Technology used to recognize activities of daily living in community-dwelling older adults

The use of technology has been suggested as a means of allowing continued autonomous living for older adults, while reducing the burden on caregivers and aiding decision-making relating to healthcare. However, more clarity is needed relating to the Activities of Daily Living (ADL) recognised, and the types of technology included within current monitoring approaches. This review aims to identify these differences and highlight the current gaps in these systems. A scoping review was conducted in accordance with PRISMA-ScR, drawing on PubMed, Scopus, and Google Scholar. Articles and commercially available systems were selected if they focused on ADL recognition of older adults within their home environment. Thirty-nine ADL recognition systems were identified, nine of which were commercially available. One system incorporated environmental and wearable technology, two used only wearable technology, and 34 used only environmental technologies. Overall, 14 ADL were identified but there was variation in the specific ADL recognised by each system. Although the use of technology to monitor ADL of older adults is becoming more prevalent, there is a large variation in the ADL recognised, how ADL are defined, and the types of technology used within monitoring systems. Key stakeholders, such as older adults and healthcare workers, should be consulted in future work to ensure that future developments are functional and useable

Explainable pattern modelling and summarization in sensor equipped smart homes of elderly

In the next several decades, the proportion of the elderly population is expected to increase significantly. This has led to various efforts to help live them independently for longer periods of time. Smart homes equipped with sensors provide a potential solution by capturing various behavioral and physiological patterns of the residents. In this work, we develop techniques to model and detect changes in these patterns. The focus is on methods that are explainable in nature and allow for generating natural language descriptions. We propose a comprehensive change description framework that can detect unusual changes in the sensor parameters and describe the data leading to those changes in natural language. An approach that models and detects variations in physiological and behavioral routines of the elderly forms one part of the change description framework. The second part comes from a natural language generation system in which we identify important health-relevant features from the sensor parameters. Throughout this dissertation, we validate the developed techniques using both synthetic and real data obtained from the homes of the elderly living in sensor-equipped facilities. Using multiple real data retrospective case studies, we show that our methods are able to detect variations in the sensor data that are correlated with important health events in the elderly as recorded in their Electronic Health Records.Includes bibliographical reference

WSN based sensing model for smart crowd movement with identification: a conceptual model

With the advancement of IT and increase in world population rate, Crowd Management (CM) has become a subject undergoing intense study among researchers. Technology provides fast and easily available means of transport and, up-to-date information access to the people that causes crowd at public places. This imposes a big challenge for crowd safety and security at public places such as airports, railway stations and check points. For example, the crowd of pilgrims during Hajj and Ummrah while crossing the borders of Makkah, Kingdom of Saudi Arabia. To minimize the risk of such crowd safety and security identification and verification of people is necessary which causes unwanted increment in processing time. It is observed that managing crowd during specific time period (Hajj and Ummrah) with identification and verification is a challenge. At present, many advanced technologies such as Internet of Things (IoT) are being used to solve the crowed management problem with minimal processing time. In this paper, we have presented a Wireless Sensor Network (WSN) based conceptual model for smart crowd movement with minimal processing time for people identification. This handles the crowd by forming groups and provides proactive support to handle them in organized manner. As a result, crowd can be managed to move safely from one place to another with group identification. The group identification minimizes the processing time and move the crowd in smart way