Heterogeneity Activity Recognition
Donated on 10/25/2015
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.) in real-world contexts; specifically, the dataset is gathered with a variety of different device models and use-scenarios, in order to reflect sensing heterogeneities to be expected in real deployments.
Dataset Characteristics
Multivariate, Time-Series
Subject Area
Computer Science
Associated Tasks
Classification, Clustering
Feature Type
Real
# Instances
43930257
# Features
-
Dataset Information
Additional Information
The Heterogeneity Dataset for Human Activity Recognition from Smartphone and Smartwatch sensors consists of two datasets devised to investigate sensor heterogeneities' impacts on human activity recognition algorithms (classification, automatic data segmentation, sensor fusion, feature extraction, etc). The datasets were used for the results and analyses produced in [1]. Activity recognition data set The dataset contains the readings of two motion sensors commonly found in smartphones. Reading were recorded while users executed activities scripted in no specific order carrying smartwatches and smartphones. Activities: ‘Biking’, ‘Sitting’, ‘Standing’, ‘Walking’, ‘Stair Up’ and ‘Stair down’. Sensors: Sensors: Two embedded sensors, i.e., Accelerometer and Gyroscope, sampled at the highest frequency the respective device allows. Devices: 4 smartwatches (2 LG watches, 2 Samsung Galaxy Gears) 8 smartphones (2 Samsung Galaxy S3 mini, 2 Samsung Galaxy S3, 2 LG Nexus 4, 2 Samsung Galaxy S+) Recordings: 9 users Recording scenario =============== The activity recognition environment and scenario has been designed to generate many activity primitives, yet in a realistic manner. Users took 2 different routes for the biking and walking, and 2 different set of stairs were used for the stairs up and down. Still experiment data set =================== Accelerometer recordings as above but with devices lying still, in 6 different orientations. Devices used comprise 31 smartphones, 4 smartwatches and 1 tablet, representing 13 different models from 4 manufacturers, running variants of Android and iOS.
Has Missing Values?
Yes
Variables Table
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Additional Variable Information
Activity recognition data set accelerometer Samples ------------ The Phones_accelerometer.csv contains all smartphone accelerometer samples from all devices and users. The csv file consist of the following columns: 'Index', 'Arrival_Time', 'Creation_Time', 'x', 'y', 'z', 'User', 'Model', 'Device', 'gt' All samples from all the experiments is a row in the file containing each column value. ------------- Groundtruths -------------------- The null class is defined as null in the gt (groundtruth) column, whereas the rest of the classes can be seen in the column. ------------- Devices -------------------------- the phones from the still experiment which has been used for activity recognition is the following: ‘it-116', 'it-133', 'it-108', 'it-103','it-123','3Renault-AH', 'no-name/LG-Nexus4','G-Watch' The device numbering used in the data set is: LG-Nexus 4 'nexus4_1' 'nexus4_2' Saumsung Galaxy S3 's3_1' 's3_2’ Samsung Galaxy S3 min: 's3mini_1' 's3mini_2' Samsung Galaxy S+: 'samsungold_1' 'samsungold_2' Still experiment data set This is the Heterogeneity Dataset for Human Activity Recognition, and contains all the samples from the static still experiment. Where the phones where place in the 6 different possible orientation. The data set is structured in the following way: ------------- Static Accelerometer Samples ------------ Each specific device is located in the following way: Orientation/[Web Link] Where the 6 different orientations can be either one of the following: Phoneonback,Phoneonbottom,Phoneonfront,Phoneonleft,Phoneonright,Phoneontop For example to get the samples from the device named 3Renault-AH of the model Samsung-Galaxy-S3 Mini when laying static on the back we get the following structure: Phoneonback/3Renault-AH/Samsung-Galaxy-S3 Mini.csv. Each CSV file consist of 6 columns creation time, sensor time,arrival time,x,y,z. The six axes from the accelerometer is the x,y,z columns.
Dataset Files
| File | Size |
|---|---|
| Activity recognition exp.zip | 741.1 MB |
| Still exp.zip | 42.9 MB |
Reviews
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pip install ucimlrepo
from ucimlrepo import fetch_ucirepo # fetch dataset heterogeneity_activity_recognition = fetch_ucirepo(id=344) # data (as pandas dataframes) X = heterogeneity_activity_recognition.data.features y = heterogeneity_activity_recognition.data.targets # metadata print(heterogeneity_activity_recognition.metadata) # variable information print(heterogeneity_activity_recognition.variables)
Blunck, H., Bhattacharya, S., Prentow, T., Kjrgaard, M., & Dey, A. (2015). Heterogeneity Activity Recognition [Dataset]. UCI Machine Learning Repository. https://doi.org/10.24432/C5689X.
Creators
Henrik Blunck
Sourav Bhattacharya
Thor Prentow
Mikkel Kjrgaard
Anind Dey
DOI
License
This dataset is licensed under a Creative Commons Attribution 4.0 International (CC BY 4.0) license.
This allows for the sharing and adaptation of the datasets for any purpose, provided that the appropriate credit is given.