Towards a Benchmark for Wearable Sleep Analysis with Inertial Wrist-worn Sensing Units

Description

The monitoring of sleep by quantifying sleeping time and quality is pivotal in many preventive health care scenarios. A substantial amount of wearable sensing products have been introduced to the market for just this reason, detecting whether the user is either sleeping or awake. Assessing these devices for their accuracy in estimating sleep is a daunting task, as their hardware design tends to be different and many are closed-source systems that have not been clinically tested. In this paper, we present a challenging benchmark dataset from an open source wrist-worn data logger that contains relatively high-frequent (100Hz) 3D inertial data from 42 sleep lab patients, along with their data from clinical polysomnography. We analyse this dataset with two traditional approaches for detecting sleep and wake states and propose a new algorithm specifically for 3D acceleration data, Estimation of Stationary Sleep-segments (ESS). Results show that all three methods generally over-estimate for sleep, with our method performing slightly better (74% overall mean accuracy) than the traditional activity count-based methods.

Fig. 1: Polysomnography assessment of sleep stages (top), raw acceleration data from the wrist sensor (middle) and light sensor readings (bottom) from a 24 year old female patient.

Figure 1 shows a sample from the sleeping lab data set that can be obtained for further studies. The data set consists of timestamped raw acceleration data, light intensity readings and the sleep stages (awake, NoREM1-3, REM). 

Further details can be found in the paper.

Downloads

Download the zip-file which contains the data set and scripts to visualize the data.

Citation / first paper

Marko Borazio, Eugen Berlin, Nagihan Kücükyildiz, Philipp M. Scholl and Kristof Van Laerhoven, "Towards a Benchmark for Wearable Sleep Analysis with Inertial Wrist-worn Sensing Units", ICHI 2014, Verona, Italy, IEEE Press, 2014.

Important note

Disclaimer: This data is property of the Embedded Sensing Systems Group, FB Informatik, TU Darmstadt. You may use this data for scientific, non-commercial purposes, provided that you give credit to the owners when publishing any work based on this data.

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