Description

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 setThe 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.

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