Demographic predictions of population aged 65 and over suggest the need for telemedicine applications in the eldercare domain. Current solutions are mainly focused on fall detection. This paper presents a working prototype of a system that in addition to fall detection monitors a variety of user’s behavior characteristics that help raise awareness of health risks. It monitors the state of user’s health, and more importantly, detects changes in behavior characteristics that potentially indicate a forthcoming or current disease, illness or some other disability. The system utilizes domain knowledge from medical literature on quantitative behavior analysis and combines it with an outlier-detection algorithm in order to identify anomalous behavior. Preliminary results with the working prototype are promising, showing a potential to deploy the system in practice.

Research in open-domain commonsense reasoning has been hindered by the lack of evaluation metrics for judging progress and comparing alternative approaches. Taking inspiration from large-scale question sets used in natural language processing research, we authored one thousand English-language questions that directly assess commonsense causal reasoning, called the Choice Of Plausible Alternatives (COPA) evaluation. Using a forced-choice format, each question gives a premise and two plausible causes or effects, where the correct choice is the alternative that is more plausible than the other. This paper describes the authoring methodology that we used to develop a validated question set with sufficient breadth to advance open-domain commonsense reasoning research. We discuss the design decisions made during the authoring process, and explain how these decisions will affect the design of high-scoring systems. We also present the performance of multiple baseline approaches that use statistical natural language processing techniques, establishing initial benchmarks for future systems.

Applications range from the detection of potential all times t 1,...T (m 1)τ. We refer to such a sequence problems (such as an elderly person who has fallen down as a model of the system. Note that these models are in their home) to general monitoring of disease progression nonparametric. Theoretically, under some smoothness assumptions (e.g. in Parkinson's disease), or simply tracking the amount (Takens, 1981), if m is big enough, and τ is not of exercise and physical activity that a person gets. Ideally, a multiple of the period of the system, such a model captures such activities should be monitored as precisely as possible, all the relevant dynamics. However, real data is noisy, but using cheap or easily available devices, and in a way that so nonparametric models of the same activity can have high does not interfere with daily life.

I will present a perspective on human-level commonsense behavior (HLCSB) that differs from commonsense reasoning (CSR) as the latter is often characterized in AI. I will argue that HLCSB is not far beyond the reach of current technology, and that it also provides solutions to some of the problems that plague CSR, most notably the brittleness problem. A key is the judicious use of metacognitive monitoring and control, especially in the area of automated learning.

his paper focuses on the issue of premissary relevance, as a challenge faced in health promotion interventions. To promote attitude change and influence health behavior change, it is crucial that we use premises that are relevant on an individual level. Relevance in argumentation refers to both the fact that the premises have to do with the standpoint at issue and the fact that our interlocutors will accept them. We claim that autonomous argumentation systems hold the promise to enable proper argumentative exchanges that capture and addresses what matters to individuals. To do so, however, there is a need to better consider and operationalise theories of argumentation that enable a reconstruction of the different stages of argumentation. The theory of argumentation known as pragma-dialectics can offer a promising basis for the architecture of autonomous health promotion advisors.

In this contribution, it is suggested that argumentation theories may offer the tools to do so. More specifically, the pragmadialectical theory of argumentation (van Eemeren and Grootendorst 1992; 2004) is proposed as a solid instrument for analyzing and evaluating argumentation in consultation, as it not only provides a set of reasonableness criteria for argumentative conduct but also can account for arguers' need to effectively tailor argumentative messages to their recipients. The instrumental value of pragma-dialectics in the field of automated argument selection will be elucidated by means of a case study concerning antibiotics. In doing so, this contribution is closely connected to the paper by Rubinelli, Wierda, Labrie, and O'Keefe (AAAI Spring Symposium 2011) and provides an exploratory investigation of the advantages of a pragma-dialectical approach to the conceptual design of automated health communication systems and autonomous health promotion.

Developments in sophisticated mobile physiological sensors have presented many novel opportunities for monitoring coaching of individuals. In this work, we investigate the ability to utilize physiological data to recognize the state ofa user while exercising. We discuss recognition of user state using data suchas heart rate, respiration rate, and activity level. We also discuss the development of a motivational agent which utilizes the physiological data to help encourage a user during an exercise routine.

The paper addresses how a robot can maintain a state representation of all that it knows about the environment over time and space, given its observations and its domain knowledge. The advantage in combining domain knowledge and observations is that the robot can in this way project from the past into the future, and reason from observations to more general statements to help guide how it plans to act and interact. The difficulty lies in the fact that observations are typically uncertain and logical inference for completion against a knowledge base is computationally hard.

A subject independent computational framework is one which does not require to be calibrated by the specific subject data to be ready to be used on the subject. The greatest challenge in developing such a framework is the variation in parameters across subjects which is termed as subject based variability. Spectral and amplitude variations in surface myoelectric signals (SEMG) are analyzed to determine the fatigue state of a muscle. But variations in the spectrum and magnitude of myoelectric signals across subjects cause variations in both marginal and conditional probability distributions in the features extracted across subjects, making it difficult to model the signal for any automated signal classification. However we observe that the manifold of the multidimensional SEMG data have an inherent similarity as the physiological state moves from no fatigue to fatigue state. In this paper we exploit this specific feature of the SEMG data and propose a domain adaptation technique that is based on intrinsic manifold of the data preserved in a low dimensional space, thus reducing the marginal probability differences between the subjects, followed by an instance selection methodology, based on similar conditional probabilities in the mapped domain. The proposed method provides significant improvement in subject independent accuracies compared to cases without any domain adaptation methods and also compared to other state-of-the-art domain adaptation methodologies.

The past decade has witnessed an increasing interest in the use of virtual coaches in healthcare. This paper describes a virtual coach to provide mindfulness meditation training, and the coaching support necessary to begin a regular practice. The coach is implemented as an embodied conversational character, and provides mindfulness training and coaching support via a web-based application. The coach is represented as a female character, capable of showing a variety of affective and conversational expressions, and interacts with the user via a mixed-initiative, text-based, natural language dialogue. The coach adapts both its facial expressions and the dialogue content to the user’s learning needs and motivational state. Findings from a pilot evaluation study indicate that the coach-based training is more effective in helping users establish a regular practice than self-administered training via written and audio materials. The paper concludes with an analysis of the coach features that contribute to these results, discussion of key challenges in affect-adaptive coaching, and plans for future work.