Journal of Ambient Intelligence and Smart Environments

Smart environments can already observe the actions of a human through pervasive sensors. Based on these observations, our work aims to predict the actions a human is likely to perform next. Predictions can enable a robot to proactively assist humans by autonomously executing an action on their behalf. In this paper, Action Graphs are introduced to model the order constraints between actions. Action Graphs are derived from a problem defined in Planning Domain Definition Language (PDDL). When an action is observed, the node values are updated and next actions predicted. Subsequently, a robot executes one of the predicted actions if it does not impact the flow of the human by obstructing or delaying them. Our Action Graph approach is applied to a kitchen domain.

The rapid growth of the world’s population increases the demand for more efficient management of ambient and environment. To address the demand, modern technologies are applied to enhance the surrounding to become more intelligent and efficient such as the development of intelligent building (IB) via the enhancement of the building with interconnected sensors, advanced event detection mechanism and automated mitigations action execution selected anomalous events. Recently, the use of complex event processing (CEP) approach has gained traction in implementation of IB. This paper aims to provide an inclusive review of the concept of IB, as well as its facilities, technologies, communication protocols, merit, and demerit in different applications. CEP technologies and their capabilities and applications to IBs are also discussed in detail. A sum of 130 publications is prepared and represented for future research. The objective of this review also highlights several issues and challenges of the conventional event processing systems of the IB applications and also provides some suggestions for advanced CEP systems. All the highlighted insights and recommendations of this review will hopefully lead to increasing efforts toward the development of future IB CEP system.

Diabetes is among the most common medical issues which people are facing nowadays. It may cause physical incapacity or even death in some cases. It has two core types, namely type I and type II. Both types are chronic and influence the functions of the human body that regulate blood sugar. In the human body, glucose is the main element that boosts cells. However, insulin is a key that enters the cells to control blood sugar. People with diabetes type I do not have the ability to produce insulin. Whereas people with diabetes type II lack the ability to react to insulin and frequently do not make enough insulin. For adequate analysis of such a fatal disease, techniques with a minimum error rate must be utilized. In this regard, different models of artificial neural network (ANN) have been investigated in the literature to diagnose/predict the condition with a minimum error rate, however, there is a need for improvement. To further advance the accuracy, a deep extreme learning machine (DELM) based prediction model is proposed and investigated in this research. By using the DELM approach, a high level of reliability with a minimum error rate is achieved. The approach shows significant improvement in results compared to previous investigations. It is observed that during the investigation the proposed approach has the highest accuracy rate of 92.8% with 70% of training (9500 samples) and 30% of test and validation (4500 examples). Simulation results validate the prediction effectiveness of the proposed scheme.

Smart homes equipped with ambient wireless sensor networks provide new opportunities to help older adults age-in-place, improve their quality of life and help better manage their health and wellness. In this paper, we present a methodology that estimates occupants’ status as active, sedentary, in-bed, out-of-home and unobservable, their location in the house, and their daily activities related to overall health and wellness. The methodology is used to visualize and examine the daily patterns and activities of older adults living in their own homes and participating in a smart home research project. The proposed location and status estimation algorithm is highly accurate as validated by a mobile app that prompts participants with questions about the estimated time of their daily activities. A case study involving a significant health-related life event is presented where the participant’s account of changes in her patterns and activities through bi-weekly interviews are shown to confirm inferences based on the results of the proposed methodology.

In this paper, we present our study regarding facilitating storytelling of older adults living in the nursing home with their children. The paper was driven by the following research questions: (1) What life stories would the older adults like to share? And (2) In which ways, could design enable the older adults to tell their stories? We designed a tangible device named Slots-Story, and conducted a preliminary evaluation to refine it. In the field study, eight pairs of participants (each pair consisted of an elderly adult and his/her child) were recruited to use the prototype for around ten days. Semi-structured interviews were conducted before and after the implementation. In total 344 stories were collected. Thematic, structural, and interaction analyses were conducted with the stories. In the discussion, we conclude the paper with design considerations for promoting older adults’ storytelling with their children.