NOVEL STACKING CLASSIFICATION AND PREDICTION ALGORITHM BASED AMBIENT ASSISTED LIVING FOR ELDERLY

Abstract

The ageing of the population in developed nations necessitates the expansion of medical services, raising the cost of both economic and human resources. In this respect, Ambient Assisted Living (AAL) is a comparatively new information and communication technology (ICT) that delivers services. It acknowledges numerous products that help elderly and disabled people to live autonomously and enrich the quality of their lives. It also aids in the cost-cutting of hospital services. Various sensors and equipment are installed in the AAL context to collect a wide variety of data. Furthermore, AAL could be the motivating technique for the most recent care models by working as an adjunct. Research and development (R&D) projects and business activities in AAL and smart home contexts frequently emphasize the significance of technology. While ICTs promote health care, they have the potential to alleviate loneliness and social isolation among the elderly. They help to enhance, expand, and maintain the social interactions of the elderly while also increasing the individual's emotional well-being. The emergence of smart homes will help the elderly and the disabled live better lives. Over the past five years, the acceptance of wearable fall detection technologies has increased. These techniques involve calling for help in an emergency, such as falling or being immobile for long periods. Because falling is widespread among older adults, it can have serious health consequences. Falls can result in physical traumas such as fractures, head injuries, and severe decay. Falls will have a considerable impact on some populations, necessitating the development of better fall prevention and management solutions. Therefore, this thesis proposed a Novel Stacking Classification and Prediction (NSCP) algorithm based on AAL for the older people with Multi-strategy Combination based Feature Selection (MCFS) and Novel Clustering Aggregation (NCA) algorithms. The primary objective of this thesis is to identify the fall detection and prediction in older persons, such as 0 - no fall detected, 1 - person slipped/tripped / fall prediction, and 2 - definite fall. This study's dataset was sourced from the Kaggle machine learning repository, and it refers to data gathering from wearable IoT devices. The experimental outcomes demonstrate the proposed MCFS, NCA, and NSCP algorithms work more effectively than previous feature selection, clustering and classification algorithms, respectively, in terms of accuracy, sensitivity, specificity, precision, recall, f-measure and execution time. This thesis concludes with a discussion of future work to improve the proposed methodology and future research directions.