SmartWall: Novel RFID-Enabled Ambient Human Activity Recognition Using Machine Learning for Unobtrusive Health Monitoring

Abstract

Human activity recognition (HAR) from sensor readings has proved to be an effective approach in pervasive computing for smart healthcare. Recent approaches in ambient assisted living (AAL) within the home or community setting offers people the prospect of independent care and improved quality of living. However, most of the available AAL systems are limited by several factors including the system complexity and computational cost. In this paper, a simple, the novel ambient HAR framework using the multivariate Gaussian is proposed. The classification framework augments prior information from passive RFID tags to obtain more detailed activity profiling. The proposed algorithm based on the multivariate Gaussian via maximum likelihood estimation is used to learn the features of the human activity model. The twelve sequential and concurrent experimental evaluations are conducted in a mock apartment environment. The sampled activities are predicted using a new dataset of the same activity and high prediction accuracy established. The proposed framework suits well for the single and multi-dwelling environment and offers pervasive sensing environment for elderly, disabled, and carers.

Keywords

• Ambient assisted living
• human activity recognition
• machine learning
• multivariate Gaussian
• pervasive computing