Smart Cupboard for Assessing Memory in Home Environment

Sensor systems for the Internet of Things (IoT) make it possible to continuously monitor people, gathering information without any extra effort from them. Thus, the IoT can be very helpful in the context of early disease detection, which can improve peoples'' quality of life by applying the right treatment and measures at an early stage. This paper presents a new use of IoT sensor systemswe present a novel three-door smart cupboard that can measure the memory of a user, aiming at detecting potential memory losses. The smart cupboard has three sensors connected to a Raspberry Pi, whose aim is to detect which doors are opened. Inside of the Raspberry Pi, a Python script detects the openings of the doors, and classifies the events between attempts of finding something without success and the events of actually finding it, in order to measure the user''s memory concerning the objects'' locations (among the three compartments of the smart cupboard). The smart cupboard was assessed with 23 different users in a controlled environment. This smart cupboard was powered by an external battery. The memory assessments of the smart cupboard were compared with a validated test of memory assessment about face-name associations and a self-reported test about self-perceived memory. We found a significant correlation between the smart cupboard results and both memory measurement methods. Thus, we conclude that the proposed novel smart cupboard successfully measured memory

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

Applications of Deep Learning and Machine Learning in Healthcare Domain – A Literature Review

In recent years, Artificial Intelligence (AI) has advanced rapidly in terms of software algorithms, hardware implementation, and implementations in a wide range of fields. The latest advances in AI applications in biomedicine, such as disease diagnostics, living assistance, biomedical information processing, and biomedical science, are summarised in this study. Brain-Computer Interfaces (BCIs), Arterial Spin Labeling (ASL) imaging, ASL-MRI, biomarkers, Natural Language Processing (NLP), and various algorithms all help to reduce errors and monitor disease progression. Computer-assisted diagnosis, decision support systems, expert systems, and software implementation can help doctors reduce intra- and inter-observer variability. In this paper, numerous researchers conduct a systematic literature review on the application and implementation of Machine Learning, Deep Learning, and Artificial Intelligence in the healthcare industry

Artificial Intelligence for Cognitive Health Assessment: State-of-the-Art, Open Challenges and Future Directions

The subjectivity and inaccuracy of in-clinic Cognitive Health Assessments (CHA) have led many researchers to explore ways to automate the process to make it more objective and to facilitate the needs of the healthcare industry. Artificial Intelligence (AI) and machine learning (ML) have emerged as the most promising approaches to automate the CHA process. In this paper, we explore the background of CHA and delve into the extensive research recently undertaken in this domain to provide a comprehensive survey of the state-of-the-art. In particular, a careful selection of significant works published in the literature is reviewed to elaborate a range of enabling technologies and AI/ML techniques used for CHA, including conventional supervised and unsupervised machine learning, deep learning, reinforcement learning, natural language processing, and image processing techniques. Furthermore, we provide an overview of various means of data acquisition and the benchmark datasets. Finally, we discuss open issues and challenges in using AI and ML for CHA along with some possible solutions. In summary, this paper presents CHA tools, lists various data acquisition methods for CHA, provides technological advancements, presents the usage of AI for CHA, and open issues, challenges in the CHA domain. We hope this first-of-its-kind survey paper will significantly contribute to identifying research gaps in the complex and rapidly evolving interdisciplinary mental health field

Internet of Things (IoT) for Automated and Smart Applications

Internet of Things (IoT) is a recent technology paradigm that creates a global network of machines and devices that are capable of communicating with each other. Security cameras, sensors, vehicles, buildings, and software are examples of devices that can exchange data between each other. IoT is recognized as one of the most important areas of future technologies and is gaining vast recognition in a wide range of applications and fields related to smart homes and cities, military, education, hospitals, homeland security systems, transportation and autonomous connected cars, agriculture, intelligent shopping systems, and other modern technologies. This book explores the most important IoT automated and smart applications to help the reader understand the principle of using IoT in such applications

Smart aging : utilisation of machine learning and the Internet of Things for independent living

Smart aging utilises innovative approaches and technology to improve older adults’ quality of life, increasing their prospects of living independently. One of the major concerns the older adults to live independently is “serious fall”, as almost a third of people aged over 65 having a fall each year. Dementia, affecting nearly 9% of the same age group, poses another significant issue that needs to be identified as early as possible. Existing fall detection systems from the wearable sensors generate many false alarms; hence, a more accurate and secure system is necessary. Furthermore, there is a considerable gap to identify the onset of cognitive impairment using remote monitoring for self-assisted seniors living in their residences. Applying biometric security improves older adults’ confidence in using IoT and makes it easier for them to benefit from smart aging. Several publicly available datasets are pre-processed to extract distinctive features to address fall detection shortcomings, identify the onset of dementia system, and enable biometric security to wearable sensors. These key features are used with novel machine learning algorithms to train models for the fall detection system, identifying the onset of dementia system, and biometric authentication system. Applying a quantitative approach, these models are tested and analysed from the test dataset. The fall detection approach proposed in this work, in multimodal mode, can achieve an accuracy of 99% to detect a fall. Additionally, using 13 selected features, a system for detecting early signs of dementia is developed. This system has achieved an accuracy rate of 93% to identify a cognitive decline in the older adult, using only some selected aspects of their daily activities. Furthermore, the ML-based biometric authentication system uses physiological signals, such as ECG and Photoplethysmogram, in a fusion mode to identify and authenticate a person, resulting in enhancement of their privacy and security in a smart aging environment. The benefits offered by the fall detection system, early detection and identifying the signs of dementia, and the biometric authentication system, can improve the quality of life for the seniors who prefer to live independently or by themselves

Ambient Assisted Living: Scoping Review of Artificial Intelligence Models, Domains, Technology, and Concerns

Background: Ambient assisted living (AAL) is a common name for various artificial intelligence (AI)—infused applications and platforms that support their users in need in multiple activities, from health to daily living. These systems use different approaches to learn about their users and make automated decisions, known as AI models, for personalizing their services and increasing outcomes. Given the numerous systems developed and deployed for people with different needs, health conditions, and dispositions toward the technology, it is critical to obtain clear and comprehensive insights concerning AI models used, along with their domains, technology, and concerns, to identify promising directions for future work. Objective: This study aimed to provide a scoping review of the literature on AI models in AAL. In particular, we analyzed specific AI models used in AАL systems, the target domains of the models, the technology using the models, and the major concerns from the end-user perspective. Our goal was to consolidate research on this topic and inform end users, health care professionals and providers, researchers, and practitioners in developing, deploying, and evaluating future intelligent AAL systems. Methods: This study was conducted as a scoping review to identify, analyze, and extract the relevant literature. It used a natural language processing toolkit to retrieve the article corpus for an efficient and comprehensive automated literature search. Relevant articles were then extracted from the corpus and analyzed manually. This review included 5 digital libraries: IEEE, PubMed, Springer, Elsevier, and MDPI. Results: We included a total of 108 articles. The annual distribution of relevant articles showed a growing trend for all categories from January 2010 to July 2022. The AI models mainly used unsupervised and semisupervised approaches. The leading models are deep learning, natural language processing, instance-based learning, and clustering. Activity assistance and recognition were the most common target domains of the models. Ambient sensing, mobile technology, and robotic devices mainly implemented the models. Older adults were the primary beneficiaries, followed by patients and frail persons of various ages. Availability was a top beneficiary concern. Conclusions: This study presents the analytical evidence of AI models in AAL and their domains, technologies, beneficiaries, and concerns. Future research on intelligent AAL should involve health care professionals and caregivers as designers and users, comply with health-related regulations, improve transparency and privacy, integrate with health care technological infrastructure, explain their decisions to the users, and establish evaluation metrics and design guidelines. Trial Registration: PROSPERO (International Prospective Register of Systematic Reviews) CRD42022347590; https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42022347590This work was part of and supported by GoodBrother, COST Action 19121—Network on Privacy-Aware Audio- and Video-Based Applications for Active and Assisted Living

A Unified Recommendation Framework for Data-driven, People-centric Smart Home Applications

With the rapid growth in the number of things that can be connected to the internet, Recommendation Systems for the IoT (RSIoT) have become more significant in helping a variety of applications to meet user preferences, and such applications can be smart home, smart tourism, smart parking, m-health and so on. In this thesis, we propose a unified recommendation framework for data-driven, people-centric smart home applications. The framework involves three main stages: complex activity detection, constructing recommendations in timely manner, and insuring the data integrity. First, we review the latest state-of-the-art recommendations methods and development of applications for recommender system in the IoT so, as to form an overview of the current research progress. Challenges of using IoT for recommendation systems are introduced and explained. A reference framework to compare the existing studies and guide future research and practices is provided. In order to meet the requirements of complex activity detection that helps our system to understand what activity or activities our user is undertaking in relatively high level. We provide adequate resources to be fit for the recommender system. Furthermore, we consider two inherent challenges of RSIoT, that is, capturing dynamicity patterns of human activities and system update without a focus on user feedback. Based on these, we design a Reminder Care System (RCS) which harnesses the advantages of deep reinforcement learning (DQN) to further address these challenges. Then we utilize a contextual bandit approach for improving the quality of recommendations by considering the context as an input. We aim to address not only the two previous challenges of RSIoT but also to learn the best action in different scenarios and treat each state independently. Last but not least, we utilize a blockchain technology to ensure the safety of data storage in addition to decentralized feature. In the last part, we discuss a few open issues and provide some insights for future directions

Seven HCI Grand Challenges

This article aims to investigate the Grand Challenges which arise in the current and emerging landscape of rapid technological evolution towards more intelligent interactive technologies, coupled with increased and widened societal needs, as well as individual and collective expectations that HCI, as a discipline, is called upon to address. A perspective oriented to humane and social values is adopted, formulating the challenges in terms of the impact of emerging intelligent interactive technologies on human life both at the individual and societal levels. Seven Grand Challenges are identified and presented in this article: Human-Technology Symbiosis; Human-Environment Interactions; Ethics, Privacy and Security; Well-being, Health and Eudaimonia; Accessibility and Universal Access; Learning and Creativity; and Social Organization and Democracy. Although not exhaustive, they summarize the views and research priorities of an international interdisciplinary group of experts, reflecting different scientific perspectives, methodological approaches and application domains. Each identified Grand Challenge is analyzed in terms of: concept and problem definition; main research issues involved and state of the art; and associated emerging requirements