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2020 Doctoral thesis Open Access OPEN

Personalized Real-time Anomaly Detection and Health Feedback for Older Adults
Parvin P.
The rapid population aging and the availability of sensors and intelligent objects motivate the development of information technology-based healthcare systems that meet the needs of older adults by supporting them to continue their day-to-day activities. These systems collect information regarding the daily activities of the users and potentially help to detect abnormal behaviors. Anomaly detection can subsequently be combined with real-time, continuous and personalized interventions to help older adults actively enjoy a healthy lifestyle. This thesis introduces a system that takes a novel approach to generate personalized health feedback. The proposed system models user's daily behavior in order to detect anomalous behaviors and strategically generates interventions to encourage behaviors conducive to a healthier lifestyle. To this aim, we propose a real-time solution which models the user daily routine using a task model specification and detects relevant contextual events occurred in their life through a Context Server (a middle-ware software). In addition, by a systematic validation through a system that automatically generates wrong sequences of tasks, we show that our algorithm is able to find behavioral deviations from the expected behavior at different times by considering the extended classification of the possible deviations with good accuracy. Later, the system uses a Mamdani-type fuzzy rule-based component to predict the level of intervention needed for each detected anomaly and a sequential decision-making algorithm, Contextual Multi-armed Bandit, to generate suggestions to minimize anomalous behaviour. We describe the system architecture in detail and we provide example implementations for corresponding health feedback.

See at: etd.adm.unipi.it Open Access | ISTI Repository Open Access | CNR ExploRA Open Access


2020 Conference object Open Access OPEN

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
DOI: 10.1145/3411170.3411228

See at: ISTI Repository Open Access | Unknown Repository Restricted | Unknown Repository Restricted | Unknown Repository Restricted | Unknown Repository Restricted | CNR ExploRA Restricted


2019 Article Open Access OPEN

A Personalisation Platform for Older Adults with Mild Cognitive Impairments
Manca M., Parvin P., Paternò F., Santoro C., Zedda E.
The AAL PETAL project has developed a platform for personalising remote assistance of older adults with mild cognitive impairments. The platform is targeted at caregivers without programming knowledge in order to help seniors in their daily activities at home.Source: ERCIM news (2019): 39–40.

See at: ercim-news.ercim.eu Open Access | ISTI Repository Open Access | CNR ExploRA Open Access


2019 Article Restricted

Personalized real-time anomaly detection and health feedback for older adults
Parvin P., Chessa S., Kaptein M., Paterno F.
Rapid population aging and the availability of sensors and intelligent objects motivate the development of healthcare systems; these systems, in turn, meet the needs of older adults by supporting them to accomplish their day-to-day activities. Collecting information regarding older adults daily activity potentially helps to detect abnormal behavior. Anomaly detection can subsequently be combined with real-time, continuous and personalized interventions to help older adults actively enjoy a healthy lifestyle. This paper introduces a system that uses a novel approach to generate personalized health feedback. The proposed system models user's daily behavior in order to detect anomalous behaviors and strategically generates interventions to encourage behaviors conducive to a healthier lifestyle. The system uses a Mamdani-type fuzzy rule-based component to predict the level of intervention needed for each detected anomaly and a sequential decision-making algorithm, Contextual Multi-armed Bandit, to generate suggestions to minimize anomalous behavior. We describe the system's architecture in detail and we provide example implementations for the anomaly detection and corresponding health feedback.Source: Journal of ambient intelligence and smart environments (Print) 11 (2019): 453–469. doi:10.3233/AIS-190536
DOI: 10.3233/AIS-190536
DOI: 10.3233/ais-190536

See at: Journal of Ambient Intelligence and Smart Environments Restricted | Journal of Ambient Intelligence and Smart Environments Restricted | Journal of Ambient Intelligence and Smart Environments Restricted | Journal of Ambient Intelligence and Smart Environments Restricted | CNR ExploRA Restricted | Tilburg University Repository Restricted | Journal of Ambient Intelligence and Smart Environments Restricted | Journal of Ambient Intelligence and Smart Environments Restricted


2018 Article Open Access OPEN

Real-time anomaly detection in elderly behavior with the support of task models
Parvin P., Chessa S., Manca M., Paternò F.
With today's technology, elderly can be supported in living independently in their own homes for a prolonged period of time. Monitoring and analyzing their behavior in order to find possible unusual situation helps to provide the elderly with health warnings at the proper time. Current studies are focusing on the elderly daily activity and the detection of anomalous behaviors aiming to provide the older people with remote support. To this aim, we propose a real-time solution which models the user daily routine using a task model specification and detects relevant contextual events occurred in their life through a context manager. In addition, by a systematic validation through a system that automatically generates wrong sequences of tasks, we show that our algorithm is able to find behavioral deviations from the expected behavior at different times by considering the extended classification of the possible deviations with good accuracy.Source: Proceedings of the ACM on human-computer interaction 2 (2018). doi:10.1145/3229097
DOI: 10.1145/3229097

See at: ISTI Repository Open Access | Proceedings of the ACM on Human-Computer Interaction Restricted | Proceedings of the ACM on Human-Computer Interaction Restricted | dl.acm.org Restricted | Proceedings of the ACM on Human-Computer Interaction Restricted | Proceedings of the ACM on Human-Computer Interaction Restricted | Proceedings of the ACM on Human-Computer Interaction Restricted | Proceedings of the ACM on Human-Computer Interaction Restricted | CNR ExploRA Restricted


2018 Conference object Restricted

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
DOI: 10.1109/IE.2018.00025
DOI: 10.1109/ie.2018.00025

See at: Unknown Repository Restricted | Unknown Repository Restricted | Unknown Repository Restricted | ieeexplore.ieee.org Restricted | CNR ExploRA Restricted | Unknown Repository Restricted


2018 Conference object Open Access OPEN

Real-time anomaly detection in elderly behavior
Parvin P.
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
DOI: 10.1145/3220134.3220145

See at: dl.acm.org Open Access | ISTI Repository Open Access | CNR ExploRA Open Access | Unknown Repository Restricted | Unknown Repository Restricted | Unknown Repository Restricted


2017 Conference object Restricted

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
DOI: 10.1145/3102113.3102128

See at: Unknown Repository Restricted | Unknown Repository Restricted | dl.acm.org Restricted | Unknown Repository Restricted | CNR ExploRA Restricted