Smartphone Dataset for Human Activity Recognition (HAR) in Ambient Assisted Living (AAL)
Donated on 3/8/2016
This data is an addition to an existing dataset on UCI. We collected more data to improve the accuracy of our human activity recognition algorithms applied in the domain of Ambient Assisted Living.
Dataset Characteristics
Time-Series
Subject Area
Computer Science
Associated Tasks
Classification
Feature Type
Real
# Instances
5744
# Features
-
Dataset Information
Additional Information
This dataset is an addition to the dataset at https://archive.ics.uci.edu/ml/datasets/Human+Activity+Recognition+Using+Smartphones We collected more dataset to improve the accuracy of our HAR algorithms applied in a Social connectedness experiment in the domain of Ambient Assisted Living. The dataset was collected from the in-built accelerometer and gyroscope of a smartphone worn around the waist of participants. See waist_mounted_phone.PNG. The data was collected from 30 participants within the age group of 22-79 years. Each activity (standing, sitting, laying, walking, walking upstairs, walking downstairs) was performed for 60secs and the 3-axial linear acceleration and 3-axial angular velocity were collected at a constant rate of 50Hz.
Has Missing Values?
No
Variables Table
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Additional Variable Information
For each record in the dataset it is provided: - Triaxial acceleration from the accelerometer (total acceleration). Filenames: final_acc_train.txt, final_acc_test.txt - Triaxial Angular velocity from the gyroscope. Filenames: final_gyro_train.txt, final_gyro_test.txt - A 561-feature vector with time and frequency domain variables (extracted from the triaxial data) Filenames: final_X_train.txt, final_X_test.txt For more information about the features extracted see (features.txt and features_info.txt) - The corresponding activity labels. Filenames: final_y_train.txt and final_y_test.txt.
Dataset Files
| File | Size |
|---|---|
| dataset_uci/final_X_train.txt | 19.8 MB |
| dataset_uci/final_gyro_train.txt | 7.4 MB |
| dataset_uci/final_X_test.txt | 6.9 MB |
| dataset_uci/final_acc_train.txt | 6.3 MB |
| dataset_uci/final_gyro_test.txt | 2.6 MB |
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pip install ucimlrepo
from ucimlrepo import fetch_ucirepo # fetch dataset smartphone_dataset_for_human_activity_recognition_har_in_ambient_assisted_living_aal = fetch_ucirepo(id=364) # data (as pandas dataframes) X = smartphone_dataset_for_human_activity_recognition_har_in_ambient_assisted_living_aal.data.features y = smartphone_dataset_for_human_activity_recognition_har_in_ambient_assisted_living_aal.data.targets # metadata print(smartphone_dataset_for_human_activity_recognition_har_in_ambient_assisted_living_aal.metadata) # variable information print(smartphone_dataset_for_human_activity_recognition_har_in_ambient_assisted_living_aal.variables)
Davis, K. & Owusu, E. (2016). Smartphone Dataset for Human Activity Recognition (HAR) in Ambient Assisted Living (AAL) [Dataset]. UCI Machine Learning Repository. https://doi.org/10.24432/C5P597.
Creators
Kadian Davis
Evans Owusu
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.