Cejudo Ander, Beristain Andoni, Almeida Aitor, Rebescher Kristin, Martín Cristina, Macía Iván
Fundación Vicomtech, Basque Research and Technology Alliance (BRTA), Mikeletegi 57, 20009, Donostia-San Sebastián, Spain.
Faculty of Engineering, University of Deusto, Avda. Universidades, 24, 48007, Bilbao, Spain.
Med Biol Eng Comput. 2025 Jun;63(6):1821-1835. doi: 10.1007/s11517-025-03308-y. Epub 2025 Jan 31.
Smart homes have the potential to enable remote monitoring of the health and well-being of older adults, leading to improved health outcomes and increased independence. However, current approaches only consider a limited set of daily activities and do not combine data from individuals. In this work, we propose the use of deep learning techniques to model behavior at the population level and detect significant deviations (i.e., anomalies) while taking into account the whole set of daily activities (41). We detect and visualize daily routine patterns, train a set of recurrent neural networks for behavior modelling with next-day prediction, and model errors with a normal distribution to identify significant deviations while considering the temporal component. Clustering of daily routines achieves a silhouette score of 0.18 and the best model obtains a mean squared error in next day routine prediction of 4.38%. The mean number of deviated activities for the anomalies in the train and test set are 3.6 and 3.0, respectively, with more than 60% of anomalies involving three or more deviated activities in the test set. The methodology is scalable and can incorporate additional activities into the analysis.
智能家居有潜力实现对老年人健康和福祉的远程监测，从而改善健康状况并提高独立性。然而，目前的方法仅考虑了有限的日常活动集，且未整合个体数据。在这项工作中，我们提出使用深度学习技术在人群层面上对行为进行建模，并在考虑整套日常活动的情况下检测显著偏差（即异常）（41）。我们检测并可视化日常活动模式，训练一组循环神经网络用于行为建模和次日预测，并使用正态分布对误差进行建模，以在考虑时间成分的同时识别显著偏差。日常活动的聚类实现了0.18的轮廓系数，最佳模型在次日活动预测中的均方误差为4.38%。训练集和测试集中异常情况的平均偏离活动数量分别为3.6和3.0，测试集中超过60%的异常情况涉及三个或更多的偏离活动。该方法具有可扩展性，并且可以将其他活动纳入分析。
