’s disease (PD) as our test case because of its prevalence and societal significance, we conduct benchmark experiments to evaluate AADMML against state-of-the-art models on a large-scale dataset containing thousands of instances. We present three case studies to demonstrate the practical utility and economic benefits of AADMML and by applying it to detect early-stage PD. We discuss how our work is related to the IS knowledge base and its practical implications. This work can contribute to improved life quality for senior citizens and advance IS research in mobile health analytics. © 2022 University of Minnesota. All rights reserved.