Fleury Anthony, Noury Norbert, Vacher Michel
Laboratory TIMC-IMAG, UMR CNRS/UJF 5525, team AFIRM, Faculté de Médecine de Grenoble, bâtiment Jean Roget, 38706 La Tronche, France.
Annu Int Conf IEEE Eng Med Biol Soc. 2009;2009:6099-102. doi: 10.1109/IEMBS.2009.5334931.
By 2050, about a third of the French population will be over 65. To face this modification of the population, the current studies of our laboratory focus on the monitoring of elderly people at home. This aims at detect, as early as possible, a loss of autonomy by objectivizing criterions such as the international ADL or the French AGGIR scales implementing automatic classification of the different Activities of Daily Living. A Health Smart Home is used to achieve this goal. This flat includes different sensors. The data from the various sensors were used to classify each temporal frame into one of the activities of daily living that has been previously learnt (seven activities: hygiene, toilets, eating, resting, sleeping, communication and dressing/undressing). This is done using Support Vector Machines. We performed an experimentation with 13 young and healthy subjects to learn the model of activities and then we tested the classification algorithm (cross-validation) on real data.
到2050年，约三分之一的法国人口将超过65岁。为应对这一人口结构变化，我们实验室目前的研究聚焦于对居家老年人的监测。其目的是通过将诸如国际日常生活活动能力量表（ADL）或法国AGGIR量表等客观标准进行量化，尽早发现老年人自理能力的丧失，这些标准可对不同的日常生活活动进行自动分类。为此采用了一个健康智能家居。这套住房配备了不同的传感器。来自各种传感器的数据被用于将每个时间帧分类为先前已习得的日常生活活动之一（七种活动：卫生清洁、上厕所、进食、休息、睡觉、交流以及穿衣/脱衣）。这一过程通过支持向量机来完成。我们对13名年轻健康的受试者进行了实验以学习活动模型，然后在真实数据上测试了分类算法（交叉验证）。
