Single Elder Home Monitoring: Gas and Position
Donated on 5/27/2023
This dataset contains gas and temperature sensors as well as movement infra-red sensors from the monitoring of an elder person living alone in their own home from 2019-11-06 to 2020-02-13. The measurings have a temporal resolution of 20 seconds. The air and gas sensors measure temperature, humidity, CO2, CO and MOX readings. The data from the position sensors are binary; for each room, a 1 means that movement has been detected in that room while a 0 means that the sensor has gone back to baseline. The attached figure represents a simple layout of the monitored home as well as the sensors locations. The dataset also includes 19 days of measurements (from 2020-01-25 to 2020-02-13) where noone was occupying the room (except for an esporadic visit the 2020-01-29 at 15:), as reference data
Dataset Characteristics
Tabular
Subject Area
Engineering
Associated Tasks
Classification
Feature Type
Real
# Instances
444631
# Features
10
Dataset Information
For what purpose was the dataset created?
This dataset was created in order to build a model that could translate the information gathered by a gas array sensor installed in a single elder person living alone into a safety monitoring system. Gas sensors are non-invasive, and with the control data from the movement sensors, the gas data could be used as an indicator of activity anomaly.
Who funded the creation of the dataset?
The European Commission Program Horizon 2020
What do the instances in this dataset represent?
Each instance ir a measure by either the gas and temperature sensors or the movement sensors with its corresponding timestamp.
Are there recommended data splits?
The only recommended split is the reference data, which is already provided in a different file.
Does the dataset contain data that might be considered sensitive in any way?
No.
Was there any data preprocessing performed?
No.
Has Missing Values?
No
Introductory Paper
By Daniel Marín, Joshua Llano, Zouhair Haddi, Alexandre Perera-Lluna, Jordi Fonollosa. 2023
Published in B2SLab, Departament d’Enginyeria de Sistemes, Automàtica i Informàtica Industrial, Universitat Politècnica de Catalunya, Barcelona, 08028, Spain
Variables Table
| Variable Name | Role | Type | Description | Units | Missing Values |
|---|---|---|---|---|---|
| timestamp | Feature | Categorical | no | ||
| temperature | Feature | Continuous | no | ||
| humidity | Feature | Continuous | no | ||
| CO2CosIRValue | Feature | Continuous | no | ||
| CO2MG811Value | Feature | Continuous | no | ||
| MOX1 | Feature | Continuous | no | ||
| MOX2 | Feature | Continuous | no | ||
| MOX3 | Feature | Continuous | no | ||
| MOX4 | Feature | Continuous | no | ||
| COValue | Feature | Continuous | no |
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Dataset Files
| File | Size |
|---|---|
| DB elder monitoring/database_gas.csv | 34.6 MB |
| DB elder monitoring/data_ref_until_2020-02-13.csv | 6.2 MB |
| DB elder monitoring/database_pos.csv | 1.2 MB |
| __MACOSX/._DB elder monitoring | 353 Bytes |
| __MACOSX/DB elder monitoring/._data_ref_until_2020-02-13.csv | 219 Bytes |
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pip install ucimlrepo
from ucimlrepo import fetch_ucirepo # fetch dataset single_elder_home_monitoring_gas_and_position = fetch_ucirepo(id=799) # data (as pandas dataframes) X = single_elder_home_monitoring_gas_and_position.data.features y = single_elder_home_monitoring_gas_and_position.data.targets # metadata print(single_elder_home_monitoring_gas_and_position.metadata) # variable information print(single_elder_home_monitoring_gas_and_position.variables)
Marín López, D., Marín, D., Fonollosa, J., Llano, J., Perera, A., & Haddi, Z. (2023). Single Elder Home Monitoring: Gas and Position [Dataset]. UCI Machine Learning Repository. https://doi.org/10.24432/C5762W.
Keywords
Creators
Daniel Marín López
daniel.marin.lopez@upc.edu
Daniel Marín
daniel.marin.lopez@upc.edu
UPC (CREB)
Jordi Fonollosa
jordi.fonollosa.m@upc.edu
UPC (CREB)
Joshua Llano
joshua.llano@upc.edu
UPC (CREB)
Alexandre Perera
alexandre.perera@upc.edu
UPC (CREB)
Zouhair Haddi
zouhair.haddi@nvision.es
nVision
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.