Abstract and Figures

Labelled human comfort data can be a valuable resource in optimising the built environment, and improving the wellbeing of individual occupants. The acquisition of labelled data however remains a challenge. This paper presents a methodology for the collection of in-situ occupant feedback data using a Fitbit smartwatch. The clock-face application cozie can be downloaded free-of-charge on the Fitbit store and tailored to fit a range of occupant comfort related experiments. In the initial trial of the app, fifteen users were given a smartwatch for one month and were prompted to give feedback on their thermal preferences. In one month, with minimal administrative overhead, 1460 labelled responses were collected. This paper demonstrates how these large data sets of human feedback can be analysed to reveal a range of results from building anomalies, occupant behaviour, occupant personality clustering, and general feedback related to the building. The paper also discusses limitations in the approach and the next phase of design of the platform.
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Is your clock-face cozie? A smartwatch methodology for the in-situ
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Journal of Physics: Conference Series 1343 (2019) 012145
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doi:10.1088/1742-6596/1343/1/012145
1
Is your clock-face cozie? A smartwatch methodology
for the in-situ collection of occupant comfort data
Prageeth Jayathissa1, Matias Quintana1, Tapeesh Sood1, Negin
Narzarian2, Clayton Miller 1
1Building and Urban Data Science Group, National University of Singapore (NUS), 129800
Singapore
2University of New South Wales (UNSW), Australia
E-mail: p.jayathissa@nus.edu.sg
Abstract. Labelled human comfort data can be a valuable resource in optimising the built
environment, and improving the wellbeing of individual occupants. The acquisition of labelled
data however remains a challenge. This paper presents a methodology for the collection of
in-situ occupant feedback data using a Fitbit smartwatch. The clock-face application cozie can
be downloaded free-of-charge on the Fitbit store and tailored to fit a range of occupant comfort
related experiments. In the initial trial of the app, fifteen users were given a smartwatch for
one month and were prompted to give feedback on their thermal preferences. In one month,
with minimal administrative overhead, 1460 labelled responses were collected. This paper
demonstrates how these large data sets of human feedback can be analysed to reveal a range
of results from building anomalies, occupant behaviour, occupant personality clustering, and
general feedback related to the building. The paper also discusses limitations in the approach
and the next phase of design of the platform.
1. Introduction
In the Ma¯ori legends of old, there was a time when the sun would travel quickly across the sky,
leaving people without sufficient light and warmth. aui, a great hero of the time, observed
this discomfort and went on a quest to tame the sun. Armed with his magic jawbone of
Murirangawhenua, he tied down the sun and beat it, until it slowed down to the speeds we have
today. What M¯aui did was categorise everyone in a one-size-fits-all thermal comfort model, and
based on this assumption, took action to change the environment he lived in.
The way we control our buildings today is similar to the method that M¯aui used. We make
an assumption of the general population based on a survey of a few people, and change the
environment we live in based on these few data points. From these methodologies, traditional
thermal comfort research has produced comfort models based on indices such as the predicted
mean vote (PMV). A recent review of data from dozens of such studies has shown that PMV is
accurate only 34% of the time [1]. The issue with these models when applied in building practice,
is the assumption that all occupants within a building zone share the same comfort preferences.
In reality, variations in metabolic rates and the preferences or tolerances of each person presents
a challenge when conditioning a workspace to meet the requirements of all occupants [2].
Understanding the preferences of individuals presents a significant challenge, both in the times
of M¯aui and now. The state of the art uses wearable sensors that collect physiological parameters
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Experiment
Settings
Feedback
Data
Client Side
Sensor
Data
Influx
Time-Series
Database
Query
(a) (b) (c) (d)
Figure 1: Overview of the cozie app platform: (a) the Fitbit mobile app is used to set
experimental settings, (b) the flow of questions based on settings selected in (a), (c) photos
of the watch-face using a Fitbit ionic, with the strap-pack sensor box, (d) overview of data
communication.
which can feed into human comfort models [3]. These sensors can be further complemented with
the use of smartphone applications [4], or in the form of online or paper-based surveys. While
these methods work, they have inherent difficulties:
The methodologies often cannot be scaled to large sample sets due to the administrative
overhead in preparing these studies.
The studies are often conducted outside of the test subjects natural working environment,
or uses devices that the user would not traditionally wear in their day to day life.
Users suffer from survey fatigue [5] and even when willing to participate, there is a concern
about how accurate their responses are [6].
We hypothesise that the use of a smartwatch clock-face to collect subjective comfort feedback,
is a more scalable, and non-intrusive method, which also minimises survey fatigue due to the
minimal effort required from users.
This paper presents cozie, a publicly available clock-face designed for the Fitbit smartwatch
which can be used for in-situ human comfort studies. We will show how the clock-face can be
deployed for a range of tailored experimental scenarios, and be evaluated using modern data
analytics to infer behavioural patterns of the test participants. This information can be used
to optimise human comfort through spatial recommendation, or combined with building sensor
data to create a labelled data-set for the comfort optimisation of the building management
system.
2. The cozie clock-face
Cozie is built as a clock-face for Fitbit, a smartwatch with 25 million active users. The application
is publicly available for download from the cozie website1. In this paper, we define user as the test
participant who is wearing the Fitbit, and manager as the person coordinating the experiment.
The default status of the clock-face is a simple binary question: Comfy or Not Comfy, as seen
1https://cozie.app/
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in Figure 1. By simply clicking one of the icons, information about the users’ location (GPS),
heart-rate, steps walked since the last log, and the comfort data is anonymously sent to an Influx
time series cloud database 2. Data from this database can be queried with an API key that can
be provided to the manager.
If the manager is interested as to why the user is feeling discomfort, then there is a range
of additional questions that can be configured using the cellphone that the Fitbit is paired
with. The optional questions include: thermal preference, light preference, noise preference,
indoor/outdoor, mood, and whether the user is in office. These settings, along with a unique
user-id for each user, and a unique experiment-id can be configured by the manager. The watch-
face can also prompt the user with a gentle vibration and force them to provide feedback by
hiding the clock until feedback has been given.
3. Experimental methodology
An experiment was deployed as part of Project Coolbit3, an international effort that focusses
on the use of smartwatches for human comfort analysis [7]. Fifteen participants residing in
Singapore were recruited for the experiment and were equipped with Fitbit Versa or Ionic
watches. Most of the participants were working at the National University of Singapore (NUS)
at several flexible workspaces around campus. The cozie clock-face was set to request thermal
preference (prefer warmer, prefer cooler, comfy), and the set to request feedback at the hours
of 9:00, 11:00, 13:00, 15:00, and 17:00.
The watch was further complemented with Internet-of-Things (IoT) connected on-body and
environmental sensors. The on-body sensor consists of a temperature and light sensor from
mbient-labs4that had been modified to fit the watch strap with a custom 3D printed case. An
off-body sensor measuring temperature and humidity was attached to the participant’s bag.
The sensors communicate via Bluetooth to Raspberry-Pi gateways that had been positioned
throughout the working space. Data from the cozie clock-face and the environmental sensors
were aggregated in an Influx cloud time-series database, which served as a platform for data
acquisition and fault detection.
4. Results
The experiment, consisting of fifteen users each equipped with a Fitbit over a month, produced
a data set of 1,460 data points. Each data point is effectively a survey of the user at a particular
time. The results presented in this section is a demonstration of the type of analysis that can
be conducted using data acquired from the cozie clock-face.
4.1. Overview of spatio-temporal data
Figure 2a details the spatial distribution of data throughout Singapore. Each of these data
points is tagged with the users heart-rate, response, and local temperature which can be used
to infer faults or issues within the building. Figure 2b is a simple heat map plotting the number
of responses based on the hour of the day, and day of the week. It is interesting to note that
55% of responses come from the hours of 9:00, 11:00, 13:00, 15:00, and 17:00 when the occupant
is buzzed and forced to give feedback. The remaining 45% of responses are made outside these
times through the self-motivation of the participants themselves. Figure 2c details the daily
responses from the participants, and no observable decrease in responses can be made, indicating
no effects of survey fatigue. Dips in responses naturally occur during the weekend, and during
2https://github.com/influxdata/influxdb
3http://www.projectcoolbit.com
4https://mbientlab.com/
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Prefer Cooler
Comfortable
Prefer Warmer
co-working space 1
(a)
(b) (c)
(d) (e) (f)
days
responses
hour
daynormalised distribution
Heart Rate (bpm) Temperature (C) Relative Humidity (%)
SpatialTemporalSensor
Figure 2: Overview of raw data extracted from the cozie clock-face and additional sensors. (a)
spatial distribution of responses throughout Singapore, (b) temporal distribution of responses,
(c) number of responses per day over the course of the experiment, (d-f) rug plots detailing
the normalised distribution of responses based on the Fitbit heart rate sensor, wrist-mounted
temperature sensor, and off-body humidity sensor.
the first week when users were still being onboarded. A normal distribution of heart-rate data
from the Fitbit heart rate sensor can be found in Figure 2d.
4.2. Merging with environmental sensor data
Combining the cozie clock-face, with additional environmental sensors opens further dimensions
of analysis. User responses are mapped to the environmental condition at which they are
exposed, which can provide a high quality labelled data set for training data-driven models.
Figure 2e-f detail the distribution of temperature and humidity data. Unfortunately, due to
communication issues from these sensors, not all data points could be recorded. The temperature
of the strap-mounted temperature sensor is on average 0.8 C warmer than the surrounding
environment due to the influence of body temperature.
4.3. Clustering of thermal comfort personality
Individual user feedback can be clustered using unsupervised learning techniques. In this
example, we use a hierarchical k-means clustering based on Euclidean distance using the nearest-
point-algorithm. The results, shown in Figure 3, show four distinct clusters of users.
Understanding and defining these differences in user preferences can be used to recommend
spaces that may better suit the needs of the occupant. For example, Group A, which primarily
works off-site can be recommended alternative workspaces that are on average cooler. Group C
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A
C
B
D
Temperature
Prefer Cooler Comfortable Prefer Warmer
Heart Rate
co-working space 1
Location
National University
of Singapore
Prefer Cooler
Comfortable
Prefer Warmer
Figure 3: Hierarchical clustering heatmap of user feedback using k-means with Euclidean
distance. The numbers within the cluster-map detail the normalised number of responses. Four
distinct groups can be observed. (A) two users that generally prefer cooler environments to their
norm, (B) users that are comfortable 50% of the time, (C) user that is almost always comfortable,
(D) users that are comfortable on average 70% of the time. To the right are breakdowns of the
respective groups via sensor data and location. Below the cluster plot are spatial distributions
of feedback responses at the university.
on the other hand is a single highly satisfied user, who works from a single work-space within
a narrow temperature range, and relatively low resting heart-rate. Group D represents our
conventional occupant that may be comfortable 70% of the time.
Below the cluster plot are spatial distributions of responses that can be used to identify
different building climates. The majority of Prefer Warmer responses occur in co-working
space 1. This area can be labelled as a cooler working space for users that would prefer cooler
working environments. Alternatively, if facilities management wishes to save energy, increasing
the set-point temperature of these over-cooled spaces may be a low effort solution which may
simultaneously improve occupant well-being.
5. Discussion
5.1. Large uncontrolled data vs. small controlled data
Placing a group of participants in a controlled experimental space and conducting feedback
surveys is a trusted traditional method for human comfort surveys. Giving each participant a
smartwatch, and analysing the patterns of hundreds of data points per user would be more akin
to modern data analytics employed in industries outside the building sector. Both methods are
useful for reaching different types of conclusions. The traditional method can derive conclusions
such as: 4 of the 15 users felt warm at temperatures higher than 25.6 C. This insight is
useful in the context of the previously mentioned generalizable thermal comfort models that
are traditionally created in built environment research, but have poor accuracy. On the other
hand, the uncontrolled, large data method can draw conclusions such as: 4 of 15 users can be
categorized as a user type that prefers cooler working environments. This paper focuses on the
use of clustering to show the groups of comfort personality types.
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5.2. In-situ benefits and limitations
Uncontrolled experiments have minimal management overhead, which means that it can be
easily scaled to larger groups by purchasing more devices. Furthermore, the users are analysed
in their natural work environments and give feedback with a simple click on their watch. This
reduction of effort results in no fatigue in the number of voluntary responses given as shown in
Figure 2c. While users generally work from their office, they sometimes work from home, or at
a local cafe. This presents a limitation in the context of traditional small controlled data. Large
enough data must be obtained to filter out these scenarios and interpret meaningful results.
5.3. Indoor Localisation
The clock-face collects GPS data from the Fitbit, however GPS data indoors is not always
reliable, and often not accessible. Only 30% of all data points were tagged with GPS data.
The team is currently investigating other methods such as Bluetooth based localisation from
Steerpath, and pattern matching of wearable sensor data to indoor sensors.
6. Conclusion
First trial runs of the cozie application for occupant comfort data collection have proven
successful. Within just four weeks 1460 data points of thermal comfort were obtained from
the 15 test participants, with minimal administrative overhead. This rich data set provides
new opportunities in analysing occupant comfort behaviour through data-driven methods. We
have demonstrated how the data can be manipulated and clustered to group people into various
comfort profiles. In our case, there were 4 distinct groups, which can be recommended spaces that
better suit their comfort profile. The data can also be clustered via time to display building
defects or anomalies in occupant behaviour. Finally, we demonstrate how the app can be
combined with wearable environmental sensors to cross-reference a users preference to their
environment. The open data repository for this paper can be found on Github5.
Next steps in this research involve using cozie for the exploration of occupant clustering and
spatial recommendation. We will explore elements of sound, light, and thermal comfort to de-
termine whether spatial recommendation can serve as an alternative to individualised adaptive
buildings control. Furthermore, the development of a strap-pack, a smartwatch environmental
sensor that can be adapted to the watch strap is underway.
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centric environmental control, Building and Environment 132 (2018) 114–124.
[3] S. Liu, Personal thermal comfort models based on physiological parameters measured by wearable sensors,
Rethinking Comfort.
[4] L. Barrios, W. Kleiminger, The comfstat-automatically sensing thermal comfort for smart thermostats, in:
2017 IEEE International Conference on Pervasive Computing and Communications (PerCom), IEEE, 2017,
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[5] S. R. Porter, M. E. Whitcomb, W. H. Weitzer, Multiple surveys of students and survey fatigue, New Directions
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5https://github.com/buds-lab/cisbat-cozie-paper
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The predicted mean vote (PMV) and predicted percentage of dissatisfied (PPD) are the most widely used thermal comfort indices. Yet, their performance remains a contested topic. The ASHRAE Global Thermal Comfort Database II, the largest of its kind, was used to evaluate the prediction accuracy of the PMV/PPD model. We focused on: (i) the accuracy of PMV in predicting both observed thermal sensation (OTS) or observed mean vote (OMV) and (ii) comparing the PMV-PPD relationship with binned OTS – observed percentage of unacceptability (OPU). The accuracy of PMV in predicting OTS was only 34%, meaning that the thermal sensation is incorrectly predicted two out of three times. PMV had a mean absolute error of one unit on the thermal sensation scale and its accuracy decreased towards the ends of the thermal sensation scale. The accuracy of PMV was similarly low for air-conditioned, naturally ventilated and mixed-mode buildings. In addition, the PPD was not able to predict the dissatisfaction rate. If the PMV model would perfectly predict thermal sensation, then PPD accuracy is higher close to neutrality but it would overestimate dissatisfaction by approximately 15–25% outside of it. Furthermore, PMV-PPD accuracy varied strongly between ventilation strategies, building types and climate groups. These findings demonstrate the low prediction accuracy of the PMV–PPD model, indicating the need to develop high prediction accuracy thermal comfort models. For demonstration, we developed a simple thermal prediction model just based on air temperature and its accuracy, for this database, was higher than PMV.
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Existing HVAC systems involve little feedback from indoor occupants, resulting in unnecessary cooling/heating waste and high percentage of discomfort. In addition, large thermal preference variance amongst people requires the development of personal thermal comfort models, rather than group-based methodologies such as predicted mean vote (PMV). This study focuses on assessing wearable solutions with the aim to predict personal thermal preference. We collected physiological signals (e.g., skin temperature, heart rate) of 14 subjects (6 female and 8 male adults) and environmental parameters (e.g., air temperature, wind speed, solar radiation, precipitation) for two weeks (at least 20 hr/d) to infer personal real-time thermal preference. The subjects reported their real-time thermal sensation and preference using cell-phones approximately every hour. We trained a Random Forest algorithm using data collected from individuals to develop a personal comfort model with the objective to predict thermal preference. The results show that subjects expressed needs for "warmer" or "cooler" conditions at about 30% (from 21% to 88%) of their daily time on average, implying the strong demand for a personalized indoor thermal comfort. In addition, the personal comfort model using Random Forest can infer individual thermal preference with a mean accuracy of 75% (53-93%) using physiological and environmental parameters, demonstrating the strengths of the proposed data-driven method.
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Thermal comfort in shared workplaces is often contested and impacts productivity, wellbeing, and energy use. Yet, subjective and situated comfort experiences are rarely captured and engaged with. In this paper, we explore roles for digital surveys in capturing and visualising subjective experiences of comfort in situ for comfort management. We present findings from a 3-week field trial of our prototype system called ThermoKiosk, which we deployed in an open plan, shared office with a history of thermal comfort complaints. In interviews with occupants and members of facilities management, we find that the data and interactions can play an important role in initiating dialogue to understand and handle tensions, and point to design considerations for more systematically integrating them into workplace comfort practices.
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A personal comfort model is a new approach to thermal comfort modeling that predicts an individual's thermal comfort response, instead of the average response of a large population. It leverages the Internet of Things and machine learning to learn individuals' comfort requirements directly from the data collected in their everyday environment. Its results could be aggregated to predict comfort of a population. To provide guidance on future efforts in this emerging research area, this paper presents a unified framework for personal comfort models. We first define the problem by providing a brief discussion of existing thermal comfort models and their limitations for real-world applications, and then review the current state of research on personal comfort models including a summary of key advances and gaps. We then describe a modeling framework to establish fundamental concepts and methodologies for developing and evaluating personal comfort models, followed by a discussion of how such models can be integrated into indoor environmental controls. Lastly, we discuss the challenges and opportunities for applications of personal comfort models for building design, control, standards, and future research.
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This chapter reviews the literature on survey fatigue and summarizes a research project that indicates that administering multiple surveys in one academic year can significantly suppress response rates in later surveys.
Project coolbit updates: Personal thermal comfort assessments using wearable devices
  • N Nazarian
  • C Miller
  • L Norford
  • M Kohler
  • W Chow
  • J Lee
  • S Alhadad
  • M Quintana
  • L Suden
  • A Martilli
N. Nazarian, C. Miller, L. Norford, M. Kohler, W. Chow, J. Lee, S. Alhadad, M. Quintana, L. Suden, A. Martilli, Project coolbit updates: Personal thermal comfort assessments using wearable devices, Geophysical Research Abstracts, EGU General Assembly 21 EGU2019-13042.
Project coolbit updates: Personal thermal comfort assessments using wearable devices, Geophysical Research Abstracts
  • Nazarian