The intricate connection between daily behaviours and health necessitates robust behaviour monitoring, particularly with the advent of IoT systems. This study introduces an innovative approach, exploiting the synergy of information from various IoT sources, to assess the alignment of behaviour routines with health guidelines. We grouped routines based on guideline compliance and used a clustering method to identify similarities in behaviours and key characteristics within each cluster. Applied to an elderly care case study, our approach unveils patterns leading to physical inactivity by categorising days based on recommended daily steps. Utilising data from wristbands, smartphones, and ambient sensors, the study provides insights not achievable with single-source data. Visualisation in a calendar view aids health experts in understanding patient behaviours, enabling precise interventions. Notably, the approach facilitates early detection of behaviour changes during events like COVID-19 and Ramadan, available in our dataset. This work signifies a promising path for behavioural analysis and discovering variations to empower smart healthcare, offering insights into patient health, personalised interventions, and healthier routines through continuous IoT-driven data analysis.

翻译：日常行为与健康之间的复杂联系需要稳健的行为监测，尤其是在物联网系统兴起的背景下。本研究提出了一种创新方法，利用来自多个物联网源的信息协同效应，评估行为习惯与健康指南的一致性。我们根据指南依从性对行为习惯进行分组，并采用聚类方法识别各聚类内行为的相似性及关键特征。将该方法应用于老年人护理案例研究，通过基于推荐每日步数对天数进行分类，揭示了导致缺乏体力活动的模式。利用来自腕带、智能手机和环境传感器的数据，本研究提供了单一数据源无法获得的见解。通过日历视图的可视化，帮助健康专家理解患者行为，从而实现精准干预。值得注意的是，该方法能够在我们数据集中的COVID-19和斋月等事件期间，早期检测到行为变化。这项工作为行为分析及发现变化以赋能智能医疗开辟了有前景的路径，通过持续的物联网驱动数据分析，为患者健康、个性化干预和更健康的生活习惯提供了深入洞察。