This tutorial teaches how to develop an activity recognition algorithm in Matlab. It provides several reusable abstractions to load process segment and process data using signal processing and machine learning algorithms commonly used in wearable computing.
In this example application, we analyse the gait of a cow to detect lameness. We use a sample data set of a healthy cow and another data set from a lame cow. The code I provide segments the individual strides using a peak detector and trains a machine learning classifier to classify between normal and abnormal strides.
- install Matlab (this tutorial was developed with version 2018b).
- in Matlab,
addpath(genpath('./')) - in Matlab, run
main.m. - this code uses the mRMR library for feature selection. If you get an error 'estpab function not found', then you need to:
cd libraries/mRMR_0.9/mi/
mex -setup C++
makeosmex
Note: I suggest you to set a breakpoint, run the code line by line and see the runtime values by hovering the mouse on top of each variable.
- A toolkit to develop wearable device applications: https://github.com/avenix/WDK
- Andreas Bulling's tutorial on activity recognition: https://dl.acm.org/citation.cfm?id=2499621
- Wearable device applications:
My name is Juan Haladjian. I developed this tutorial for my students at the Technical University of Munich. Feel free to contact me with questions or feature requests and to reuse and redistribute this software as you wish. The project is under an MIT license. You are welcome to use the code in this repository, extend it and redistribute it for any purpose, as long as you give credit for it by copying the LICENSE.txt file to any copy of this software.
Email: haladjia@in.tum.de
Website: http://in.tum.de/~haladjia