Detection of social connectedness in everyday life via multimodal lifelogging data

• First study to identify physiological correlates of social disconnection in the real-world. • Detected mood-related general social connectedness using wearable devices. • Developed a social density estimation model of people within a 5m vicinity of participants. • Detected social connectedness to people nearby. • Classification models detected correlates of social connection using physiological features from wearable devices. 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 5m 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.