Start coding for Bobbi – the open source ECG monitor

. Caption: Download all files related to this post here I was asked to put some basic code examples online to help developers get started with the Totem Bobbi Motion + ECG Monitor. For those not in the know: “The Bobbi” is a very cool, *fully* open source ECG and 9-axis motion sensor with low energy bluetooth (BLE 4.0). It has a MicroSD card slot, and works with an AAA battery. It can give you access to raw data (.csv or .txt files), and the hardware is 100% open source. In other words: you decide how to use it. On this page I will post some scripts and examples that should enable you to start developing quickly. All scripts make use of a basic Bobbi set of data files. An archive with the data and the scripts used in this post can be downloaded here – or via this GitHub repository. I have divided this post into four sections – each illustrating a different take on basic Bobbi data analysis, each making use of a different programming language. The sections are: Bobbi export file format definitions Bobbi meets Python Bobbi meets R Bobbi meets JAVA Bobbi export file format definitions. By default, the Bobbi saves the data it collects from its sensors in three files per session, with an auto incrementing session number preceding a format descriptor. That is, for the duration of a session, the Bobbi would save the following three files: 0-config.csv. 0-sdata.csv. 0-sdataHR.csv. Let’s go over them, one by one: config.csv. This file is quite straightforward – it contains an overview of which sensors are currently enabled and at which rate these sensors are being sampled: The resulting data is saved to ...-sdata.csv files. One little issue is that the data is sampled at 120 hz while the sdata reports the timing in milliseconds (ms). A future firmware patch will resolve this. sdata.csv. This file contains the data of all sensors defined in config.csv, samped at the rate defined in that file. It is a basic CSV type file – the only particularity is that its not comma, but semicolon delimited: Each row contains samples from each of the Bobbi’s sensors at the time in milliseconds since the start of a session as represented by the “ms” column. Specifically, the columns represent: sdataHR.csv. The x-sdataHR.csv file contains just the heartrate column, but sampled at the higher rate of 500Hz. The file is, again, semicolon delimited: Apart from the higher sampling rate, and the fact that this file just contains the heartrate and BPM columns, the file format conforms the the sdata.csv files: Bobbi meets Python. Let’s start out by running some Python code – always a great way to start any data science project! By making use of a basic import script, you are but one step away of applying the code of Paul van Gent’s super intro into ECG data analysis* (see also his related Github repo here) to our Bobbi’s data: View the code on Gist. At the end of the script, I do a quick plot of a subset of the heart rate data: Looks good! Lets see if we can follow Paul’s blog and run some first analyses using a slightly adapted version of heartrate.py – for the code, see here. On importing this file, we can use a few lines of code to see if we are able to do some peak detection: View the code on Gist. Again, looking really good: Of course, when your unfiltered data looks as nice as this, it’s probably time to run some more advanced analyses: Which results in a whole lot of useful heart rate related measures, such as: BPM: 84.4409695074 ibi: 710.555555556 sdnn: 19.1162783712 RR_list: [725.0, 725.0, ... RR_diff: [0.0, 0.0, 41.66666666666663, ... RR_sqdiff: [0.0, 0.0, 1736.111111111108, ... RR_sqdiff: [0.0, 0.0, 1736.111111111108, ... sdnn: 19.1162783712 sdsd: 18.0151737505 rmssd: 27.0947778018 nn20: [41.66666666666663, 58.33333333333337, ... nn50: [58.33333333333337] pnn20: 0.42857142857142855 pnn50: 0.07142857142857142 . . Bobbi meets R. On to some R scripts to enable the statistically inclined Bobbi data analysts to quick-start their development! Again, let’s start by importing the data: . Which nets us the following nice little chart: Which is all fine and dandy, but importing does not equal data analysis – so let’s continue with a 4-level Daubechie wavelet decomposition to do some peak detection, and calculate the average heart rate value for our sample: . Resulting in, among others, the following plot: Bobbi meets JAVA. Python and R are cool – but there are those of us who would like to integrate Bobby with a mobile phone or tablet. In that case it makes more sense to start coding in Java – the programming language that is used in Android, at the time the dominant platform for developing and deploying mobile device apps. So lets first import our data again – making use of, for example, the following simple class (Though, if you are working on an Android app, streaming data over Bluetooth might make more sense. For another time, another blog post, I guess!): By extending this class, we can once again see if we are able to find the average heart rate of our data: Let’s see what happens if we run this code! Yet another method, yet another heart rate! Of course, these are all basic examples – very curious as to all of the cool algorithms, code and apps the Bobbi community will be able to come up with! . . * van Gent, P. (2016). Analyzing a Discrete Heart Rate Signal Using Python. A tech blog about fun things with Python and embedded electronics. Retrieved from: http://www.paulvangent.com/2016/03/15/analyzing-a-discrete-heart-rate-signal-using-python-part-1/

Pavlov

May 2017

Start coding for Bobbi – the open source ECG monitor