Cognitive assisted living ambient system: a survey

The demographic change towards an aging population is creating a significant impact and introducing drastic challenges to our society. We therefore need to find ways to assist older people to stay independently and prevent social isolation of these population. Information and Communication Technologies (ICT) provide various solutions to help older adults to improve their quality of life, stay healthier, and live independently for a time. Ambient Assisted Living (AAL) is a field to investigate innovative technologies to provide assistance as well as healthcare and rehabilitation to impaired seniors. The paper provides a review of research background and technologies of AAL

IoT-Based Applications in Healthcare Devices

The last decade has witnessed extensive research in the field of healthcare services and their technological upgradation. To be more specific, the Internet of Things (IoT) has shown potential application in connecting various medical devices, sensors, and healthcare professionals to provide quality medical services in a remote location. This has improved patient safety, reduced healthcare costs, enhanced the accessibility of healthcare services, and increased operational efficiency in the healthcare industry. The current study gives an up-to-date summary of the potential healthcare applications of IoT- (HIoT-) based technologies. Herein, the advancement of the application of the HIoT has been reported from the perspective of enabling technologies, healthcare services, and applications in solving various healthcare issues. Moreover, potential challenges and issues in the HIoT system are also discussed. In sum, the current study provides a comprehensive source of information regarding the different fields of application of HIoT intending to help future researchers, who have the interest to work and make advancements in the field to gain insight into the topic

Biosignal‐based human–machine interfaces for assistance and rehabilitation : a survey

As a definition, Human–Machine Interface (HMI) enables a person to interact with a device. Starting from elementary equipment, the recent development of novel techniques and unobtrusive devices for biosignals monitoring paved the way for a new class of HMIs, which take such biosignals as inputs to control various applications. The current survey aims to review the large literature of the last two decades regarding biosignal‐based HMIs for assistance and rehabilitation to outline state‐of‐the‐art and identify emerging technologies and potential future research trends. PubMed and other databases were surveyed by using specific keywords. The found studies were further screened in three levels (title, abstract, full‐text), and eventually, 144 journal papers and 37 conference papers were included. Four macrocategories were considered to classify the different biosignals used for HMI control: biopotential, muscle mechanical motion, body motion, and their combinations (hybrid systems). The HMIs were also classified according to their target application by considering six categories: prosthetic control, robotic control, virtual reality control, gesture recognition, communication, and smart environment control. An ever‐growing number of publications has been observed over the last years. Most of the studies (about 67%) pertain to the assistive field, while 20% relate to rehabilitation and 13% to assistance and rehabilitation. A moderate increase can be observed in studies focusing on robotic control, prosthetic control, and gesture recognition in the last decade. In contrast, studies on the other targets experienced only a small increase. Biopotentials are no longer the leading control signals, and the use of muscle mechanical motion signals has experienced a considerable rise, especially in prosthetic control. Hybrid technologies are promising, as they could lead to higher performances. However, they also increase HMIs’ complex-ity, so their usefulness should be carefully evaluated for the specific application

International Conference on NeuroRehabilitation 2012

This volume 3, number 2 gathers a set of articles based on the most outstanding research on accessibility and disability issues that was presented in the International Conference on NeuroRehabilitation 2012 (ICNR).The articles’ research present in this number is centred on the analysis and/or rehabilitation of body impairment most due to brain injury and neurological disorders.JACCES thanks the collaboration of the ICNR members and the research authors and reviewers that have collaborated for making possible that issue

INTEGRATION OF INTERNET OF THINGS AND HEALTH RECOMMENDER SYSTEMS

The Internet of Things (IoT) has become a part of our lives and has provided many enhancements to day-to-day living. In this project, IoT in healthcare is reviewed. IoT-based healthcare is utilized in remote health monitoring, observing chronic diseases, individual fitness programs, helping the elderly, and many other healthcare fields. There are three main architectures of smart IoT healthcare: Three-Layer Architecture, Service-Oriented Based Architecture (SoA), and The Middleware-Based IoT Architecture. Depending on the required services, different IoT architecture are being used. In addition, IoT healthcare services, IoT healthcare service enablers, IoT healthcare applications, and IoT healthcare services focusing on Smartwatch are presented in this research. Along with IoT in smart healthcare, Health Recommender Systems integration with IoT is important. Main Recommender Systems including Content-based filtering, Collaborative-based filtering, Knowledge-based filtering, and Hybrid filtering with machine learning algorithms are described for the Health Recommender Systems. In this study, a framework is presented for the IoT-based Health Recommender Systems. Also, a case is investigated on how different algorithms can be used for Recommender Systems and their accuracy levels are presented. Such a framework can help with the health issues, for example, risk of going to see the doctor during pandemic, taking quick actions in any health emergencies, affordability of healthcare services, and enhancing the personal lifestyle using recommendations in non-critical conditions. The proposed framework can necessitate further development of IoT-based Health Recommender Systems so that people can mitigate their medical emergencies and live a healthy life

Rehabilitation Engineering

Population ageing has major consequences and implications in all areas of our daily life as well as other important aspects, such as economic growth, savings, investment and consumption, labour markets, pensions, property and care from one generation to another. Additionally, health and related care, family composition and life-style, housing and migration are also affected. Given the rapid increase in the aging of the population and the further increase that is expected in the coming years, an important problem that has to be faced is the corresponding increase in chronic illness, disabilities, and loss of functional independence endemic to the elderly (WHO 2008). For this reason, novel methods of rehabilitation and care management are urgently needed. This book covers many rehabilitation support systems and robots developed for upper limbs, lower limbs as well as visually impaired condition. Other than upper limbs, the lower limb research works are also discussed like motorized foot rest for electric powered wheelchair and standing assistance device

Surface Electromyography and Artificial Intelligence for Human Activity Recognition - A Systematic Review on Methods, Emerging Trends Applications, Challenges, and Future Implementation

Human activity recognition (HAR) has become increasingly popular in recent years due to its potential to meet the growing needs of various industries. Electromyography (EMG) is essential in various clinical and biological settings. It is a metric that helps doctors diagnose conditions that affect muscle activation patterns and monitor patients’ progress in rehabilitation, disease diagnosis, motion intention recognition, etc. This review summarizes the various research papers based on HAR with EMG. Over recent years, the integration of Artificial Intelligence (AI) has catalyzed remarkable advancements in the classification of biomedical signals, with a particular focus on EMG data. Firstly, this review meticulously curates a wide array of research papers that have contributed significantly to the evolution of EMG-based activity recognition. By surveying the existing literature, we provide an insightful overview of the key findings and innovations that have propelled this field forward. It explore the various approaches utilized for preprocessing EMG signals, including noise reduction, baseline correction, filtering, and normalization, ensure that the EMG data is suitably prepared for subsequent analysis. In addition, we unravel the multitude of techniques employed to extract meaningful features from raw EMG data, encompassing both time-domain and frequency-domain features. These techniques are fundamental to achieving a comprehensive characterization of muscle activity patterns. Furthermore, we provide an extensive overview of both Machine Learning (ML) and Deep Learning (DL) classification methods, showcasing their respective strengths, limitations, and real-world applications in recognizing diverse human activities from EMG signals. In examining the hardware infrastructure for HAR with EMG, the synergy between hardware and software is underscored as paramount for enabling real-time monitoring. Finally, we also discovered open issues and future research direction that may point to new lines of inquiry for ongoing research toward EMG-based detection.publishedVersio

Robotic Platforms for Assistance to People with Disabilities

People with congenital and/or acquired disabilities constitute a great number of dependents today. Robotic platforms to help people with disabilities are being developed with the aim of providing both rehabilitation treatment and assistance to improve their quality of life. A high demand for robotic platforms that provide assistance during rehabilitation is expected because of the health status of the world due to the COVID-19 pandemic. The pandemic has resulted in countries facing major challenges to ensure the health and autonomy of their disabled population. Robotic platforms are necessary to ensure assistance and rehabilitation for disabled people in the current global situation. The capacity of robotic platforms in this area must be continuously improved to benefit the healthcare sector in terms of chronic disease prevention, assistance, and autonomy. For this reason, research about human–robot interaction in these robotic assistance environments must grow and advance because this topic demands sensitive and intelligent robotic platforms that are equipped with complex sensory systems, high handling functionalities, safe control strategies, and intelligent computer vision algorithms. This Special Issue has published eight papers covering recent advances in the field of robotic platforms to assist disabled people in daily or clinical environments. The papers address innovative solutions in this field, including affordable assistive robotics devices, new techniques in computer vision for intelligent and safe human–robot interaction, and advances in mobile manipulators for assistive tasks