An Advanced Home ElderCare Service

With the increase of welfare cost all over the developed world, there is a need to resort to new technologies that could help reduce this enormous cost and provide some quality eldercare services. This paper presents a middleware-level solution that integrates monitoring and emergency detection solutions with networking solutions. The proposed system enables efficient integration between a variety of sensors and actuators deployed at home for emergency detection and provides a framework for creating and managing rescue teams willing to assist elders in case of emergency situations. A prototype of the proposed system was designed and implemented. Results were obtained from both computer simulations and a real-network testbed. These results show that the proposed system can help overcome some of the current problems and help reduce the enormous cost of eldercare service

ANGELAH: A Framework for Assisting Elders At Home

The ever growing percentage of elderly people within modern societies poses welfare systems under relevant stress. In fact, partial and progressive loss of motor, sensorial, and/or cognitive skills renders elders unable to live autonomously, eventually leading to their hospitalization. This results in both relevant emotional and economic costs. Ubiquitous computing technologies can offer interesting opportunities for in-house safety and autonomy. However, existing systems partially address in-house safety requirements and typically focus on only elder monitoring and emergency detection. The paper presents ANGELAH, a middleware-level solution integrating both ”elder monitoring and emergency detection” solutions and networking solutions. ANGELAH has two main features: i) it enables efficient integration between a variety of sensors and actuators deployed at home for emergency detection and ii) provides a solid framework for creating and managing rescue teams composed of individuals willing to promptly assist elders in case of emergency situations. A prototype of ANGELAH, designed for a case study for helping elders with vision impairments, is developed and interesting results are obtained from both computer simulations and a real-network testbed

Towards Vision-Based Smart Hospitals: A System for Tracking and Monitoring Hand Hygiene Compliance

One in twenty-five patients admitted to a hospital will suffer from a hospital acquired infection. If we can intelligently track healthcare staff, patients, and visitors, we can better understand the sources of such infections. We envision a smart hospital capable of increasing operational efficiency and improving patient care with less spending. In this paper, we propose a non-intrusive vision-based system for tracking people's activity in hospitals. We evaluate our method for the problem of measuring hand hygiene compliance. Empirically, our method outperforms existing solutions such as proximity-based techniques and covert in-person observational studies. We present intuitive, qualitative results that analyze human movement patterns and conduct spatial analytics which convey our method's interpretability. This work is a step towards a computer-vision based smart hospital and demonstrates promising results for reducing hospital acquired infections.Comment: Machine Learning for Healthcare Conference (MLHC

A Study of Technology Innovations and Applications in Transforming Safety and Security in Healthcare Facility Management

The increasing complexity of construction projects has transformed the Architecture, Engineering, and Construction (AEC) industry through technology adoption over the last decade. But, Facility Management (FM) as an industry has been slow in technology adoption. Growing market competition, corporate demands and new ways of attracting clients for owners are compelling FM professionals to be more efficient. This requirement is driving technology adoption by FM professionals. This study identifies technologies adopted by Healthcare Facility Management (HFM) professionals for improving safety and security of users that have capabilities and conceived and/or developed applications that can or in some cases are at present used in HFM. Simultaneously, it also looks into potentials and capabilities of a handful of other technologies in further improving safety and security. Using Literature-Based Discovery (LBD), the technology applications and innovations aimed towards safety and security are discovered from the literature that falls within the purview of HFM to form a picture of how these technologies enhance the practice of FM. The study aims at detecting how technologies have contributed towards transforming user experience. Also, this study identifies existing technologies and innovation demands (knowledge and gaps in knowledge), a general understanding of technology, its use and capabilities, and its recognition by users and industry professionals (adoption/rejection). They serve to illustrate how and to what degree the technologies will come to be used in facility management. Technologies, as they mature, will come to be used by facility managers in similar functions and hypothetically, entirely new ones. One should create a better user experience tailored to the functionality demanded. It is important for facility managers to partner with technology companies presenting innovative solutions to create a platform that is tailored to user-specific needs. Acceptance of a unified process, together with input from users, facility managers, and an assessment of current technologies and new advances in practice are productive ways to develop technologies that drive user satisfaction. This paper works to illustrate a future vision of HFM based on these technologies. Healthcare facility managers will have a reference that assembles multiple technological proficiencies that can function in their practice going forward

Advancements In Crowd-Monitoring System: A Comprehensive Analysis of Systematic Approaches and Automation Algorithms: State-of-The-Art

Growing apprehensions surrounding public safety have captured the attention of numerous governments and security agencies across the globe. These entities are increasingly acknowledging the imperative need for reliable and secure crowd-monitoring systems to address these concerns. Effectively managing human gatherings necessitates proactive measures to prevent unforeseen events or complications, ensuring a safe and well-coordinated environment. The scarcity of research focusing on crowd monitoring systems and their security implications has given rise to a burgeoning area of investigation, exploring potential approaches to safeguard human congregations effectively. Crowd monitoring systems depend on a bifurcated approach, encompassing vision-based and non-vision-based technologies. An in-depth analysis of these two methodologies will be conducted in this research. The efficacy of these approaches is contingent upon the specific environment and temporal context in which they are deployed, as they each offer distinct advantages. This paper endeavors to present an in-depth analysis of the recent incorporation of artificial intelligence (AI) algorithms and models into automated systems, emphasizing their contemporary applications and effectiveness in various contexts