Assessing the availability of users to engage in just-in-time intervention in the natural environment

Hillol Sarker; Moushumi Sharmin; Amin Ahsan Ali; Md Mahbubur Rahman; Rummana Bari; Syed Monowar Hossain; Santosh Kumar

doi:10.1145/2632048.2636082

Conference Paper2014Cited by 120

Assessing the availability of users to engage in just-in-time intervention in the natural environment

Hillol Sarker, Moushumi Sharmin, Amin Ahsan Ali, Md Mahbubur Rahman, Rummana Bari, Syed Monowar Hossain, Santosh Kumar

Proceedings of the 2014 ACM international joint conference on pervasive and ubiquitous computing

ACM, pp. 909–920

DOI: 10.1145/2632048.2636082 Open Access URL

Abstract

Wearable wireless sensors for health monitoring are enabling the design and delivery of just-in-time interventions (JITI). Critical to the success of JITI is to time its delivery so that the user is available to be engaged. We take a first step in modeling users' availability by analyzing 2,064 hours of physiological sensor data and 2,717 self-reports collected from 30 participants in a week-long field study. We use delay in responding to a prompt to objectively measure availability. We compute 99 features and identify 30 as most discriminating to train a machine learning model for predicting availability. We find that location, affect, activity type, stress, time, and day of the week, play significant roles in predicting availability. We find that users are least available at work and during driving, and most available when walking outside. Our model finally achieves an accuracy of 74.7% in 10-fold cross-validation and 77.9% with leave-one-subject-out.

Keywords

Wearable computerComputer sciencePsychological interventionAffect (linguistics)Field (mathematics)Human–computer interactionDuration (music)Wearable technologyWirelessWork (physics)Intervention (counseling)Real-time computing

CCDS Authors

Amin Ahsan Ali, PhD

Director, AI/ML Wing

Professor

Department of Computer Science and Engineering

Independent University, Bangladesh

Research Interests

Artificial Intelligence, Machine Learning

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Abstract

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