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.

翻译：孤独感是一个日益严重的健康问题，因为长期感到孤独可能导致抑郁及其他相关心理健康问题。利用智能手机和可穿戴传感器数据捕捉日常行为模式的被动感知方法，为早期检测孤独感提供了有前景的途径。鉴于孤独感的主观特性以及人们日常活动的差异性，以往基于机器学习模型的检测方法在有效识别孤独感方面常面临挑战。本文提出了一种方法论上的创新方法，尤其是开发了一个随时间演化、适应新数据并提供实时检测的孤独感检测系统。本研究采用Globem数据集，这是一个涵盖大学生10周被动感知数据的综合数据集。我们方法的基础是通过增量聚类方法持续识别并优化学生间的相似行为群组。随着新数据的加入，模型会基于变化的行为模式不断改进。与此并行，我们创建并更新分类模型，以在演化的学生行为群组中检测孤独感。当观察到学生的独特行为模式时，会建立专门分类模型。对于孤独感的预测，采用通用模型与专门模型的协同机制，将每次预测视为一次投票。研究结果表明，基于群组的孤独感检测模型相比通用模型展现出更优性能，这凸显了针对特定行为模式定制个性化方法的必要性。这些成果为未来研究开辟了新方向，强调开发精细调谐的个体化心理健康干预措施。