Activity recognition using wearable physiological measurements
Donated on 12/3/2019
This dataset contains features from Electrocardiogram (ECG), Thoracic Electrical Bioimpedance (TEB) and the Electrodermal Activity (EDA) for activity recognition.
Dataset Characteristics
Multivariate
Subject Area
Health and Medicine
Associated Tasks
Classification
Feature Type
Real
# Instances
4480
# Features
533
Dataset Information
Additional Information
In order to elicit the different activities, we have used a segment documentary called 'Earth' to induce Neutral Activity. In order to elicit emotional activity, we used a set of segments extracted from several validated movies. “American History X' (1998) by Savoy Pictures, “I am legend' (2007) by Warner Bross, 'Life is beautiful' (1997) by Miramax, and “Cannibal Holocaust' (1980) by F.D. Cinematografica. The mental activity was elicited using a set of games based on mental arithmetic and playing the well-known game “Tetris', used several times to elicit mental activity. The designed activity recognition system had to take a decision every 10 s, and each individual generated 28 time slots of each activity (the database is balanced). Thus, the total number of patterns (decisions) for this analysis was 4480, and each class is composed of 1120 different patterns. In the present analysis, we have used four different activities: -Neutral activity, registered during the last 140 s of the first movie (the documentary). As each individual watched each movie twice, there are 280 s for each individual in the database -Emotional activity, registered during the viewing of the last 70 s of the second and third movies (140 s); therefore, we obtained a total of 280 s per individual. -Mental activity, registered during the last 140 s of both games, producing 280 s in total. -Physical activity registered during the last 280 s of the physical activity stage. To elicit physical load the participant had to go up and down the stairs for five minutes. Each attributed was determined using a 40 s window. Measurements were collected from 40 subjects.
Has Missing Values?
No
Variable Information
The first column correspond to the index of the subject. The next 174 attributes are statistics extracted from the ECG signal. The next 151 attributes are features extracted from the TEB signal. The next 104 attributes come from the EDA measured in the arm, and the next 104 ones from the EDA in the hand. The last attribute is the pattern class, that is, the corresponding activity: 1-neutral, 2-emotional, 3-mental and 4-physical.
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pip install ucimlrepo
from ucimlrepo import fetch_ucirepo # fetch dataset activity_recognition_using_wearable_physiological_measurements = fetch_ucirepo(id=552) # data (as pandas dataframes) X = activity_recognition_using_wearable_physiological_measurements.data.features y = activity_recognition_using_wearable_physiological_measurements.data.targets # metadata print(activity_recognition_using_wearable_physiological_measurements.metadata) # variable information print(activity_recognition_using_wearable_physiological_measurements.variables)
Activity recognition using wearable physiological measurements. (2019). UCI Machine Learning Repository. https://doi.org/10.24432/C5RK6V.
@misc{misc_activity_recognition_using_wearable_physiological_measurements_552,
title = {{Activity recognition using wearable physiological measurements}},
year = {2019},
howpublished = {UCI Machine Learning Repository},
note = {{DOI}: https://doi.org/10.24432/C5RK6V}
}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.