A Multi-Sensor Machine Learning Approach to Precision Sleep Tracking for Nightshift Workers

Summary

Sleep is often a challenge for nightshift workers because their work and sleep schedules are inverted. Sleep is commonly measured using actigraphy, which is the standard measure of objective sleep in the general population; however, this method has substantial limitations for nightshift workers because the standard legacy algorithms only correctly identify 50.3% of daytime sleep. This significantly reduces the validity for nightshift workers. The purpose of this study is to test a novel method to expand actigraphy by using 1) a multi-sensor approach that 2) uses machine learning (ML) algorithms to increase the accuracy of detecting daytime sleep.

Description

Eligibility

Age range

18+ years

Sex

All

Healthy volunteers

Yes

Interventions

• Other
  Single-Sensor Tracking (In-Lab)

  In-lab sleep tracking using only raw accelerometer data from a single sensor collected and processed with legacy actigraphy algorithms.

• Other
  Multi-Sensor Sleep Tracking (In-Lab)

  In-lab sleep tracking using raw accelerometer data and additional sensors collected and processed with machine learning.

• Other
  Multi-Sensor Sleep Tracking (At-Home)

  At-home sleep tracking using raw accelerometer data and additional sensors collected and processed with machine learning.

Location