An initiative for the creation of open datasets within pervasive healthcare

In this paper issues surrounding the collection, annotation, management and sharing of data gathered from pervasive health systems are presented. The overarching motivation for this work has been to provide an approach whereby annotated data sets can be made readily accessible to the research community in an effort to assist the advancement of the state-of-the-art in activity recognition and behavioural analysis using pervasive health systems. Recommendations of how this can be made a reality are presented in addition to the initial steps which have been taken to facilitate such an initiative involving the definition of common formats for data storage and a common set of tools for data processing and visualization

Ontology-Based Framework for the Automatic Recognition of Activities of Daily Living Using Class Expression Learning Techniques

The miniaturization and price reduction of sensors have encouraged the proliferation of smart environments, in which multitudinous sensors detect and describe the activities carried out by inhabitants. In this context, the recognition of activities of daily living has represented one of the most developed research areas in recent years. Its objective is to determine what daily activity is developed by the inhabitants of a smart environment. In this field, many proposals have been presented in the literature, many of them being based on ad hoc ontologies to formalize logical rules, which hinders their reuse in other contexts. In this work, we propose the use of class expression learning (CEL), an ontology-based data mining technique, for the recognition of ADL. This technique is based on combining the entities in the ontology, trying to find the expressions that best describe those activities. As far as we know, it is the first time that this technique is applied to this problem. To evaluate the performance of CEL for the automatic recognition of activities, we have first developed a framework that is able to convert many of the available datasets to all the ontology models we have found in the literature for dealing with ADL. Two different CEL algorithms have been employed for the recognition of eighteen activities in two different datasets. Although all the available ontologies in the literature are focused on the description of the context of the activities, the results show that the sequence of the events produced by the sensors is more relevant for their automatic recognition, in general terms

A situation-driven framework for relearning of activities of daily living in smart home environments

Activities of Daily Living (ADLs) are sine qua non for self-care and improved quality of life. Self-efficacy is major challenge for seniors with early-stage dementia (ED) when performing daily living activities. ED causes deterioration of cognitive functions and thus impacts aging adults’ functioning initiative and performance of instrumental activities of daily living (IADLs). Generally, IADLs requires certain skills in both planning and execution and may involve sequence of steps for aging adults to accomplish their goals. These intricate procedures in IADLs potentially predispose older adults to safety-critical situations with life-threatening consequences. A safety-critical situation is a state or event that potentially constitutes a risk with life-threatening injuries or accidents. To address this problem, a situation-driven framework for relearning of daily living activities in smart home environment is proposed. The framework is composed of three (3) major units namely: a) goal inference unit – leverages a deep learning model to infer human goal in a smart home, b) situation-context generator – responsible for risk mitigation in IADLs, and c) a recommendation unit – to support decision making of aging adults in safety-critical situations. The proposed framework was validated against IADLs dataset collected from a smart home research prototype and the results obtained are promising