Dataset: Longitudinal Indoor Personal Comfort Preferences

About this Dataset

Longitudinal Indoor Personal Comfort Preferences

This dataset includes files from two BUDS lab deployments of the Cozie Fitbit smartwatch platform:

the CRESH Experiment that resulted in the publication Humans-as-a-Sensor for Buildings—Intensive Longitudinal Indoor Comfort Models and
the ENTH Experiment that resulted in the open data publication submitted to the DATA 2021 workshop at BuildSys 2021 called Longitudinal personal thermal comfort preference data in the wild.

Context

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.

Content

For the CRESH dataset, is intensive longitudinal subjective feedback of comfort-based preference using micro ecological momentary assessments on a smartwatch platform. An experiment with 30 occupants over two weeks produced 4378 field-based surveys for thermal, noise, and acoustic preference

For the ENTH dataset, 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.

Inspiration

What models can be developed using these models as a starting point

Tables

Cresh Tabular Merged

@kaggle.claytonmiller_longitudinal_personal_thermal_comfort_preferences.cresh_tabular_merged

229.27 KB
4378 rows
25 columns


CREATE TABLE cresh_tabular_merged (
  "unnamed_0" BIGINT,
  "index" BIGINT,
  "time" VARCHAR,
  "clothing" DOUBLE,
  "comfort_cozie" DOUBLE,
  "heartrate_cozie" DOUBLE,
  "lat_cozie" DOUBLE,
  "light_cozie" DOUBLE,
  "lon_cozie" DOUBLE,
  "noise_cozie" DOUBLE,
  "responsespeed_cozie" DOUBLE,
  "thermal_cozie" DOUBLE,
  "user_id" VARCHAR,
  "floor" DOUBLE,
  "latitude" DOUBLE,
  "longitude" DOUBLE,
  "space_id" DOUBLE,
  "room" DOUBLE,
  "co2_sensing" DOUBLE,
  "humidity_sensing" DOUBLE,
  "light_sensing" DOUBLE,
  "noise_sensing" DOUBLE,
  "temperature_sensing" DOUBLE,
  "voc_sensing" DOUBLE,
  "temperature_mbient" DOUBLE
);

Enth Surveys Renamed

@kaggle.claytonmiller_longitudinal_personal_thermal_comfort_preferences.enth_surveys_renamed

28.68 KB
17 rows
40 columns


CREATE TABLE enth_surveys_renamed (
  "user_id" VARCHAR,
  "yob" BIGINT,
  "sex" VARCHAR,
  "height" BIGINT,
  "weight" DOUBLE,
  "shoulder_circumference" BIGINT,
  "years_here" BIGINT,
  "used_weather" VARCHAR,
  "satisfaction_weather" VARCHAR,
  "sweating" BIGINT,
  "enjoy_ourdoor" VARCHAR,
  "outdoor_hr_weekday" BIGINT,
  "outdoor_hr_weekend" BIGINT,
  "subtleties_awareness" BIGINT,
  "overwhelemed_awareness" BIGINT,
  "rich_life" BIGINT,
  "rattled" BIGINT,
  "deeply_moved" BIGINT,
  "annoyed" BIGINT,
  "violent_movies" BIGINT,
  "unpleasant" BIGINT,
  "changes_shake" BIGINT,
  "delicates" BIGINT,
  "stimuli_awareness" BIGINT,
  "nervous" BIGINT,
  "life_ideal" BIGINT,
  "life_conditions" BIGINT,
  "life_satisfaction" BIGINT,
  "important_things" BIGINT,
  "life_changes" BIGINT,
  "extraverted" BIGINT,
  "critical" BIGINT,
  "dependable" BIGINT,
  "anxious" BIGINT,
  "new_exp" BIGINT,
  "reserved" BIGINT,
  "sympathetic" BIGINT,
  "disorganized" BIGINT,
  "calm" BIGINT,
  "conventional" BIGINT
);

Enth Tabular Merged

@kaggle.claytonmiller_longitudinal_personal_thermal_comfort_preferences.enth_tabular_merged

127.7 KB
1403 rows
38 columns


CREATE TABLE enth_tabular_merged (
  "time" VARCHAR,
  "space_id" BIGINT,
  "building_name" VARCHAR,
  "air_vel" DOUBLE,
  "body_presence" BOOLEAN,
  "change" DOUBLE,
  "clothing" DOUBLE,
  "comfort" DOUBLE,
  "heartrate" DOUBLE,
  "indoor_outdoor" DOUBLE,
  "met" DOUBLE,
  "response_speed" DOUBLE,
  "resting_heartrate" BIGINT,
  "thermal" DOUBLE,
  "nb_temp" DOUBLE,
  "indoor_floor" DOUBLE,
  "indoor_latitude" DOUBLE,
  "indoor_longitude" DOUBLE,
  "skin_temp" DOUBLE,
  "co2_indoor" DOUBLE,
  "voc_indoor" DOUBLE,
  "pm25_indoor" DOUBLE,
  "noise_indoor" DOUBLE,
  "n_0_3um_count_outdoor" DOUBLE,
  "n_0_5um_count_outdoor" DOUBLE,
  "n_1_0um_count_outdoor" DOUBLE,
  "n_10_0um_count_outdoor" DOUBLE,
  "n_2_5um_count_outdoor" DOUBLE,
  "n_5_0um_count_outdoor" DOUBLE,
  "humidity_outdoor" DOUBLE,
  "pm1_0_outdoor" DOUBLE,
  "pm10_0_outdoor" DOUBLE,
  "pm2_5_outdoor" DOUBLE,
  "pressure_outdoor" DOUBLE,
  "temp_outdoor" DOUBLE,
  "user_id" VARCHAR,
  "temp_zone" DOUBLE,
  "humidity_zone" DOUBLE
);