This repository contains the longitidinal experiment data named ENTH.
| # Participants (Sex) | Age range | # Responses/participant | Duration |
|---|---|---|---|
| 17 (10M, 7F) | 21 to 27 | 13 (min), 112(max) | 4 weeks |
@inproceedings{10.1145/3485730.3493693,
author = {Quintana, Matias and Abdelrahman, Mahmoud and Frei, Mario and Tartarini, Federico and Miller, Clayton},
title = {Longitudinal Personal Thermal Comfort Preference Data in the Wild},
year = {2021},
isbn = {9781450390972},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3485730.3493693},
doi = {10.1145/3485730.3493693},
abstract = {Thermal comfort affects the well-being, productivity, and overall satisfaction of building occupants. However, due to economical and practical limitations, the number of longitudinal studies that have been conducted is limited, and only a few of these studies have shared their data publicly. Longitudinal datasets collected indoors are a valuable resource to better understand how people perceive their environment. Moreover, they provide a more realistic scenario to those conducted in thermal chambers. Our objective was to share publicly a longitudinal dataset comprising data collected over a 4-week long experiment. A total of 17 participants completed thermal preferences surveys which accounted for a total of approximately 1400 unique responses across indoor and outdoor 17 spaces. For the whole duration of the study, we monitored environmental variables (e.g., temperature and relative humidity) throughout 3 buildings. Participants completed comfort surveys from the screen of their smartwatches using an open-source application named Cozie. Their indoor location was continuously monitored using a custom-designed smartphone application. Location data were used to time and spatially align environmental measurements to thermal preference responses provided by the participants. Background information of participants, such as physical characteristics and personality traits (satisfaction with life scale, highly sensitive person scale, the Big Five personality traits), was collected using an on-boarding survey administered at the beginning of the experiment. The dataset is available at https://zenodo.org/record/5502441#.YT7xyaARUTs.},
booktitle = {Proceedings of the 19th ACM Conference on Embedded Networked Sensor Systems},
pages = {556–559},
numpages = {4},
keywords = {Datasets, Thermal comfort, Longitudinal experiment, Smart buildings},
location = {Coimbra, Portugal},
series = {SenSys '21}
}
- Abdelrahman, M. M., Chong, A., & Miller, C. (2021). Personal thermal comfort models using digital twins: Preference prediction with BIM-extracted spatial–temporal proximity data from Build2Vec. Building and Environment, 108532.
- Miller, C., Abdelrahman, M., Chong, A., Biljecki, F., Quintana, M., Frei, M., ... & Wong, D. (2021, November). The Internet-of-Buildings (IoB)—Digital twin convergence of wearable and IoT data with GIS/BIM. In Journal of Physics: Conference Series (Vol. 2042, No. 1, p. 012041). IOP Publishing.
- Sae-Zhang, P., Quintana, M., & Miller, C. (2020). Differences in thermal comfort state transitional time among comfort preference groups.
To install requirements:
conda env create --file env/environment_macos.yaml
- Outdoor environmental sensors:
- PurpleAir-PA-II weather station
- Indoor environmental sensors:
- UbiBot WS1-Pro
- Awair Omni
- senSING
- Building Management Systems (BMS)
- Indoor localization:
- Steerpath bluetooth beacons
- YAK mobile application
- Wearables:
- Fitbit Versa 2 smartwatch
- Cozie smartwatch application
- iButtons
To reproduce the pre-processing done to the raw files, extracted directly from the different sensors, run the following files located in data/raw/ in the following order:
- Run
01_create_folders.pyto sort the files fromdata/raw/into different folders. - Run
02_sensors_data.ipynbto process and format all sensor datasets into new files. - Run
03_main.ipynbto process every user data with the sensor data (this will take quite long, around 20 minutes to an hour). - Run
04_temp_light_humidity.ipynbto add columns for temp, light and humidity without being separated by the type of sensor. - There will be csv files created for each user, e.g.
enth01_merged_z.csvand another file for all usersenth_all_users_merged.csvandenth_new_cols_z.csv. The final merged fileenth_tabular_merged.csvhas appropriate column naming conventions. - Run
05_surveys.pyto calculate the scores for each onboarding survey.
The resulting files are then saved in data/processed/
Three different surveys were completed by the study participants upon starting the longitudinal experiments:
- Highly Sensitive Person Scale (reference)
- Satisfaction With Life Scale (reference)
- Big Five Personality Trait (in the form of Ten-Item Personality Inventory survey) (reference)
All three surveys have multiple questions each in a 7-level Likert scale and their score computation was done following their respective references.
The file with the calculated scores is enth_surveys_calc.csv and can be found in data/processed/.
| Feature name | Type | Description/Question |
|---|---|---|
user_id |
String | Unique identifier for each participant |
yob |
Integer | Year of birth |
sex |
Categorical | Self-reported sex |
height |
Integer | Height in cm |
weight |
Float | Weight in kg |
shoulder_circumference |
Integer | Shoulder circumference, in cm |
years_here |
Integer | "How long have you been in Singapore (in years)?" |
used_weather |
Boolean | "Can you say that you are used to the weather in Singapore?" |
satisfaction_weather |
Categorical | 7-point Likert scale, "Can you say that you are satisfied with the weather in this city (outdoor weather conditions)?" |
sweating |
Categorical | From 1 to 5, "Do you suffer from sweating in Singapore?" |
enjoy_ourdoor |
Boolean | "Do you enjoy being outdoor in Singapore?" |
outdoor_hr_weekday |
Integer | "What is your estimation of your time (hours) spent outdoor (per day) during the weekdays? (for example 2 hrs)" |
outdoor_hr_weekend |
Integer | "What is your estimation of your time (hours) spent outdoor (per day) during the weekend? (for example 4 hrs)" |
hsps |
Float | Highly Sensitive Person Scale score |
swls |
Intenger | Satisfaction With Life Scale score |
extraversion |
Float | Extraversion trait score in a 7-point Likert scale |
agreeableness |
Float | Agreeableness trait score in a 7-point Likert scale |
conscientiousness |
Float | Consientiousness trait score in a 7-point Likert scale |
emotional_stability |
Float | Emotional Stability trait score in a 7-point Likert scale |
openness_to_experiences |
Float | Openness to New Experiences trait score in a 7-point Likert scale |
| Feature name | Type | Description |
|---|---|---|
space_id |
Categorical | Unique location on campus |
building_name |
String | Campus building name |
air_vel |
Categorical | Perceived air movement, 9 = "Not Perceived", 11 = "Perceived" |
body_presence |
Boolean | Wheter the user was wearing the smartwatch or not |
change |
Categorical | Change location, activity or clothing, 11 = "Yes Change", 10 = "No Change" |
clothing |
Categorical | Clothing, 8 = "very light", 9 = "Light", 10 = "Medium", 11 = "Heavy" |
comfort |
Categorical | Overall comfort, 10 = "Comfy", 9 = "Not Comfy" |
heartrate |
Float | Heart rate in bpm |
indoor/outdoor |
Categorical | Location, 9 = "Outdoor", 11 = "Indoor" |
met |
Categorical | Metabolic rate, 8 = "resting", 9 = "sitting", 10 = "standing", 11 = "exercising" |
response_speed |
Float | Time in seconds it took to complete the survey |
resting_heartrate |
Float | Resting heart rate in bpm |
thermal |
Categorical | Thermal preference, 9 = "Warmer", 10 = "No Change", 11 = "Cooler" |
nb_temp |
Float | Near body temperature, at the wrist level, in degree Celsius |
skin_temp |
Float | Skin temperature, at the wrist level, in degree Celsius |
indoor_floor |
Integer | Building floor provided by the indoor bluetooth beacons |
indoor_latitude |
Float | Latitude provided by the indoor bluetooth beacons |
indoor_longitude |
Float | Longitude provided by the indoor bluetooth beacons |
co2_indoor |
Float | Indoor CO2 measurement by a fixed sensor in ppm |
voc_indoor |
Float | Indoor Volatile Organic Compound measurement by a fixed sensor in ppm |
pm25_indoor |
Float | Indoor Particulate Matter (PM2.5 - Fine Dust) measurement by a fixed sensor in µg/m3 |
noise_indoor |
Float | Indoor Ambient noise levels measurement by a fixed sensor in dB |
0.3um_count_outdoor |
Float | Outdoor count concentration (particles/100ml) of all particles greater than 0.3 µm diameter |
0.5um_count_outdoor |
Float | Outdoor count concentration (particles/100ml) of all particles greater than 0.5 µm diameter |
1.0um_count_outdoor |
Float | Outdoor count concentration (particles/100ml) of all particles greater than 1.0 µm diameter |
10.0um_count_outdoor |
Float | Outdoor count concentration (particles/100ml) of all particles greater than 10.0 µm diameter |
2.5um_count_outdoor |
Float | Outdoor count concentration (particles/100ml) of all particles greater than 2.5 µm diameter |
5.0um_count_outdoor |
Float | Outdoor count concentration (particles/100ml) of all particles greater than 5.0 µm diameter |
humidity_outdoor |
Float | Outdoor relative humidity by a weather station in % |
pm1.0_outdoor |
Float | Outdoor estimated mass concentration PM1(ug/m3) for PM1.0 |
pm10.0_outdoor |
Float | Outdoor estimated mass concentration PM1(ug/m3) for PM10.0 |
pm2.5_outdoor |
Float | Outdoor estimated mass concentration PM1(ug/m3) for PM2.5 |
pressure_outdoor |
Float | Outdoor pressure by a weather station in millibars |
temp_outdoor |
Float | Outdoor temperature by a weather station in degree Celsius |
user_id |
String | Unique identifier for each participant |
temp_indoor |
Float | Indoor temperature by a fixed sensor in degree Celsius |
light_indoor |
Float | Indoor light level by a fixed sensor in lux |
humidity_indoor |
Float | Indoor relative humidity by a fixed sensor in % |
