  Loneliness is a growing health concern as it can lead to depression and other
associated mental health problems for people who experience feelings of
loneliness over prolonged periods of time. Utilizing passive sensing methods
that use smartphone and wearable sensor data to capture daily behavioural
patterns offers a promising approach for the early detection of loneliness.
Given the subjective nature of loneliness and people's varying daily routines,
past detection approaches using machine learning models often face challenges
with effectively detecting loneliness. This paper proposes a methodologically
novel approach, particularly developing a loneliness detection system that
evolves over time, adapts to new data, and provides real-time detection. Our
study utilized the Globem dataset, a comprehensive collection of passive
sensing data acquired over 10 weeks from university students. The base of our
approach is the continuous identification and refinement of similar behavioural
groups among students using an incremental clustering method. As we add new
data, the model improves based on changing behavioural patterns. Parallel to
this, we create and update classification models to detect loneliness among the
evolving behavioural groups of students. When unique behavioural patterns are
observed among student data, specialized classification models have been
created. For predictions of loneliness, a collaborative effort between the
generalized and specialized models is employed, treating each prediction as a
vote. This study's findings reveal that group-based loneliness detection models
exhibit superior performance compared to generic models, underscoring the
necessity for more personalized approaches tailored to specific behavioural
patterns. These results pave the way for future research, emphasizing the
development of finely-tuned, individualized mental health interventions.
