Literature DB >> 18473085

User-adaptive reminders for home-based medical tasks. A case study.

P Kaushik1, S S Intille, K Larson.   

Abstract

OBJECTIVES: We present a prototype adaptive reminder system for home-based medical tasks. The system consists of a mobile device for reminder presentation and ambient sensors to determine opportune moments for reminder delivery. Our objective was to study interaction with the prototype under naturalistic living conditions and gain insight into factors affecting the long-term acceptability of context-sensitive reminder systems for the home setting.
METHODS: A volunteer participant used the prototype in a residential research facility while adhering to a regimen of simulated medical tasks for ten days. Some reminders were scheduled at fixed times during the day and some were automatically time-shifted based on sensor data. We made a complete video and sensor record of the stay. Finally, the participant commented about his experiences with the system in a debriefing interview.
RESULTS: Based on this case study, including direct observation of individual alert-action sequences, we make four recommendations for designers of context-sensitive adaptive reminder systems. Captured metrics suggest that adaptive reminders led to faster reaction times and were perceived by the participant as being more useful.
CONCLUSIONS: The evaluation of context-sensitive systems that overlap into domestic lives is challenging. We believe that the ideal experiment is to deploy such systems in real homes and assess performance longitudinally. This case study in an instrumented live-in facility is a step toward that long-term goal.

Entities:  

Mesh:

Year:  2008        PMID: 18473085

Source DB:  PubMed          Journal:  Methods Inf Med        ISSN: 0026-1270            Impact factor:   2.176


  9 in total

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Authors:  Emily J Van Etten; Alyssa Weakley; Maureen Schmitter-Edgecombe; Diane Cook
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2.  CASAS: A Smart Home in a Box.

Authors:  Diane J Cook; Aaron S Crandall; Brian L Thomas; Narayanan C Krishnan
Journal:  Computer (Long Beach Calif)       Date:  2013-07       Impact factor: 2.683

3.  Automated activity-aware prompting for activity initiation.

Authors:  Lawrence B Holder; Diane J Cook
Journal:  Gerontechnology       Date:  2013-01-01

4.  Activity discovery and activity recognition: a new partnership.

Authors:  Diane J Cook; Narayanan C Krishnan; Parisa Rashidi
Journal:  IEEE Trans Cybern       Date:  2012-09-27       Impact factor: 11.448

5.  Prompting technologies: A comparison of time-based and context-aware transition-based prompting.

Authors:  Kayela Robertson; Cody Rosasco; Kyle Feuz; Maureen Schmitter-Edgecombe; Diane Cook
Journal:  Technol Health Care       Date:  2015       Impact factor: 1.285

6.  CRAFFT: An Activity Prediction Model based on Bayesian Networks.

Authors:  Ehsan Nazerfard; Diane J Cook
Journal:  J Ambient Intell Humaniz Comput       Date:  2015-04-01

Review 7.  Application of cognitive rehabilitation theory to the development of smart prompting technologies.

Authors:  Adriana M Seelye; Maureen Schmitter-Edgecombe; Barnan Das; Diane J Cook
Journal:  IEEE Rev Biomed Eng       Date:  2012

8.  Learning dictionaries of sparse codes of 3D movements of body joints for real-time human activity understanding.

Authors:  Jin Qi; Zhiyong Yang
Journal:  PLoS One       Date:  2014-12-04       Impact factor: 3.240

9.  Learning Activity Predictors from Sensor Data: Algorithms, Evaluation, and Applications.

Authors:  Bryan Minor; Janardhan Rao Doppa; Diane J Cook
Journal:  IEEE Trans Knowl Data Eng       Date:  2017-09-11       Impact factor: 6.977

  9 in total

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