Towards a framework to characterize ubiquitous software projects
Information and Software Technology
A comprehensive machine learning approach to prognose pulmonary disease from home
Proceedings of the International Conference on Advances in Computing, Communications and Informatics
Length of stay prediction for clinical treatment process using temporal similarity
Expert Systems with Applications: An International Journal
Reprint of "Length of stay prediction for clinical treatment process using temporal similarity"
Expert Systems with Applications: An International Journal
Affective and cognitive design for mass personalization: status and prospect
Journal of Intelligent Manufacturing
CoCaMAAL: A cloud-oriented context-aware middleware in ambient assisted living
Future Generation Computer Systems
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Elderly in-home assistance (EHA) has traditionally been tackled by human caregivers to equip the elderly with homecare assistance in their daily living. The emerging ambience intelligence (AmI) technology suggests itself to be of great potential for EHA applications, owing to its effectiveness in building a context-aware environment that is sensitive and responsive to the presence of humans. This paper presents a case-driven AmI (C-AmI) system, aiming to sense, predict, reason, and act in response to the elderly activities of daily living (ADLs) at home. The C-AmI system architecture is developed by synthesizing various sensors, activity recognition, case-based reasoning, along with EHA-customized knowledge, within a coherent framework. An EHA information model is formulated through the activity recognition, case comprehension, and assistive action layers. The rough set theory is applied to model ADLs based on the sensor platform embedded in a smart home. Assistive actions are fulfilled with reference to a priori case solutions and implemented within the AmI system through human-object-environment interactions. Initial findings indicate the potential of C-AmI for enhancing context awareness of EHA applications.