Room Occupancy Estimation
Donated on 8/15/2023
Data set for estimating the precise number of occupants in a room using multiple non-intrusive environmental sensors like temperature, light, sound, CO2 and PIR.
Dataset Characteristics
Multivariate, Time-Series
Subject Area
Computer Science
Associated Tasks
Classification
Feature Type
Real
# Instances
10129
# Features
18
Dataset Information
Additional Information
The experimental testbed for occupancy estimation was deployed in a 6m x 4.6m room. The setup consisted of 7 sensor nodes and one edge node in a star configuration with the sensor nodes transmitting data to the edge every 30s using wireless transceivers. No HVAC systems were in use while the dataset was being collected. Five different types of non-intrusive sensors were used in this experiment: temperature, light, sound, CO2 and digital passive infrared (PIR). The CO2, sound and PIR sensors needed manual calibration. For the CO2 sensor, zero-point calibration was manually done before its first use by keeping it in a clean environment for over 20 minutes and then pulling the calibration pin (HD pin) low for over 7s. The sound sensor is essentially a microphone with a variable-gain analog amplifier attached to it. Therefore, the output of this sensor is analog which is read by the microcontroller’s ADC in volts. The potentiometer tied to the gain of the amplifier was adjusted to ensure the highest sensitivity. The PIR sensor has two trimpots: one to tweak the sensitivity and the other to tweak the time for which the output stays high after detecting motion. Both of these were adjusted to the highest values. Sensor nodes S1-S4 consisted of temperature, light and sound sensors, S5 had a CO2 sensor and S6 and S7 had one PIR sensor each that were deployed on the ceiling ledges at an angle that maximized the sensor’s field of view for motion detection. The data was collected for a period of 4 days in a controlled manner with the occupancy in the room varying between 0 and 3 people. The ground truth of the occupancy count in the room was noted manually. Please refer to our publications for more details.
Has Missing Values?
No
Introductory Paper
By A. Singh, Vivek Jain, S. Chaudhari, F. Kraemer, S. Werner, V. Garg. 2018
Published in 2018 IEEE Globecom Workshops (GC Wkshps)
Variables Table
| Variable Name | Role | Type | Description | Units | Missing Values |
|---|---|---|---|---|---|
| Date | Feature | Date | YYYY/MM/DD | no | |
| Time | Feature | Date | HH:MM:SS | no | |
| S1_Temp | Feature | Continuous | C | no | |
| S2_Temp | Feature | Continuous | C | no | |
| S3_Temp | Feature | Continuous | C | no | |
| S4_Temp | Feature | Continuous | C | no | |
| S1_Light | Feature | Integer | Lux | no | |
| S2_Light | Feature | Integer | Lux | no | |
| S3_Light | Feature | Integer | Lux | no | |
| S4_Light | Feature | Integer | Lux | no |
0 to 10 of 19
Additional Variable Information
Date: YYYY/MM/DD Time: HH:MM:SS Temperature: In degree Celsius Light: In Lux Sound: In Volts (amplifier output read by ADC) CO2: In PPM CO2 Slope: Slope of CO2 values taken in a sliding window PIR: Binary value conveying motion detection Room_Occupancy_Count: Ground Truth
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pip install ucimlrepo
from ucimlrepo import fetch_ucirepo # fetch dataset room_occupancy_estimation = fetch_ucirepo(id=864) # data (as pandas dataframes) X = room_occupancy_estimation.data.features y = room_occupancy_estimation.data.targets # metadata print(room_occupancy_estimation.metadata) # variable information print(room_occupancy_estimation.variables)
Singh,Adarsh Pal and Chaudhari,Sachin. (2023). Room Occupancy Estimation. UCI Machine Learning Repository. https://doi.org/10.24432/C5P605.
@misc{misc_room_occupancy_estimation_864,
author = {Singh,Adarsh Pal and Chaudhari,Sachin},
title = {{Room Occupancy Estimation}},
year = {2023},
howpublished = {UCI Machine Learning Repository},
note = {{DOI}: https://doi.org/10.24432/C5P605}
}Keywords
Creators
Adarsh Pal Singh
adarshpal.singh@alumni.iiit.ac.in
IIIT
Sachin Chaudhari
sachin.c@iiit.ac.in
IIIT
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.