Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, 20133 Milano, Italy.
Sensors (Basel). 2022 Jun 25;22(13):4823. doi: 10.3390/s22134823.
Population aging requires innovative solutions to increase the quality of life and preserve autonomous and independent living at home. A need of particular significance is the identification of behavioral drifts. A relevant behavioral drift concerns sociality: older people tend to isolate themselves. There is therefore the need to find methodologies to identify if, when, and how long the person is in the company of other people (possibly, also considering the number). The challenge is to address this task in poorly sensorized apartments, with non-intrusive sensors that are typically wireless and can only provide local and simple information. The proposed method addresses technological issues, such as PIR (Passive InfraRed) blind times, topological issues, such as sensor interference due to the inability to separate detection areas, and algorithmic issues. The house is modeled as a graph to constrain transitions between adjacent rooms. Each room is associated with a set of values, for each identified person. These values decay over time and represent the probability that each person is still in the room. Because the used sensors cannot determine the number of people, the approach is based on a multi-branch inference that, over time, differentiates the movements in the apartment and estimates the number of people. The proposed algorithm has been validated with real data obtaining an accuracy of 86.8%.
人口老龄化需要创新的解决方案来提高生活质量，保持在家中的自主和独立生活。一个特别重要的需求是识别行为漂移。一个相关的行为漂移涉及社交性：老年人往往会孤立自己。因此，有必要找到方法来确定一个人何时以及与多少人在一起，以及在一起的时间长短（可能还需要考虑人数）。挑战在于在传感器配置较差的公寓中解决这个任务，这些公寓使用的传感器是非侵入式的，通常是无线的，只能提供局部和简单的信息。所提出的方法解决了技术问题，例如 PIR（被动红外）盲区、拓扑问题，例如由于无法分离检测区域而导致的传感器干扰，以及算法问题。房屋被建模为一个图，以限制相邻房间之间的转换。每个房间都与一组值相关联，每个值对应一个已识别的人。这些值随时间衰减，代表每个人仍在房间内的概率。由于使用的传感器无法确定人数，因此该方法基于多分支推理，随着时间的推移，该推理可以区分公寓内的移动，并估计人数。所提出的算法已经使用真实数据进行了验证，准确率达到 86.8%。
