Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

PARIS: Personalized Activity Recommendation for Improving Sleep Quality

Meghna Singh, Saksham Goel, Abhiraj Mohan, Jaideep Srivastava

TL;DR

PARIS tackles sleep quality optimization by learning per-person behavior modes from actigraphy data and by deriving sleep-focused activity recipes that guide daily activity. It then uses a continuous recommendation engine that matches real-time activity with these recipes while honoring individual health metadata. The approach identifies two dominant behavior modes per subject on the HCHS/SOL dataset and generates multiple Good Sleep recipes that correlate with improved sleep efficiency in retrospective analysis. This goal-directed framework enables personalized, actionable guidance for sleep health and could extend to other health objectives.

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 sleep quality for each behavior mode within each cluster. These activity recipes are supplied to an activity recommendation engine for suggesting a mix of relaxed to intense activities to subjects during their daily routines. The recommendations are further personalized based on the subjects' lifestyle constraints, i.e. their age, gender, body mass index (BMI), resting heart rate, etc, with the objective of the recommendation being the improvement of that night's quality of sleep. This would in turn serve a longer-term health objective, like lowering heart rate, improving the overall quality of sleep, etc.

PARIS: Personalized Activity Recommendation for Improving Sleep Quality

TL;DR

PARIS tackles sleep quality optimization by learning per-person behavior modes from actigraphy data and by deriving sleep-focused activity recipes that guide daily activity. It then uses a continuous recommendation engine that matches real-time activity with these recipes while honoring individual health metadata. The approach identifies two dominant behavior modes per subject on the HCHS/SOL dataset and generates multiple Good Sleep recipes that correlate with improved sleep efficiency in retrospective analysis. This goal-directed framework enables personalized, actionable guidance for sleep health and could extend to other health objectives.

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 sleep quality for each behavior mode within each cluster. These activity recipes are supplied to an activity recommendation engine for suggesting a mix of relaxed to intense activities to subjects during their daily routines. The recommendations are further personalized based on the subjects' lifestyle constraints, i.e. their age, gender, body mass index (BMI), resting heart rate, etc, with the objective of the recommendation being the improvement of that night's quality of sleep. This would in turn serve a longer-term health objective, like lowering heart rate, improving the overall quality of sleep, etc.

Paper Structure

This paper contains 21 sections, 6 equations, 7 figures, 1 table.

Table of Contents

  1. Introduction
  2. Problem Definition
  3. Behavior Modes
  4. Activity Recipes
  5. Activity Recommendation
  6. Solution Approach
  7. Generating Behavior Modes
  8. Activity Recipes for Good Sleep
  9. Continuous Activity Recommendation Engine
  10. Experiments and Results
  11. HCHS/SOL Dataset
  12. Building Feature Space
  13. Generating Behavior Modes
  14. Activity Recipes for Good Sleep
  15. Discussion
  16. ...and 6 more sections

Figures (7)

  • Figure 1: Activity Recommendation System
  • Figure 2: Cluster centroids for behavior modes using distance metrics Euclidean and Jensen-Shannon (JS)
  • Figure 3: Cluster Composition for behavior modes using distance metrics Euclidean and Jensen-Shannon (JS)
  • Figure 4: Activity recipes for good sleep generated for a specific behavior mode
  • Figure 5: Activities recommended based to a target user based on their activity till time $t_m$
  • ...and 2 more figures