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<div id='write' class = 'is-node'><h1><a name='header-n0' class='md-header-anchor '></a>Motion Artifact Detection in Wrist-Measured Electrodermal Activity Data</h1><p>This repository contains the Python code and data to reproduce the experiments in our <a href='https://arxiv.org/abs/1707.08287'>ISWC 2017 paper "Unsupervised Motion Artifact Detection in Wrist-Measured Electrodermal Activity Data"</a>. We evaluate 5 supervised and 3 unsupervised machine learning algorithms for motion artifact (MA) detection on electrodermal activity (EDA) and 3-axis accelerometer data. Our experiments are performed on two publicly available data sets:</p><ul><li>UT Dallas Stress (UTD) Data: 20 college students performed a sequence of tasks subjecting them to physical, cognitive, and emotional stress in a lab environment. It contains about 13 hours of data total (Birjandtalab et al., 2016). We include only the pre-processed feature matrices. The raw data is available at <a href='http://www.utdallas.edu/~nourani/Bioinformatics/Biosensor_Data/'>http://www.utdallas.edu/~nourani/Bioinformatics/Biosensor_Data/</a></li><li>Alan Walks Wales (AWW) Data: collected by Alan Dix while he walked around Wales from mid-April to July 2013. We extracted segments of data over 10 different days resulting in 10 hours of data in total (5 hours walking, 5 hours resting). We include both the feature matrices and the raw data for the 10 hours we extracted. The raw data over Alan's entire journey is available at <a href='http://alanwalks.wales/data/' target='_blank' >http://alanwalks.wales/data/</a></li></ul><p>Refer to the <a href='https://arxiv.org/abs/1707.08287'>paper</a> for more details. The code requires the <a href='http://scikit-learn.org/'>scikit-learn</a> Python package.</p><h2><a name='header-n13' class='md-header-anchor '></a>Contents</h2><p>In the root directory:</p><ul><li><code>Feature Details.txt</code>: Description of all features constructed from EDA and accelerometer data.</li><li><code>FeatureMatrix_AWW.py</code>: Script to compute feature matrix from Alan Walks Wales raw data. This script does not need to be executed to reproduce our experiments because we also include the pre-processed feature matrix.</li><li><code>LICENSE.txt</code>: License for this software.</li><li><code>MachineLearning_InSample7_AWW.py</code>: Script to perform in-sample MA prediction (using leave-one-segment-out cross-validation) on AWW resting and walking data separately using all algorithms aside from the multi-layer Perceptron (MLP).</li><li><code>MachineLearning_InSample7_UTD.py</code>: Script to perform in-sample MA prediction (using leave-one-subject-out cross-validation) on UTD data using all algorithms aside from the multi-layer Perceptron (MLP).</li><li><code>MachineLearning_OutofSample7.py</code>: Script to perform out-of-sample MA prediction (both train on AWW/test on UTD and train on UTD/test on AWW) using all algorithms aside from the multi-layer Perceptron (MLP).</li><li><code>MLP_inSample_AWW.py</code>: Script to perform in-sample MA prediction (using leave-one-segment-out cross-validation) on AWW resting and walking data separately using the multi-layer Perceptron (MLP). This is the most time-consuming algorithm so we separated it into its own script.</li><li><code>MLP_InSample_UTD.py</code>: Script to perform in-sample MA prediction (using leave-one-subject-out cross-validation) on UTD data using the multi-layer Perceptron (MLP).</li><li><code>MLP_OutSample.py</code>: Script to perform out-of-sample MA prediction (both train on AWW/test on UTD and train on UTD/test on AWW) using the multi-layer Perceptron (MLP).</li></ul><p>Each subdirectory (either <code>AlanWalksWales</code> or <code>UTDallas</code>) contains the data files. For example, for the AWW resting data, the following files are included:</p><ul><li><code>AWW_rest_acc.csv</code>: Feature matrix for AWW resting data using only accelerometer features. Each row is a 5-second time window, and each column is a feature.</li><li><code>AWW_rest_all.csv</code>: Feature matrix for AWW resting data using all (EDA and accelerometer) features</li><li><code>AWW_rest_eda.csv</code>: Feature matrix for AWW resting data using only EDA features</li><li><code>AWW_rest_groups.csv</code>: Mapping of 5-second time windows to groups for leave-one-group-out cross-validation. The value on the <em>i</em>th row indicates which group (extracted segment of data) the <em>i</em>th second time window belongs to.</li><li><code>AWW_rest_label_All3</code>.csv: Labels of each time window as clean (0) or MA (1) by 3 EDA experts.</li><li><code>AWW_rest_label.csv</code>: Majority vote over 3 EDA expert labels for each time window.</li></ul><p>Each file is also available for the AWW walking data and for the UTD data. For the AWW data, we also include the raw CSV files, e.g. <code>2013_05_14_40mins_eating.csv</code>, from the Affectiva Q sensor, as well as the CSV files exported from <a href='http://eda-explorer.media.mit.edu/'>EDA Explorer</a> containing our expert labels, e.g. <code>2013_05_14_40mins_eating_Epochs.csv</code> in the subdirectory <code>Raw</code>.</p><h2><a name='header-n67' class='md-header-anchor '></a>References</h2><p>Birjandtalab, J., Cogan, D., Pouyan, M. B., & Nourani, M. (2016). A non-EEG biosignals dataset for assessment and visualization of neurological status. In Proceedings of the IEEE International Workshop on Signal Processing Systems (pp. 110–114). IEEE. <a href='https://doi.org/10.1109/SiPS.2016.27' target='_blank' >https://doi.org/10.1109/SiPS.2016.27</a></p><p>Zhang, Y., Haghdan, M., & Xu, K. S. (2017). Unsupervised motion artifact detection in wrist-measured electrodermal activity data. In Proceedings of the 21st International Symposium on Wearable Computers (to appear). Retrieved from <a href='http://arxiv.org/abs/1707.08287' target='_blank' >http://arxiv.org/abs/1707.08287</a></p><h2><a name='header-n72' class='md-header-anchor '></a>License</h2><p>Distributed with a BSD license; see <code>LICENSE.txt</code></p></div>
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