Petersen Johanna, Larimer Nicole, Kaye Jeffrey A, Pavel Misha, Hayes Tamara L
Department of Biomedical Engineering, OHSU, Portland, OR 97239, USA.
Annu Int Conf IEEE Eng Med Biol Soc. 2012;2012:5850-3. doi: 10.1109/EMBC.2012.6347324.
With the rising age of the population, there is increased need to help elderly maintain their independence. Smart homes, employing passive sensor networks and pervasive computing techniques, enable the unobtrusive assessment of activities and behaviors of the elderly which can be useful for health state assessment and intervention. Due to the multiple health benefits associated with socializing, accurately tracking whether an individual has visitors to their home is one of the more important aspects of elders' behaviors that could be assessed with smart home technology. With this goal, we have developed a preliminary SVM model to identify periods where untagged visitors are present in the home. Using the dwell time, number of sensor firings, and number of transitions between major living spaces (living room, dining room, kitchen and bathroom) as features in the model, and self report from two subjects as ground truth, we were able to accurately detect the presence of visitors in the home with a sensitivity and specificity of 0.90 and 0.89 for subject 1, and of 0.67 and 0.78 for subject 2, respectively. These preliminary data demonstrate the feasibility of detecting visitors with in-home sensor data, but highlight the need for more advanced modeling techniques so the model performs well for all subjects and all types of visitors.
随着人口老龄化程度的不断加深，帮助老年人维持其独立性的需求日益增加。智能家居利用无源传感器网络和普适计算技术，能够对老年人的活动和行为进行不引人注意的评估，这对于健康状况评估和干预很有帮助。由于社交活动对健康有诸多益处，准确追踪个人家中是否有访客是利用智能家居技术可评估的老年人行为中较为重要的方面之一。出于这一目的，我们开发了一个初步的支持向量机模型，用于识别家中有无标签访客的时间段。以停留时间、传感器触发次数以及主要生活空间（客厅、餐厅、厨房和浴室）之间的转换次数作为模型特征，并以两名受试者的自我报告作为基准事实，我们能够准确检测出家中访客的存在，对于受试者1，灵敏度和特异性分别为0.90和0.89；对于受试者2，灵敏度和特异性分别为0.67和0.78。这些初步数据证明了利用家中传感器数据检测访客的可行性，但也凸显了需要更先进的建模技术，以便该模型对所有受试者和所有类型的访客都能有良好表现。
