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

Machine Learning-Based Occupancy Estimation Using Multivariate Sensor Nodes

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 NameRoleTypeDemographicDescriptionUnitsMissing Values
DateFeatureDateYYYY/MM/DDno
TimeFeatureDateHH:MM:SSno
S1_TempFeatureContinuousCno
S2_TempFeatureContinuousCno
S3_TempFeatureContinuousCno
S4_TempFeatureContinuousCno
S1_LightFeatureIntegerLuxno
S2_LightFeatureIntegerLuxno
S3_LightFeatureIntegerLuxno
S4_LightFeatureIntegerLuxno

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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Keywords

sensor data

Creators

Adarsh Pal Singh

adarshpal.singh@alumni.iiit.ac.in

IIIT

Sachin Chaudhari

sachin.c@iiit.ac.in

IIIT

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