Loneliness Forecasting Using Multi-modal Wearable and Mobile Sensing in Everyday Settings

TL;DR

This study demonstrates that personalized machine learning models using wearable and mobile sensing data can accurately forecast loneliness levels seven days in advance, aiding early intervention efforts.

Contribution

It introduces a novel approach combining wearable devices and smartphones with personalized models to predict loneliness, which was not extensively explored before.

Findings

Achieved 0.82 accuracy in loneliness forecasting

Utilized physiological and behavioral data for prediction

Enhanced model interpretability with Shapley values

Abstract

The adverse effects of loneliness on both physical and mental well-being are profound. Although previous research has utilized mobile sensing techniques to detect mental health issues, few studies have utilized state-of-the-art wearable devices to forecast loneliness and estimate the physiological manifestations of loneliness and its predictive nature. The primary objective of this study is to examine the feasibility of forecasting loneliness by employing wearable devices, such as smart rings and watches, to monitor early physiological indicators of loneliness. Furthermore, smartphones are employed to capture initial behavioral signs of loneliness. To accomplish this, we employed personalized machine learning techniques, leveraging a comprehensive dataset comprising physiological and behavioral information obtained during our study involving the monitoring of college students. Through the development of personalized models, we achieved a notable accuracy of 0.82 and an F-1 score of 0.82 in forecasting loneliness levels seven days in advance. Additionally, the application of Shapley values facilitated model explainability. The wealth of data provided by this study, coupled with the forecasting methodology employed, possesses the potential to augment interventions and facilitate the early identification of loneliness within populations at risk.