National Research Council of Italy, Institute for Microelectronics and Microsystems, 73100 Lecce, Italy.
Sensors (Basel). 2022 Jun 29;22(13):4893. doi: 10.3390/s22134893.
COVID-19 has affected daily life in unprecedented ways, with dramatic changes in mental health, sleep time and level of physical activity. These changes have been especially relevant in the elderly population, with important health-related consequences. In this work, two different sensor technologies were used to quantify the energy expenditure of ageing adults. To this end, a technological platform based on Raspberry Pi 4, as an elaboration unit, was designed and implemented. It integrates an ambient sensor node, a wearable sensor node and a coordinator node that uses the information provided by the two sensor technologies in a combined manner. Ambient and wearable sensors are used for the real-time recognition of four human postures (standing, sitting, bending and lying down), walking activity and for energy expenditure quantification. An important first aim of this work was to realize a platform with a high level of user acceptability. In fact, through the use of two unobtrusive sensors and a low-cost processing unit, the solution is easily accessible and usable in the domestic environment; moreover, it is versatile since it can be used by end-users who accept being monitored by a specific sensor. Another added value of the platform is the ability to abstract from sensing technologies, as the use of human posture and walking activity for energy expenditure quantification enables the integration of a wide set of devices, provided that they can reproduce the same set of features. The obtained results showed the ability of the proposed platform to automatically quantify energy expenditure, both with each sensing technology and with the combined version. Specifically, for posture and walking activity classification, an average accuracy of 93.8% and 93.3% was obtained, respectively, with the wearable and ambient sensor, whereas an improvement of approximately 4% was reached using data fusion. Consequently, the estimated energy expenditure quantification always had a relative error of less than 3.2% for each end-user involved in the experimentation stage, classifying the high level information (postures and walking activities) with the combined version of the platform, justifying the proposed overall architecture from a hardware and software point of view.
COVID-19 以空前的方式影响了日常生活，导致心理健康、睡眠时间和身体活动水平发生了巨大变化。这些变化在老年人群体中尤为明显，对健康有重要影响。在这项工作中，使用了两种不同的传感器技术来量化老年人的能量消耗。为此，设计并实现了一个基于 Raspberry Pi 4 的技术平台，作为一个精心制作的单元。它集成了一个环境传感器节点、一个可穿戴传感器节点和一个协调器节点，该协调器节点以组合的方式使用两种传感器技术提供的信息。环境传感器和可穿戴传感器用于实时识别四种人体姿势（站立、坐着、弯腰和躺下）、行走活动和能量消耗量化。这项工作的一个重要目标是实现一个具有高用户接受度的平台。实际上，通过使用两个不显眼的传感器和一个低成本的处理单元，该解决方案在家庭环境中易于访问和使用；此外，它具有多功能性，因为它可以被接受由特定传感器进行监测的最终用户使用。该平台的另一个附加值是能够从传感技术中抽象出来，因为使用人体姿势和行走活动来量化能量消耗可以实现广泛的设备集成，只要它们能够再现相同的特征集。所得到的结果表明，该平台能够自动量化能量消耗，无论是使用每种传感技术还是使用组合版本。具体来说，对于姿势和行走活动分类，可穿戴传感器和环境传感器分别获得了 93.8%和 93.3%的平均准确率，而使用数据融合则提高了约 4%。因此，对于参与实验阶段的每个最终用户，估计的能量消耗量化总是具有小于 3.2%的相对误差，通过平台的组合版本对高级信息（姿势和行走活动）进行分类，从硬件和软件角度证明了所提出的总体架构是合理的。
