Bio-Interactive Healthcare Service System Using Lifelog Based Context Computing

Intelligent bio-sensor information processing was developed using lifelog based context aware technology to provide a flexible and dynamic range of diagnostic capabilities to satisfy healthcare requirements in ubiquitous and mobile computing environments. To accomplish this, various noise signals were grouped into six categories by context estimation and effectively reconfigured noise reduction filters by neural network and genetic algorithm. The neural network-based control module effectively selected an optimal filter block by noise context-based clustering in running mode, and filtering performance was improved by genetic algorithm in evolution mode. Due to its adaptive criteria, genetic algorithm was used to explore the action configuration for each identified bio-context to implement our concept. Our proposed Bio-interactive healthcare service system adopts the concepts of biological context-awareness with evolutionary computations in working environments modeled and identified as bio-sensors based environmental contexts. We used an unsupervised learning algorithm for lifelog based context modeling and a supervised learning algorithm for context identification.