Abstract:
Sleep quantity affects an individual’s personal health.
The gold standard of measuring sleep and diagnosing sleep
disorders is Polysomnography (PSG). Although PSG is accurate,
it is expensive and it lacks portability. A number of wearable
devices with embedded sensors have emerged in the recent past
as an alternative to PSG for regular sleep monitoring directly
by the user. These devices are intrusive and cause discomfort
besides being expensive. In this work, we present an algorithm
to detect sleep using a smartphone with the help of its inbuilt
accelerometer sensor. We present three different approaches to
classify raw acceleration data into two states - Sleep and Wake. In
the first approach, we take an equation from Kushida’s algorithm
to process accelerometer data. Henceforth, we call it Kushida’s
equation. While the second is based on statistical functions,
the third is based on Hidden Markov Model (HMM) training.
Although all the three approaches are suitable for a phone’s
resources, each approach demands different amount of resources.
While Kushida’s equation-based approach demands the least, the
HMM training-based approach demands the maximum.
We collected data from mobile phone’s accelerometer for four
subjects for twelve days each. We compare accuracy of sleep
detection using each of the three approaches with that of Zeo
sensor, which is based on Electroencephalogram (EEG) sensor to
detect sleep. EEG is an important modality in PSG. We find that
HMM training-based approach is as much as 84% accurate. It is
15% more accurate as compared to Kushida’s equation-based
approach and 10% more accurate as compared to statistical
method-based approach. In order to concisely represent the sleep
quality of people, we model their sleep data using HMM. We
present an analysis to find out a tradeoff between the amount
of training data and the accuracy provided in the modeling
of sleep. We find that six days of sleep data is sufficient for
accurate modeling. We compare accuracy of our HMM trainingbased
algorithm with a representative third party app SleepTime
available from Google Play Store for Android. We find that the
detection done using HMM approach is closer to that done by
Zeo by 13% as compared to the third party Android application
SleepTime. We show that our HMM training-based approach is
efficient as it takes less than ten seconds to get executed on Moto
G Android phone.