Unobtrusive Health Monitoring in Private Spaces: The Smart Home

With the advances in sensor technology, big data, and artificial intelligence, unobtrusive in-home health monitoring has been a research focus for decades. Following up our research on smart vehicles, within the framework of unobtrusive health monitoring in private spaces, this work attempts to provide a guide to current sensor technology for unobtrusive in-home monitoring by a literature review of the state of the art and to answer, in particular, the questions: (1) What types of sensors can be used for unobtrusive in-home health data acquisition? (2) Where should the sensors be placed? (3) What data can be monitored in a smart home? (4) How can the obtained data support the monitoring functions? We conducted a retrospective literature review and summarized the state-of-the-art research on leveraging sensor technology for unobtrusive in-home health monitoring. For structured analysis, we developed a four-category terminology (location, unobtrusive sensor, data, and monitoring functions). We acquired 912 unique articles from four relevant databases (ACM Digital Lib, IEEE Xplore, PubMed, and Scopus) and screened them for relevance, resulting in n=55 papers analyzed in a structured manner using the terminology. The results delivered 25 types of sensors (motion sensor, contact sensor, pressure sensor, electrical current sensor, etc.) that can be deployed within rooms, static facilities, or electric appliances in an ambient way. While behavioral data (e.g., presence (n=38), time spent on activities (n=18)) can be acquired effortlessly, physiological parameters (e.g., heart rate, respiratory rate) are measurable on a limited scale (n=5). Behavioral data contribute to functional monitoring. Emergency monitoring can be built up on behavioral and environmental data. Acquired physiological parameters allow reasonable monitoring of physiological functions to a limited extent. Environmental data and behavioral data also detect safety and security abnormalities. Social interaction monitoring relies mainly on direct monitoring of tools of communication (smartphone; computer). In summary, convincing proof of a clear effect of these monitoring functions on clinical outcome with a large sample size and long-term monitoring is still lacking

An enhanced fall detection system for elderly person monitoring using consumer home networks

Various fall-detection solutions have been previously proposed to create a reliable surveillance system for elderly people with high requirements on accuracy, sensitivity and specificity. In this paper, an enhanced fall detection system is proposed for elderly person monitoring that is based on smart sensors worn on the body and operating through consumer home networks. With treble thresholds, accidental falls can be detected in the home healthcare environment. By utilizing information gathered from an accelerometer, cardiotachometer and smart sensors, the impacts of falls can be logged and distinguished from normal daily activities. The proposed system has been deployed in a prototype system as detailed in this paper. From a test group of 30 healthy participants, it was found that the proposed fall detection system can achieve a high detection accuracy of 97.5%, while the sensitivity and specificity are 96.8% and 98.1% respectively. Therefore, this system can reliably be developed and deployed into a consumer product for use as an elderly person monitoring device with high accuracy and a low false positive rate

The Impact of COVID-19 on California's Plans for Caring for its Aging Population: Technology Adoption and Employment

Older adults have traditionally been the age cohort most resistant to the adoption of technology, making aging in place difficult and frequently expensive. But recent advances in technology - and those just on the horizon - have the potential to transform the ability to age in place by making senior healthcare both safer and more connected. Such technology can enable healthcare professionals to monitor their charges in real time, respond quickly to healthcare emergencies, make healthcare consultations safer and less onerous for both patients and healthcare workers, assist elders with physical therapy, connect older adults to family and friends, and provide entertainment and learning opportunities for seniors. It is now even possible for older adults in rural areas to receive remote surgery from world class surgeons. The arrival of COVID-19 forced older adults into isolation, severely limiting contact with healthcare providers, family and friends. But COVID-19 also accelerated the adoption of many of the trends already extant, incentivizing older adults and healthcare providers to adopt new technologies much more rapidly than ever before. The WelbeHealth Program of All-inclusive Care for the Elderly (PACE) in California’s Central Valley, may provide a model of flexible, responsive and adaptive care

Ubiquitous Healthcare Information System: Toward Crossing the Security Chasm

Ubiquitous healthcare information system is increasingly seen as a viable option for reducing the inherent time lag and inaccuracies in the traditional model of healthcare and promoting the delivery and practice of evidence-based healthcare―as and when needed―without any location and time constraints. Although promising, the realization of ubiquitous healthcare information system brings several threats and risks rooted in real-time collection, analysis, storage, transmission, and access of critical medical data. In this research, we address information security concerns pertaining to the paradigm of ubiquitous healthcare information system. To accomplish this we use National Institute for Standards and Technology’s (NIST’s) system development lifecycle model (SDLC) as the underlying framework to explore the current state of ubiquitous healthcare from the perspective of security. We then leverage the model to propose future research directions in this area. By implementing the NIST’s SDLC model in such a manner, we offer a different dynamic of healthcare security that has not been addressed in literature before

Exploring IoT in Smart Cities: Practices, Challenges and Way Forward

The rise of Internet of things (IoT) technology has revolutionized urban living, offering immense potential for smart cities in which smart home, smart infrastructure, and smart industry are essential aspects that contribute to the development of intelligent urban ecosystems. The integration of smart home technology raises concerns regarding data privacy and security, while smart infrastructure implementation demands robust networking and interoperability solutions. Simultaneously, deploying IoT in industrial settings faces challenges related to scalability, standardization, and data management. This research paper offers a systematic literature review of published research in the field of IoT in smart cities including 55 relevant primary studies that have been published in reputable journals and conferences. This extensive literature review explores and evaluates various aspects of smart home, smart infrastructure, and smart industry and the challenges like security and privacy, smart sensors, interoperability and standardization. We provide a unified perspective, as we seek to enhance the efficiency and effectiveness of smart cities while overcoming security concerns. It then explores their potential for collective integration and impact on the development of smart cities. Furthermore, this study addresses the challenges associated with each component individually and explores their combined impact on enhancing urban efficiency and sustainability. Through a comprehensive analysis of security concerns, this research successfully integrates these IoT components in a unified approach, presenting a holistic framework for building smart cities of the future. Integrating smart home, smart infrastructure, and smart industry, this research highlights the significance of an integrated approach in developing smart cities