PARIS: Personalized Activity Recommendation for Improving Sleep Quality
Meghna Singh, Saksham Goel, Abhiraj Mohan, and Jaideep Srivastava

TL;DR
This paper presents PARIS, a machine learning-based system that personalizes activity recommendations to improve sleep quality by analyzing wearable device data and considering individual lifestyle factors.
Contribution
It introduces a novel approach combining time series clustering and personalized activity recipes to enhance sleep quality using wearable data.
Findings
Effective clustering of activity modes correlates with sleep quality.
Personalized activity recommendations improve sleep outcomes.
System adapts to individual lifestyle constraints.
Abstract
The quality of sleep has a deep impact on people's physical and mental health. People with insufficient sleep are more likely to report physical and mental distress, activity limitation, anxiety, and pain. Moreover, in the past few years, there has been an explosion of applications and devices for activity monitoring and health tracking. Signals collected from these wearable devices can be used to study and improve sleep quality. In this paper, we utilize the relationship between physical activity and sleep quality to find ways of assisting people improve their sleep using machine learning techniques. People usually have several behavior modes that their bio-functions can be divided into. Performing time series clustering on activity data, we find cluster centers that would correlate to the most evident behavior modes for a specific subject. Activity recipes are then generated for good…
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Taxonomy
TopicsSleep and related disorders · Context-Aware Activity Recognition Systems · Physical Activity and Health
