A State-of-the-Art Review of Computational Models for Analyzing Longitudinal Wearable Sensor Data in Healthcare

TL;DR

This paper reviews computational models for analyzing long-term wearable sensor data in healthcare, highlighting challenges, current methods, and future directions to enhance predictive and personalized medicine.

Contribution

It provides a comprehensive overview of three key models—routines, rhythms, and stability metrics—for understanding longitudinal wearable data in healthcare.

Findings

Identifies challenges in processing multi-granularity temporal data.

Discusses limitations of current models and analysis techniques.

Suggests future research directions for improved modeling.

Abstract

Wearable devices are increasingly used as tools for biomedical research, as the continuous stream of behavioral and physiological data they collect can provide insights about our health in everyday contexts. Long-term tracking, defined in the timescale of months of year, can provide insights of patterns and changes as indicators of health changes. These insights can make medicine and healthcare more predictive, preventive, personalized, and participative (The 4P's). However, the challenges in modeling, understanding and processing longitudinal data are a significant barrier to their adoption in research studies and clinical settings. In this paper, we review and discuss three models used to make sense of longitudinal data: routines, rhythms and stability metrics. We present the challenges associated with the processing and analysis of longitudinal wearable sensor data, with a special focus on how to handle the different temporal dynamics at various granularities. We then discuss current limitations and identify directions for future work. This review is essential to the advancement of computational modeling and analysis of longitudinal sensor data for pervasive healthcare.