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Hypnogram from accelerometer data

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This article documents creation of a hypnogram (http://en.wikipedia.org/wiki/Hypnogram) using purely sleep activity data recorded by the MonBaby (http://mondevices.com/monbaby) sensor. The subject was yours truly and the data measurements were done around Jan 2014. Typically hypnograms are done using EEG, this is an attempt to do it using 3D accelerometer signal. Below is a hypnogram of normal, healthy adult copied from the Wikipedia article. Methodology was to convert raw output of accelerometer into a proxy signal that represents activity – first graph above on the first figure. Second graph is aggregation of number of abrupt movements plotted over time, time interval for aggregation is 30 minutes. The interval divided into 5 quantiles that represent 5 stages of sleep. They are represented as horizontal dotted lines on a second graph. Anything above the top line is Wake state, everything below bottom, deep sleep. Not sure if this method can detect REM state, but it will be among lowe
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Feature engineering for breath rate detection

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 Feature engineering for breathing rate detection Continuous detection of breathing or respiratory monitoring is important for many brunches of medicine, including detection of sleep apnea, for respiratory rehabilitation and detection of abnormal breathing patterns. Wearable technology is able to provide continuous and convenient way to measure noninvasive breathing rate measurements and monitoring. It becomes important to develop precise algorithms and machine learning techniques that work on data produced by wearable technology. It processes respiratory raw signals and interprets them into useful information. In this paper we will discuss the following components of the breathing rate detections: feature engineering to process signals from 3D axis accelerometer that are used by breathing detection algorithms. results from using extracted features on actual data one of the proposed algorithms for breathing detection Note that the breathing detection algorithm we will discuss here comp
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