Detection of social connectedness in everyday life via multimodal lifelogging data

Dobbins, C, Fairclough, S ORCID: 0000-0002-7850-5688, Haslam, C, Haslam, SA and Bentley, S (2026) Detection of social connectedness in everyday life via multimodal lifelogging data. International Journal of Human Computer Studies, 209. ISSN 1071-5819

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Abstract

Loneliness, low mood, and social disconnection can have damaging effects on physical and mental health. Detecting these emotions in the context of everyday life is important as these psychological states can manifest differently outside the laboratory. Lifelogging and quantified self technologies, including wearable devices, offer an approach to continuously monitor physiological signals in the real-world. However, little work has been undertaken with these devices to detect social connection in everyday life. This paper presents a study that leveraged machine learning to infer mood and social connectedness in everyday life using multimodal lifelogging data collected via a wrist-worn wearable device. Fifty participants were supplied with a wearable device and smartphone that collected physiological and subjective data across two consecutive weekdays as they went about their daily lives. The analysis examined physiological correlates between a person's psychological perceptions of general connectedness, as well as feelings of in-the-moment social connection to people within an estimated 5 m vicinity and mood at specific timepoints using four machine learning classification models – k-Nearest Neighbour, Random Forest, Support Vector Machine and Naïve Bayes. Results demonstrated that Random Forest obtained the highest accuracy of 0.8 – 0.84 for the binary detection of mood and social connection in everyday life.

Item Type:	Article
Uncontrolled Keywords:	46 Information and Computing Sciences; 4608 Human-Centred Computing; Networking and Information Technology R&D (NITRD); Behavioral and Social Science; Mental Health; Bioengineering; Machine Learning and Artificial Intelligence; Clinical Research; 3 Good Health and Well Being; 0806 Information Systems; 1702 Cognitive Sciences; Human Factors; 46 Information and computing sciences
Subjects:	B Philosophy. Psychology. Religion > BF Psychology Q Science > QA Mathematics > QA76 Computer software
Divisions:	Psychology (from Sep 2019)
Publisher:	Elsevier
Date of acceptance:	19 January 2026
Date of first compliant Open Access:	23 March 2026
Date Deposited:	23 Mar 2026 14:23
Last Modified:	23 Mar 2026 14:23
DOI or ID number:	10.1016/j.ijhcs.2026.103749
URI:	https://researchonline.ljmu.ac.uk/id/eprint/28271

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