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MNFL: the monitoring and notification flow language for assistive monitoring

Published: 28 January 2012 Publication History

Abstract

Assistive monitoring analyzes data from sensors and cameras to detect situations of interest, and notifies appropriate persons in response. Customization of assistive technology by end-users is necessary for technology adoption and retention. We introduce MNFL, the Monitoring and Notification Flow Language, developed over the past several years to allow lay people without programming experience, but with some technical acumen, to effectively program customized monitoring and notification systems. MNFL is a graphical flow language having intuitive yet sufficiently powerful execution semantics and built-in constructs for assistive monitoring. We describe the language's semantics and built-in constructs, demonstrate the language's use for customizing several common assistive monitoring tasks, and provide results of initial usability trials showing that lay people with almost no training on MNFL can more than 50% of the time and in just a few minutes select and connect the right 1-2 blocks to complete basic applications that have 4-5 blocks total.

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Cited By

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  • (2022)Context-Aware End-User Development ReviewApplied Sciences10.3390/app1201047912:1(479)Online publication date: 4-Jan-2022
  • (2019)Using Policy Refinement to Assist IoT Device Management in Smart HospitalsComputational Science and Its Applications – ICCSA 201910.1007/978-3-030-24308-1_51(645-657)Online publication date: 29-Jun-2019
  • (2013)Accurate and Efficient Algorithms that Adapt to Privacy-Enhanced Video for Improved Assistive MonitoringACM Transactions on Management Information Systems (TMIS)10.1145/2523025.25230264:3(1-16)Online publication date: 1-Oct-2013
  • Show More Cited By

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cover image ACM Conferences
IHI '12: Proceedings of the 2nd ACM SIGHIT International Health Informatics Symposium
January 2012
914 pages
ISBN:9781450307819
DOI:10.1145/2110363
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 28 January 2012

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Author Tags

  1. assistive monitoring
  2. embedded systems
  3. end-user programming
  4. monitoring
  5. notification
  6. sensors
  7. smart homes
  8. telehealth
  9. telemonitoring
  10. ubiquitous systems

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IHI '12
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IHI '12: ACM International Health Informatics Symposium
January 28 - 30, 2012
Florida, Miami, USA

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Cited By

View all
  • (2022)Context-Aware End-User Development ReviewApplied Sciences10.3390/app1201047912:1(479)Online publication date: 4-Jan-2022
  • (2019)Using Policy Refinement to Assist IoT Device Management in Smart HospitalsComputational Science and Its Applications – ICCSA 201910.1007/978-3-030-24308-1_51(645-657)Online publication date: 29-Jun-2019
  • (2013)Accurate and Efficient Algorithms that Adapt to Privacy-Enhanced Video for Improved Assistive MonitoringACM Transactions on Management Information Systems (TMIS)10.1145/2523025.25230264:3(1-16)Online publication date: 1-Oct-2013
  • (2013)Automated In-Home Assistive Monitoring with Privacy-Enhanced VideoProceedings of the 2013 IEEE International Conference on Healthcare Informatics10.1109/ICHI.2013.29(192-198)Online publication date: 9-Sep-2013

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