As far as you can, complete the model datasheet. If you have got the data from the internet, you may not have all the information you need, but make sure you include all the information you do have.
- For what purpose was the dataset created?
- Who created the dataset (e.g., which team, research group) and on behalf of which entity (e.g., company, institution, organization)? Who funded the creation of the dataset?
About Dataset
The Human Activity Recognition database was built from the recordings of 30 study participants performing activities of daily living (ADL) while carrying a waist-mounted smartphone with embedded inertial sensors. The objective is to classify activities into one of the six activities performed.
Description of experiment
The experiments have been carried out with a group of 30 volunteers within an age bracket of 19-48 years. Each person performed six activities (WALKING, WALKING_UPSTAIRS, WALKING_DOWNSTAIRS, SITTING, STANDING, LAYING) wearing a smartphone (Samsung Galaxy S II) on the waist. Using its embedded accelerometer and gyroscope, we captured 3-axial linear acceleration and 3-axial angular velocity at a constant rate of 50Hz. The experiments have been video-recorded to label the data manually. The obtained dataset has been randomly partitioned into two sets, where 70% of the volunteers was selected for generating the training data and 30% the test data.
The sensor signals (accelerometer and gyroscope) were pre-processed by applying noise filters and then sampled in fixed-width sliding windows of 2.56 sec and 50% overlap (128 readings/window). The sensor acceleration signal, which has gravitational and body motion components, was separated using a Butterworth low-pass filter into body acceleration and gravity. The gravitational force is assumed to have only low frequency components, therefore a filter with 0.3 Hz cutoff frequency was used. From each window, a vector of features was obtained by calculating variables from the time and frequency domain.
Citation
Davide Anguita, Alessandro Ghio, Luca Oneto, Xavier Parra and Jorge L. Reyes-Ortiz. A Public Domain Dataset for Human Activity Recognition Using Smartphones. 21st European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2013. Bruges, Belgium 24-26 April 2013.
- What do the instances that comprise the dataset represent (e.g., documents, photos, people, countries)?
- How many instances of each type are there?
- Is there any missing data?
- Does the dataset contain data that might be considered confidential (e.g., data that is protected by legal privilege or by doctor–patient confidentiality, data that includes the content of individuals’ non-public communications)?
Attribute information
For each record in the dataset the following is provided:
Triaxial acceleration from the accelerometer (total acceleration) and the estimated body acceleration.
Triaxial Angular velocity from the gyroscope.
A 561-feature vector with time and frequency domain variables.
Its activity label.
An identifier of the subject who carried out the experiment.
Activity Labels are:
In the dataset, Y_labels are represented as numbers from 1 to 6 as their identifiers.
WALKING as 1
WALKING_UPSTAIRS as 2
WALKING_DOWNSTAIRS as 3
SITTING as 4
STANDING as 5
LAYING as 6
Accelerometer and Gyroscope readings are taken from 30 volunteers(referred as subjects) while performing the following 6 Activities.
Walking
WalkingUpstairs
WalkingDownstairs
Standing
Sitting
Lying.
Readings are divided into a window of 2.56 seconds with 50% overlapping.
Accelerometer readings are divided into gravity acceleration and body acceleration readings, which has x,y and z components each.
Gyroscope readings are the measure of angular velocities which has x,y and z components.
Jerk signals are calculated for BodyAcceleration readings.
Fourier Transforms are made on the above time readings to obtain frequency readings.
Now, on all the base signal readings., mean, max, mad, sma, arcoefficient, engerybands,entropy etc., are calculated for each window.
We get a feature vector of 561 features and these features are given in the dataset.
Each window of readings is a datapoint of 561 features.
Original column names: Index(['tBodyAcc-mean()-X', 'tBodyAcc-mean()-Y', 'tBodyAcc-mean()-Z', 'tBodyAcc-std()-X', 'tBodyAcc-std()-Y', 'tBodyAcc-std()-Z', 'tBodyAcc-mad()-X', 'tBodyAcc-mad()-Y', 'tBodyAcc-mad()-Z', 'tBodyAcc-max()-X', ... 'fBodyBodyGyroJerkMag-kurtosis()', 'angle(tBodyAccMean,gravity)', 'angle(tBodyAccJerkMean),gravityMean)', 'angle(tBodyGyroMean,gravityMean)', 'angle(tBodyGyroJerkMean,gravityMean)', 'angle(X,gravityMean)', 'angle(Y,gravityMean)', 'angle(Z,gravityMean)', 'subject', 'Activity'], dtype='object', length=563)
Y_Labels(Encoded)
In the dataset, Y_labels are represented as numbers from 1 to 6 as their identifiers.
WALKING as 1
WALKING_UPSTAIRS as 2
WALKING_DOWNSTAIRS as 3
SITTING as 4
STANDING as 5
LAYING as 6
- How was the data acquired?
- If the data is a sample of a larger subset, what was the sampling strategy?
- Over what time frame was the data collected?
The Human Activity Recognition database was built from the recordings of 30 study participants performing activities of daily living (ADL) while carrying a waist-mounted smartphone with embedded inertial sensors.
- Was any preprocessing/cleaning/labeling of the data done (e.g., discretization or bucketing, tokenization, part-of-speech tagging, SIFT feature extraction, removal of instances, processing of missing values)? If so, please provide a description. If not, you may skip the remaining questions in this section.
- Was the “raw” data saved in addition to the preprocessed/cleaned/labeled data (e.g., to support unanticipated future uses)?
Not known.
- What other tasks could the dataset be used for?
- Is there anything about the composition of the dataset or the way it was collected and preprocessed/cleaned/labeled that might impact future uses? For example, is there anything that a dataset consumer might need to know to avoid uses that could result in unfair treatment of individuals or groups (e.g., stereotyping, quality of service issues) or other risks or harms (e.g., legal risks, financial harms)? If so, please provide a description. Is there anything a dataset consumer could do to mitigate these risks or harms?
- Are there tasks for which the dataset should not be used? If so, please provide a description.
The dataset is only labelled with activity data so has no other known uses.
- How has the dataset already been distributed?
- Is it subject to any copyright or other intellectual property (IP) license, and/or under applicable terms of use (ToU)?
https://www.kaggle.com/datasets/uciml/human-activity-recognition-with-smartphones
License
CC0: Public Domain
- Who maintains the dataset?
The dataset is not maintained.