Fuzzy cloud-fog computing approach application for human activity recognition in smart homes

A novel WGF-LN based edge driven intelligence for wearable devices in human activity recognition

Human activity recognition (HAR) is one of the key applications of health monitoring that requires continuous use of wearable devices to track daily activities. The most efficient supervised machine learning (ML)-based approaches for predicting human activity are based on a continuous stream of sensor data. Sensor data analysis for human activity recognition using conventional algorithms and deep learning (DL) models shows promising results, but evaluating their ambiguity in decision-making is still challenging. In order to solve these issues, the paper proposes a novel Wasserstein gradient flow legonet WGF-LN-based human activity recognition system. At first, the input data is pre-processed. From the pre-processed data, the features are extracted using Haar Wavelet mother- Symlet wavelet coefficient scattering feature extraction (HS-WSFE). After that, the interest features are selected from the extracted features using (Binomial Distribution integrated-Golden Eagle Optimization) BD-GEO. The important features are then post-processed using the scatter plot matrix method. Obtained post-processing features are finally given into the WGF-LN for classifying human activities. From these experiments, the results can be obtained and showed the efficacy of the proposed model.

Evaluation of the Impact of the Sustainable Development Goals on an Activity Recognition Platform for Healthcare Systems

The Sustainable Development Goals (SDGs), also known as the Global Goals, were adopted by the United Nations in 2015 as a universal call to end poverty, protect the planet and ensure peace and prosperity for all by 2030. The 17 SDGs have been designed to end poverty, hunger, AIDS and discrimination against women and girls. Despite the clear SDG framework, there is a significant gap in the literature to establish the alignment of systems, projects or tools with the SDGs. In this research work, we assess the SDG alignment of an activity recognition platform for healthcare systems, called ACTIVA. This new platform, designed to be deployed in environments inhabited by vulnerable people, is based on sensors and artificial intelligence, and includes a mobile application to report anomalous situations and ensure a rapid response from healthcare personnel. In this work, the ACTIVA platform and its compliance with each of the SDGs is assessed, providing a detailed evaluation of SDG 7-ensuring access to affordable, reliable, sustainable and modern energy for all. In addition, a website is presented where the ACTIVA platform's compliance with the 17 SDGs has been evaluated in detail. The comprehensive assessment of this novel platform's compliance with the SDGs provides a roadmap for the evaluation of future and past systems in relation to sustainability.

UJAmI Location: A Fuzzy Indoor Location System for the Elderly

Due to the large number of elderly people with physical and cognitive issues, there is a strong need to provide indoor location systems that help caregivers monitor as many people as possible and with the best quality possible. In this paper, a fuzzy indoor location methodology is proposed in a smart environment based on mobile devices and Bluetooth Low Energy (BLE) beacons where a set of Received Signal Strength Indicators (RSSI) is received by mobile devices worn by the inhabitants. The use of fuzzy logic and a fuzzy linguistic approach is proposed to deal with the imprecise nature of the RSSI values, which are influenced by external factors such as radio waves, causing significant fluctuations. A case study carried out at the Smart Lab of the University of Jaén (UJAmI Smart Lab) is presented to demonstrate the effectiveness of the proposed methodology, where our proposal is compared with a non-fuzzy logic approach, obtaining an accuracy of 91.63%, approximately 10 points higher than the methodology without using fuzzy logic. Finally, our theoretical proposal is accompanied by a description of the UJAmI Location system, which applies the theory to the functionality of locating elderly people in indoor environments.

Application of IoT in Healthcare: Keys to Implementation of the Sustainable Development Goals

We live in complex times in the health, social, political, and energy spheres, and we must be aware of and implement new trends in intelligent social health systems powered by the Internet of Things (IoT). Sustainable development, energy efficiency, and public health are interrelated parameters that can transform a system or an environment for the benefit of people and the planet. The integration of sensors and smart devices should promote energy efficiency and ensure that sustainable development goals are met. This work is carried out according to a mixed approach, with a literature review and an analysis of the impact of the Sustainable Development Goals on the applications of the Internet of Things and smart systems. In the analysis of results, the following questions are answered about these systems and applications: (a) Are IoT applications key to the improvement of people’s health and the environment? (b) Are there research and case studies implemented in cities or territories that demonstrate the effectiveness of IoT applications and their benefits to public health? (c) What sustainable development indicators and objectives can be assessed in the applications and projects analyzed?

Fuzzy Protoform for Hyperactive Behaviour Detection Based on Commercial Devices

Hyperactive behaviour refers to a person making more movement than expected for his or her age and development, acting impulsively, and being easily distracted. There is a need to encourage early and reliable detection through the proposal of new methodologies and systems in the context of hyperactive behaviour to prevent or lessen related problems and disorders. This paper presents a methodology to compute a fuzzy protoform (a linguistic description) as an estimator for hyperactive behaviour. The proposed methodology is developed in a system called Smart HyBeDe, which integrate non-invasive and commercial wearable devices, such as activity bracelets, in order to capture data streams from inertial measurement units and optical heart rate sensors. The generated data by the wearable device are synchronized with a mobile device to process the fuzzy protoform to inform family members and professionals. Three datasets generated by the wearable device in real contexts are presented. These datasets are used to evaluate the impact of wrist choice for the wearable device, multiple fuzzy temporal windows, different aggregation operators, and relevant linguistic terms to define the fuzzy protoform as an estimator for the hyperactive behaviour. The results, analysed by a hyperactive behaviour expert, show that the proposed protoform is a suitable hyperactive behaviour estimator.