Botros Angela A, Schutz Narayan, Saner Hugo, Buluschek Philipp, Nef Tobias
Annu Int Conf IEEE Eng Med Biol Soc. 2020 Jul;2020:5826-5830. doi: 10.1109/EMBC44109.2020.9175351.
Pervasive computing based home-monitoring has attracted increasing interest over the past years, especially regarding applications in the growing population of older adults. Applications include safety, monitoring chronic conditions like dementia, or providing preventive information about changes in health and behavior. Commonly used components of such systems are inexpensive and low-power passive infrared motion sensing units, usually placed in distinct locations of an older adult's apartment. To efficiently analyse the resulting data the majority of procedures expect the resulting sensor data to be encoded in a vector space. However, most common vector space encodings are based on orthogonal representations of the sensor locations and thus lead to loss of information as the sensors are placed in a 3D-space. In this work we introduce an embedding of sensor-locations in a 2D-space based on multidimensional scaling, without knowledge of the physical position of the sensors. We evaluate this embedding, using two different algorithms and compare it to commonly used baselines in different tasks. All evaluations are carried out on a real-world home-monitoring data-set.
在过去几年中，基于普适计算的家庭监测越来越受到关注，尤其是在老年人口不断增长的情况下的应用。应用包括安全、监测痴呆症等慢性病，或提供有关健康和行为变化的预防信息。此类系统常用的组件是廉价且低功耗的被动红外运动传感单元，通常放置在老年人公寓的不同位置。为了有效地分析所得数据，大多数程序期望所得传感器数据在向量空间中进行编码。然而，最常见的向量空间编码基于传感器位置的正交表示，因此由于传感器放置在三维空间中而导致信息丢失。在这项工作中，我们基于多维缩放引入了一种在二维空间中嵌入传感器位置的方法，而无需了解传感器的物理位置。我们使用两种不同的算法评估这种嵌入，并将其与不同任务中常用的基线进行比较。所有评估均在真实世界的家庭监测数据集上进行。
