Towards the Combination of Statistical and Symbolic Techniques for Activity Recognition Extended Abstract

Techniques for activity recognition are fundamental components of any context-aware system. Indeed, precise knowledge of the user’s current activity is necessary in order to thoroughly tailor services to the user’s context

Unsupervised Recognition of Multi-Resident Activities in Smart-Homes

Several methods have been proposed in the last two decades to recognize human activities based on sensor data acquired in smart-homes. While most existing methods assume the presence of a single inhabitant, a few techniques tackle the challenging issue of multi-resident activity recognition. To the best of our knowledge, all existing methods for multi-inhabitant activity recognition require the acquisition of a labeled training set of activities and sensor events. Unfortunately, activity labeling is costly and may disrupt the users' privacy. In this article, we introduce a novel technique to recognize multi-inhabitant activities without the need of labeled datasets. Our technique relies on an unlabeled sensor data stream acquired from a single resident, and on ontological reasoning to extract probabilistic associations among sensor events and activities. Extensive experiments with a large dataset of multi-inhabitant activities show that our technique achieves an average accuracy very close to the one of state-of-the-art supervised methods, without requiring the acquisition of labeled data

Continuous media adaptation for mobile computing using coarse-grained asynchronous notifications

The recent spreading of public wireless infrastructures allowing for higher data rates makes mobile communications networks a very attractive platform for distribution of multimedia content. At the same time, limited resources in public wireless networks pose serious questions on how to bring services and multimedia to terminals to be used anywhere. Content adaptation is required in order to bring the best perceptual experience to the end-user while optimizing resources usage. Unfortunately, content adaptation is very difficult to achieve and is usually related to band-width availability only. In this paper we propose to extend existing service provisioning architectures with an asynchronous notification system to keep up-to-date the whole set of user profile data during service provisioning. We argue that the average multimedia application behavior, still adhering to a model based on a very limited number of choices, is not affected by increased reaction time and coarse-grained parameters responsivity. Furthermore, introduction of asynchronous notifications will enable service providers to adapt content considering any parameter characterizing the user profile, not just available bandwidth. © 2005 IEEE

HealthXAI: Collaborative and explainable AI for supporting early diagnosis of cognitive decline

Our aging society claims for innovative tools to early detect symptoms of cognitive decline. Several research efforts are being made to exploit sensorized smart-homes and artificial intelligence (AI) methods to detect a decline of the cognitive functions of the elderly in order to promptly alert practitioners. Even though those tools may provide accurate predictions, they currently provide limited support to clinicians in making a diagnosis. Indeed, most AI systems do not provide any explanation of the reason why a given prediction was computed. Other systems are based on a set of rules that are easy to interpret by a human. However, those rule-based systems can cope with a limited number of abnormal situations, and are not flexible enough to adapt to different users and contextual situations. In this paper, we tackle this challenging problem by proposing a flexible AI system to recognize early symptoms of cognitive decline in smart-homes, which is able to explain the reason of predictions at a fine-grained level. Our method relies on well known clinical indicators that consider subtle and overt behavioral anomalies, as well as spatial disorientation and wandering behaviors. In order to adapt to different individuals and situations, anomalies are recognized using a collaborative approach. We experimented our approach with a large set of real world subjects, including people with MCI and people with dementia. We also implemented a dashboard to allow clinicians to inspect anomalies together with the explanations of predictions. Results show that our system's predictions are significantly correlated to the person's actual diagnosis. Moreover, a preliminary user study with clinicians suggests that the explanation capabilities of our system are useful to improve the task performance and to increase trust. To the best of our knowledge, this is the first work that explores data-driven explainable AI for supporting the diagnosis of cognitive decline

Distributed context monitoring for continuous mobile services

Context-awareness has been recognized as a very desirable feature for mobile internet services. This paper considers the acquisition of context information for continuous services, i.e., services that persist in time, like streaming services. Supporting context-awareness for these services requires the continuous monitoring of context information. The paper presents the extension of a middleware architecture for the reconciliation of distributed context information to support context-aware continuous services. The paper also addresses optimization issues and illustrates an adaptive video streaming prototype used to test the middleware. © 2005 by International Federation for Information Processing