User Identification From Walking Activity
Donated on 3/1/2014
The dataset collects data from an Android smartphone positioned in the chest pocket from 22 participants walking in the wild over a predefined path.
Dataset Characteristics
Univariate, Sequential, Time-Series
Subject Area
Other
Associated Tasks
Classification, Clustering
Feature Type
Real
# Instances
-
# Features
-
Dataset Information
Additional Information
The dataset collects data from an Android smartphone positioned in the chest pocket. Accelerometer Data are collected from 22 participants walking in the wild over a predefined path. The dataset is intended for Activity Recognition research purposes. It provides challenges for identification and authentication of people using motion patterns. --- Sampling frequency of the accelerometer: DELAY_FASTEST with network connections disabled --- Number of Participants: 22 --- Data Format: CSV
Has Missing Values?
No
Variable Information
--- Data are separated by participant --- Each file contains the following information ---- time-step, x acceleration, y acceleration, z acceleration
Dataset Files
| File | Size |
|---|---|
| User Identification From Walking Activity/17.csv | 638.8 KB |
| User Identification From Walking Activity/18.csv | 594.3 KB |
| User Identification From Walking Activity/20.csv | 498.2 KB |
| User Identification From Walking Activity/14.csv | 337.7 KB |
| User Identification From Walking Activity/22.csv | 279.9 KB |
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pip install ucimlrepo
from ucimlrepo import fetch_ucirepo # fetch dataset user_identification_from_walking_activity = fetch_ucirepo(id=286) # data (as pandas dataframes) X = user_identification_from_walking_activity.data.features y = user_identification_from_walking_activity.data.targets # metadata print(user_identification_from_walking_activity.metadata) # variable information print(user_identification_from_walking_activity.variables)
Casale, P. (2012). User Identification From Walking Activity [Dataset]. UCI Machine Learning Repository. https://doi.org/10.24432/C5WC8S.
Creators
Pierluigi Casale
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.