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An Architecture to support Wearables in Education and wellbeing

This paper proposes an architecture that utilizes wearables and other devices with sensors to support education and wellbeing. It explores the use of emotional assessment and IoT-connected devices to address the needs of students at risk of dropout and older individuals with Alzheimer's. The proposed solution includes a local system and a remote reasoning system, both leveraging data mining and machine learning techniques. Promising results have been achieved and future work will focus on expanding the range of available data sources and enhancing the remote reasoning system.

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An Architecture to support Wearables in Education and wellbeing

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  1. An Architecture to support Wearables in Education and wellbeing Fernando Luís-Ferreira, Andreia Artifice, Gary McManus, Joao Sarraipa, Fernando Luís Ferreira flf@uninova.pt Portugal CELDA 2017 18 – 21 October 2017, Vilamoura, Portugal

  2. Background Observation Considerations - I • Technological devices help extending a person’s sensorial experience • Wearables and other gadgets are full of sensors • Accelerometers, cameras, physiological sensors, etc. • Those are available and have wide range of applications • It can be configured, adapted and used for the most diverse applications and goals

  3. Background Observation Considerations – II • Emotional assessment is a useful tool • Emotional states (E.S.) can be inferred • Physiological measurements give clues about E.S. • Emotions have impact on many (all?) cognitive processes • Emotional assessment is a valuable tool on: • Evaluate students motivation and engagement on learning activities • Evaluate elder mental states • Monitor health risks from young to elder people

  4. Background Observation Considerations – III • With the emergence of IoT connected devices are everywhere • Devices are used for fitness • For health monitoring • For sports improvement • But they can also prevent students dropout, and • They can be used for safety of Alzheimer patients

  5. Background Observations -> to research question Can we use the same pervasive devices and solutions to address both • Students at risk of dropout (ACACIA Project)? and • Older persons with Alzheimer (CARELINK Project)?

  6. Hypothesis Would it be Possible to come up with an architectural solution that help students and elder people at risk?

  7. OurProposal - I We propose: an architecture that have multiple finalities The proposed architecture can be used from coaching of students and elder assistance on lifelong learning activities while ensuring health safety and security.

  8. OurProposal

  9. Proposed Solution - I Two approaches are considered: • A Local System (L.S.) – that captures information from local sensors, either in smarphones or connected (e.g. wi-fi, bluetooth). • Remote Reasoning System (R.R.S) – Accessed by web-services supported by the cloud.

  10. Proposed Solution - II Local System Uses known algorithms over heartrate to provide clues about a person’s Emotional State Not aiming emotions but seeking for responsiveness to external stimuli. - Important to evaluate student’s attention and boredom. – Important to evaluate elder’s cognitive activity.

  11. Proposed Solution - III Remote Reasoning System Accessed by web services uses data from local sensors and existing knowledge/recommendation - Comprises data mining, data extraction and sensory fusion - Are supported by machine learning, deep learning or other A.I. related approaches

  12. Conclusions The proposed solution is based on results from ACACIA project since CARELINK project is in early stages. Results already achieved were tested in CADEP centers in South America (CARELINK project). Promising results from analysis of Heartrate variation along with other measurements (e.g. Galvanic skin response, blood pressure).

  13. Future Work • Will include a wider usage of available data sources (e.g. local cameras, sound capture, text and type emotional detection) • Extend the reasoning over remote advanced and evolutionary systems

  14. Thank you! Questions?? Fernando Luís Ferreira flf@uninova.pt http://carelink-aal.org http://acacia.digital

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