Toward personalized and context-aware prompting for smartphone-based intervention

Abstract

Intervention strategies can help individuals with cognitive impairment to increase adherence to instructions, independence, and activity engagement and reduce errors on everyday instrumental activities of daily living (IADLs) and caregiver burden. However, to be effective, intervention prompts should be given at a time that does not interrupt other important user activities and is more convenient. In this paper, we propose an intelligent personalized intervention system for smartphones. In our approach, we use context and activity awareness to time prompts when they will most likely be viewed and used. Our result based on real data collected using smartphone motion sensors demonstrate that the proposed approach can detect the time-frame of a user response with an average accuracy of 65% and reduce the inefficiency by 39%, on average, compared to different static time interventions which shows the possibilities and advantages of the proposed system to increase user satisfaction and response rate.

COMPASS: Unsupervised and online clustering of complex human activities from smartphone sensors

“…The great majority of the current solutions are ultimately based on supervised machine learning algorithms that use a data-driven approach to infer the user context. To this aim, various classification algorithms have been studied, including Decision Trees (Fallahzadeh et al, 2016), Support Vector Machines (Li & Chung, 2015), Artificial Neural Networks (Vaizman et al, 2018b), or ensembles of different classifiers trained on specific context information and then combined with a meta-classifier to infer the general user activity (Peng et al, 2019).…”