A Multi-Agent System for Data Fusion Techniques Applied to the Internet of Things Enabling Physical Rehabilitation Monitoring

Smart Farm Security by Combining IoT Sensor Network and Virtualized Mycelium Network

In today's world, merging sensor-based security systems with contemporary principles has become crucial. As we witness the ever-growing number of interconnected devices in the Internet of Things (IoT), it is imperative to have robust and trustworthy security measures in place. In this paper, we examine the idea of virtualizing the communication infrastructure for smart farming in the context of IoT. Our approach utilizes a metaverse-based framework that mimics natural processes such as mycelium network growth communication with a security-concept-based srtificial immune system (AIS) and transaction models of a multi-agent system (MAS). The mycelium, a bridge that transfers nutrients from one plant to another, is an underground network (IoT below ground) that can interconnect multiple plants. Our objective is to study and simulate the mycelium's behavior, which serves as an underground IoT, and we anticipate that the simulation results, supported by diverse aspects, can be a reference for future IoT network development. A proof of concept is presented, demonstrating the capabilities of such a virtualized network for dedicated sensor communication and easy reconfiguration for various needs.

Enabling Remote Elderly Care: Design and Implementation of a Smart Energy Data System with Activity Recognition

Seniors face many challenges as they age, such as dementia, cognitive and memory disorders, vision and hearing impairment, among others. Although most of them would like to stay in their own homes, as they feel comfortable and safe, in some cases, older people are taken to special institutions, such as nursing homes. In order to provide serious and quality care to elderly people at home, continuous remote monitoring is perceived as a solution to keep them connected to healthcare service providers. The new trend in medical health services, in general, is to move from 'hospital-centric' services to 'home-centric' services with the aim of reducing the costs of medical treatments and improving the recovery experience of patients, among other benefits for both patients and medical centers. Smart energy data captured from electrical home appliance sensors open a new opportunity for remote healthcare monitoring, linking the patient's health-state/health-condition with routine behaviors and activities over time. It is known that deviation from the normal routine can indicate abnormal conditions such as sleep disturbance, confusion, or memory problems. This work proposes the development and deployment of a smart energy data with activity recognition (SEDAR) system that uses machine learning (ML) techniques to identify appliance usage and behavior patterns oriented to older people living alone. The proposed system opens the door to a range of applications that go beyond healthcare, such as energy management strategies, load balancing techniques, and appliance-specific optimizations. This solution impacts on the massive adoption of telehealth in third-world economies where access to smart meters is still limited.

A Cyber-Physical Approach for Residential Energy Management: Current State and Future Directions

In this work, we an envision Home Energy Management System (HEMS) as a Cyber-Physical System (CPS) architecture including three stages: Data Acquisition, Communication Network, and Data Analytics. In this CPS, monitoring, forecasting, comfort, occupation, and other strategies are conceived to feed a control plane representing the decision-making process. We survey the main technologies and techniques implemented in the recent years for each of the stages, reviewing and identifying the cutting-edge challenges that the research community are currently facing. For the Acquisition part, we define a metering device according to the IEC TS 63297:2021 Standard. We analyze the communication infrastructure as part of beyond 2030 communication era (5G and 6G), and discuss the Analytics stage as the cyber part of the CPS-based HEMS. To conclude, we present a case study in which, using real data collected in an experimental environment, we validate proposed architecture of HEMS in monitoring tasks. Results revealed an accuracy of 99.2% in appliance recognition compared with the state-of-the-art proposals.

A Novel Perspective for Examining and Comparing Real and Virtual Test Tasks Performed by the Dominant and Non-Dominant Hand in Healthy Adults

This study presents a novel perspective for the study of functional lateralization in a virtual reality environment. In the model study of handedness, the recognition of the dominant and non-dominant hand in real and virtual conditions was assessed using selected tests, such as a real light exposure test of Piórkowski’s apparatus and classical clinical tests, as well as virtual test tasks, in healthy adults. Statistically significant differences between the dominant and non-dominant hand were observed for tests carried out both in classical conditions and the virtual environment. The results and findings of other studies suggest that the virtual reality approach is a very promising and sensitive tool in the research on functional asymmetries in healthy and disease for motor skills and cognition processes.

RespiraConNosotros: A Viable Home-Based Telerehabilitation System for Respiratory Patients

Currently, there are more than 1.55 million cases of SARS-CoV-2 infection in Spain. Of these, it is estimated that around 45% will present respiratory complications, which represents approximately 620,000 patients who will need respiratory rehabilitation. The health system has no resources for this huge quantity of patients after the hospital discharge to finish the complete recovery and avoid the chronicity of the symptoms. We propose an application named RespiraConNosotros. The application has been created and designed to guide users in performing respiratory rehabilitation exercises, especially for COVID-19 patients, and it also facilitates patient–physiotherapist contact via chat or video calling to help patients. It is accessible for all users and on all devices. All exercises would be guided and supervised by a specialized physiotherapist who suggests, adapts, and guides the exercise according to the function level of each patient. Data obtained was satisfactory; all patients pointed out the easy access, the intuitive format, and the advantage of communicating with an expert. Concerning functional assessment, all participants improved their score on the Borg scale after performing the intervention with the application.This platform would help respiratory patients to make rehabilitation treatments to recover their pulmonary function and to decrease or eliminate the possible complications they have. It never substitutes any prescribed treatment. In conclusion, RespiraConNosotros is a simple, viable, and safe alternative for the improvement and maintenance of respiratory capacity and patient’s functionality affected by COVID-19. It could be used as a complement to face-to-face treatment when the situation allows it.

A systematic review of emerging information technologies for sustainable data-centric health-care

BACKGROUND

Of the Sustainable Development Goals (SDGs), the third presents the opportunity for a predictive universal digital healthcare ecosystem, capable of informing early warning, assisting in risk reduction and guiding management of national and global health risks. However, in reality, the existing technology infrastructure of digital healthcare systems is insufficient, failing to satisfy current and future data needs.

OBJECTIVE

This paper systematically reviews emerging information technologies for data modelling and analytics that have potential to achieve Data-Centric Health-Care (DCHC) for the envisioned objective of sustainable healthcare. The goal of this review is to: 1) identify emerging information technologies with potential for data modelling and analytics, and 2) explore recent research of these technologies in DCHC.

FINDINGS

A total of 1619 relevant papers have been identified and analysed in this review. Of these, 69 were probed deeply. Our analysis found that the extant research focused on elder care, rehabilitation, chronic diseases, and healthcare service delivery. Use-cases of the emerging information technologies included providing assistance, monitoring, self-care and self-management, diagnosis, risk prediction, well-being awareness, personalized healthcare, and qualitative and/or quantitative service enhancement. Limitations identified in the studies included vendor hardware specificity, issues with user interface and usability, inadequate features, interoperability, scalability, and compatibility, unjustifiable costs and insufficient evaluation in terms of validation.

CONCLUSION

Achievement of a predictive universal digital healthcare ecosystem in the current context is a challenge. State-of-the-art technologies demand user centric design, data privacy and protection measures, transparency, interoperability, scalability, and compatibility to achieve the SDG objective of sustainable healthcare by 2030.