Integrating Alexa in a Rule-Based Personalization Platform
Manca M., Parvin P., Paternò F., Santoro C.
Vocal assistants are becoming widely used, but their potentialities have not yet been completely exploited. For instance, while assistants such as Alexa are increasingly boasting compatibility with a large set of third-party services, the possibility for end-users to personalize the joint behaviour of such connected services (including the voice-based ones) in a flexible manner seems not sufficiently explored yet. In this paper, we present how the voice-based support offered by Alexa has been integrated with a rule-based personalization platform to support the creation of trigger-action rules enhanced with voice-based support. This integration opens up the possibility for users without programming knowledge to specify and include voice-based triggers and voice-based actions in their rules. These rules can be composed of events and commands that can involve a variety of sensors and connected objects. To this aim, a novel solution has been developed, which also aims to overcome some limitations that have been found in currently available vocal assistants, e.g., the issue of unsupported languages, thus lowering the barriers for their ultimate adoption and everyday use. Indeed, the integrated platform offers the possibility to play the vocal notifications/reminders contained in relevant personalization rules in any language, including those not currently supported by Alexa.Source: EAI GOODTECHS 2020, pp. 108–113, Virtual conference, 15-17/09/2020
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Anomaly detection in the elderly daily behavior
Parvin P., Paternò F., Chessa S.
The increasing availability of sensors and intelligent objects enables new functionalities and services. In the Ambient Assisted Living (AAL) domain, such technologies can be used for monitoring and reasoning about the older people behavior to detect possible anomalous situations, which could be a sign of the next onset of chronic illness or initial physical and cognitive decline. We propose an approach to detecting abnormal behavior by developing a profiling strategy (in which task models specify the normal behavior), which can also work in case of rare anomaly data. Events corresponding to the user behavior is detecting by a middleware software(Context Manager). Afterward, our algorithm compares the planned and actual behavior to identify if any deviation occurred and also defines to which category the anomaly belongs. The resulting environment should be able to generate multi-modal actions (i.e alarms, reminders) based on detected anomalous behavior, aiming to provide useful support to improve older people well-being.Source: 14TH INTERNATIONAL CONFERENCE ON INTELLIGENT ENVIRONMENTS, pp. 103–106, Rome, 25-28 June, 2018
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Real-time anomaly detection in elderly behavior
The rapid growth of the aging population and the increasing cost of the hospitalization are arousing the urgent need of the remote health monitoring system. Using the physiological sensing devices enable early detecting of health issues and allow for prompt treatment to help elderly towards changing their anomalous behavior and having a healthy lifestyle. Our approach, exploited task models to produce scenarios (which is the expected user behavior) and a middleware software, Context Manager to detect the events happened in the real context. Later, our real-time algorithm compares the expected user behavior to the real one detected in user context to find the anomalies if there is any. Finally, we validated our approach via a simulator, which automatically generates the anomalous sequences of user activities. The experimental results show that our system can detect abnormal user behavior precisely and effectively. Besides, the system should be able to generate proper action based on the detected deviation to motivate older people towards a healthy lifestyle.Source: EICS '18: ACM SIGCHI Symposium on Engineering Interactive Computing Systems, pp. 1–6, Paris, France, June, 2018
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Detecting anomalous elderly behaviour in Ambient Assisted Living
Manca M., Parvin P., Paterno F., Santoro C.
The increasing availability of sensors and intelligent objects enables new functionalities and services. In the Ambient Assisted Living domain such technologies can be used for monitoring the elderly behaviour, and reasoning about it to detect possible anomalous situations, which could be a sign of the next onset of chronic illness or initial physical and cognitive decline. In this paper we propose a solution that exploits task models describing expected user behaviour, and a context manager able to detect relevant contextual events and conditions determined by the actual elderly behaviour. Planned and actual behaviour are compared to detect if any deviation occurred. The resulting environment is able to generate multimodal actions such as reminders and alarms aiming to provide useful support when such anomalous behaviour is detected.Source: EICS '17 - ACM SIGCHI Symposium on Engineering Interactive Computing Systems, pp. 63–68, Lisbon, Portugal, 26-29 June 2017
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