HDOMO: Smart Sensor Integration for an Active and Independent Longevity of the Elderly

Abstract

The aim of this paper is to present the main results of HDOMO, an Ambient Assisted Living (AAL) project that involved 16 Small and Medium Enterprises (SMEs) and 2 research institutes. The objective of the project was to create an autonomous and automated domestic environment, primarily for elderly people and people with physical and motor disabilities. A known and familiar environment should help users in their daily activities and it should act as a virtual caregiver by calling, if necessary, relief efforts. Substantially, the aim of the project is to simplify the life of people in need of support, while keeping them autonomous in their private environment. From a technical point of view, the project provides the use of different Smart Objects (SOs), able to communicate among each other, in a cloud base infrastructure, and with the assisted users and their caregivers, in a perspective of interoperability and standardization of devices, usability and effectiveness of alarm systems. In the state of the art there are projects that achieve only a few of the elements listed. The HDOMO project aims to achieve all of them in one single project effectively. The experimental trials performed in a real scenario demonstrated the accuracy and efficiency of the system in extracting and processing data in real time to promptly acting, and in providing timely response to the needs of the user by integrating and confirming main alarms with different interoperable smart sensors. The article proposes a new technique to improve the accuracy of the system in detecting alarms using a multi-SO approach with information fusion between different devices, proving that this architecture can provide robust and reliable results on real environments.

Non-Invasive Ambient Intelligence in Real Life: Dealing with Noisy Patterns to Help Older People

“…They developed a series of alarms that detected unusual behaviours such as falls, sleep apnoea, incorrect feeding, etc. [35]. Other approaches, such as the one presented in [36], define systems that learn from room-to-room transitions and permanence to detect behaviour changes that may be related with health problems, which are communicated via alarms.…”

Evaluation of Doppler Signal Analysis Algorithms for Heart Rate Estimation

“…The Doppler Radar realized in [3] allows to determine the complex S21 parameter between the transmitting and the receiving antenna. The received signal is modulated both in amplitude and phase by chest movement.…”

“…The possibility of continuous, non-invasive and simple monitoring would lead to a reduction in hospital costs and a greater self awareness of the patient's health status. The use of electromagnetic (EM)sensors for remote respiration monitoring has proved to be very effective [2][3]. This type of sensor is very promising for domestic use as it respects the subject's privacy, it has an excellent ability to penetrate the fabrics and therefore it can be used even when the subjects are dressed.…”

Learning-Based Algorithm for Fault Prediction Combining Different Data Mining Techniques: A Real Case Study

“…Since over the years a proper maintenance plan resulted in management, performance, and profit benefits (Lucantoni et al, 2019), the idea of maintenance has moved to a more scientific policy, namely Preventive maintenance, for failure occurrences prevention and proper operating condition restoration in manufacturing (Ding & Kamaruddin, 2015). Among strategies, Predictive Maintenance (PdM) is considered one of the most interesting in the field of fault prediction, continuously growing with the technological advancement in manufacturing: due to the growing number of machines, systems, equipment, and products, DM models for data management through algorithms analytics (Frontoni et al, 2017) has demonstrated interesting prediction capabilities and decision making from a PdM perspective (Paolanti et al, 2017). In this context, the current paper aims to develop an algorithm for failure prediction.…”