Autonomy and adaptability are essential components of ambient intelligence. For example, in smart homes, proactive acting and occupants advising, adapted to current and future contexts of living, are essential to go beyond limitations of previous domotic services. To reach such autonomy and adaptability, ambient systems need to automatically grasp their users’ ambient context. In particular, users’ activities and availabilities for communication are valuable pieces of contextual information that can help such systems to adapt to user needs and behaviours. While significant research work exists on activity recognition in homes, less attention has been given to prediction of future activities, as well as to availability recognition and prediction in general. In this article, we investigate several Dynamic Bayesian Network (DBN) architectures for activity and availability prediction of occupants in homes, including our novel model, called Past SItuations to predict the NExt Situation (PSINES). This predictive architecture utilizes context information, sensor event aggregations, and latent user cognitive states to accurately predict future home situations based on previous situations. We experimentally evaluate PSINES, as well as intermediate DBN architectures, on multiple state-of-the-art datasets, with prediction accuracies of up to 89.52% for activity and 82.08% for availability on the Orange4Home dataset.

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神经网络

自主性和适应性是环境智能的重要组成部分。例如，在智能家居中，主动行动和居住者建议，适应当前和未来的生活环境，对于超越以前的家庭服务限制至关重要。为了达到这种自主性和适应性，环境系统需要自动掌握用户的环境上下文。特别是，用户的活动和通信可用性是有价值的上下文信息，可以帮助此类系统适应用户的需求和行为。虽然在家庭活动识别方面存在大量研究工作，但对未来活动的预测以及一般可用性识别和预测的关注较少。在本文中，我们研究了几种动态贝叶斯网络 (DBN) 架构，用于预测家中居住者的活动和可用性，包括我们的新模型，称为过去情景来预测未来情景 (PSINES)。这种预测架构利用上下文信息、传感器事件聚合和潜在用户认知状态，根据先前的情况准确预测未来的家庭情况。我们在多个最先进的数据集上对 PSINES 以及中间 DBN 架构进行了实验评估，在 Orange4Home 数据集上的活动预测准确度高达 89.52%，可用性预测准确度高达 82.08%。传感器事件聚合和潜在用户认知状态，以根据以前的情况准确预测未来的家庭情况。我们在多个最先进的数据集上对 PSINES 以及中间 DBN 架构进行了实验评估，在 Orange4Home 数据集上的活动预测准确度高达 89.52%，可用性预测准确度高达 82.08%。传感器事件聚合和潜在用户认知状态，以根据以前的情况准确预测未来的家庭情况。我们在多个最先进的数据集上对 PSINES 以及中间 DBN 架构进行了实验评估，在 Orange4Home 数据集上的活动预测准确度高达 89.52%，可用性预测准确度高达 82.08%。