The Student-Life dataset contains passive and automatic sensing data from the phones of a class of 48 de-identified Dartmouth college students. This dataset is composed by two instances of data, each one corresponding to a different user and summing up to 35 days of fully labelled data. It includes 95 datasets from 3372 subjects with new material being added as researchers make their own data open to the public. Each file contains the samples (by rows) recorded for all sensors (by columns). The sensors were respectively placed on the subject's chest, right wrist and left ankle and attached by using elastic straps (as shown in the figure in attachment). The sensor positioned on the chest also provides 2-lead ECG measurements which are not used for the development of the recognition model but rather collected for future work purposes. 0. 0 Active Events. First I construct the placeholders for the inputs to our computational graph: where inputs_ are the arrays to be fed into the graph, labels_ are opne-hot encoded activities that are beind predicted, keep_prob_ is the keep probability used in dropout regularization and learning_rate_ is used in the Adam optimizer. The 10 sujects have performed 12 different types of activities during the eperiments. Each convolution is followed by a max-pooling operation to reduce the sequence length. 10000 . The data I use for this tutorial is the MHEALTH dataset, which can be downloaded from the UCI Machine Learning Repository. He has a wide range of interests, including image recognition, natural language processing, time-series analaysis and motif dicovery in genomic sequences. Hadoop, MapReduce, MultipleInput, MongoDB. There are about 100,000 rows (on average) for each subject. The Heterogeneity Human Activity Recognition (HHAR) dataset from Smartphones and Smartwatches is a dataset devised to benchmark human activity recognition algorithms (classification, automatic data segmentation, sensor fusion, feature extraction, etc.) The convolutional layers are constructed with the conv1d and max_pooling_1d functions of the layers module of Tensorflow, which provides a high-level, Keras-like implementation of CNNs. 23 different types of signals were recoreded which I will refer to as channels for the rest of this post. Each channel where a measurement was performed is of different nature, which means that they are measured in different units. 2500 . http://archive.ics.uci.edu/ml/datasets/mhealth+dataset. Notice that the first dimensions of inputs_ and labels_ are kept at None, since the model is trained using batches. ; 1.5.3 What if I catch mistakes after my pull request is merged? In a previous blog post, I have outlined several alternatives for a similar, but a simpler problem (see also the references therein). Most of these channels are related to body motion, except two of which are electrodiagram signals from the chest. For various reasons, the deep learning algorithms tend be become difficult to train when the length of the time-series is very long. 50 samples per second), therefore the time difference between each row is 0.02 seconds. Follow their code on GitHub. This is achieved by standardize function in utils.py. cc for EDAV 2020; 1 Instructions. The full code can be accessed in the accompanying Github repository. With a starting length of L time steps, I divide the series into blocks of size block_size yielding about L/block_size of new data instances of shorter length. This book contains community contributions for STAT GR 5702 Fall 2020 at Columbia University All sensing modalities are recorded at a sampling rate of 50 Hz, which is considered sufficient for capturing human activity. Generally, we want to make as much of our code available as possible, especially for published algorithms (see the Datasets page). The MHEALTH (Mobile HEALTH) dataset comprises body motion and vital signs recordings for ten volunteers of diverse profile while performing several physical activities. Access to the copyrighted datasets or privacy considerations. Real . The meaning of each column is detailed next: Column 1: acceleration from the chest sensor (X axis), Column 2: acceleration from the chest sensor (Y axis), Column 3: acceleration from the chest sensor (Z axis), Column 4: electrocardiogram signal (lead 1), Column 5: electrocardiogram signal (lead 2), Column 6: acceleration from the left-ankle sensor (X axis), Column 7: acceleration from the left-ankle sensor (Y axis), Column 8: acceleration from the left-ankle sensor (Z axis), Column 9: gyro from the left-ankle sensor (X axis), Column 10: gyro from the left-ankle sensor (Y axis), Column 11: gyro from the left-ankle sensor (Z axis), Column 12: magnetometer from the left-ankle sensor (X axis), Column 13: magnetometer from the left-ankle sensor (Y axis), Column 14: magnetometer from the left-ankle sensor (Z axis), Column 15: acceleration from the right-lower-arm sensor (X axis), Column 16: acceleration from the right-lower-arm sensor (Y axis), Column 17: acceleration from the right-lower-arm sensor (Z axis), Column 18: gyro from the right-lower-arm sensor (X axis), Column 19: gyro from the right-lower-arm sensor (Y axis), Column 20: gyro from the right-lower-arm sensor (Z axis), Column 21: magnetometer from the right-lower-arm sensor (X axis), Column 22: magnetometer from the right-lower-arm sensor (Y axis), Column 23: magnetometer from the right-lower-arm sensor (Z axis), *Units: Acceleration (m/s^2), gyroscope (deg/s), magnetic field (local), ecg (mV). 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