9Sleepy Watch – Towards Predicting Daytime Sleepiness basedon Body Temperature
Jie Bao

Keio University Graduate School of Media Designbao-jie@kmd.keio.ac.jp

Akira Kato

Keio University Graduate School of Media Design
kato@kmd.keio.ac.jp

ABSTRACT
Daytime sleepiness, the difficulty to maintain an alert waking state
during the day, is a serious problem causing vehicle accidents andadverse effects on well-being, health, and productivity. Our researchaims at predicting daytime sleepiness using wearable sensing ineveryday life to raise awareness and help people to manage theirenergy better. This study presents a first exploration of comparing
body temperature (wrist, forehead, in-ear) with users alertness,measured over a reaction test: Psychomotor vigilance task (PVT)
in 7 participants over 2 days in real-life conditions (168 hours intotal). The results indicate a weak correlation between some bodytemperature measures and the PVT scores for certain subjects. This
underlines that unobtrusive on-body temperature sensing can be aninteresting modality to understand and explore daytime sleepiness.

CCS CONCEPTS• Computer systems organization → Embedded systems.

KEYWORDS

Jiawen Han

Keio University Graduate School of Media Design
hanjiawen@kmd.keio.ac.jp

Kai Kunze

Keio University Graduate School of Media Design
kai@kmd.keio.ac.jp
Sleepiness and hours of sleep are inversely associated[3]. In certain
activities, such as driving, sleepiness is considered as a significant
risk factor that substantially contributes to the increasing numberof motor vehicle accidents each year [4]. Besides vehicle accidents,
the economic loss caused by sleepiness was reported in recent
studies [9, 10]. Additionally, people seem bad in recognizing theirlevel of alertness and sleep deprivation by themselves [7]. Many
researchers are working on detecting driver drowsiness. However,similar explorations on daily life’s sleepiness are still very limited.
So far, the COVID-19 pandemic lock-down has affected people’s
lives both physically and mentally. Boundaries of work and life
were blurred, regular daily routines were forced to change, andstress was accumulated. According to some research, this situation
has led the general public, especially female and young people, topoor sleep hygiene habits, such as sleeping hours reduction[17].
From medical research, core body temperature seems to be a goodmeasure for sleepiness [8]. If daytime sleepiness can be detected
in a rigorous but unobtrusive way, such as body temperature, itwould bring a huge impact on our daily life.

objective sleepiness, daytime sleepiness, wrist temperature, bodytemperature
ACM Reference Format:
Jie Bao, Jiawen Han, Akira Kato, and Kai Kunze. 2020. Sleepy Watch –Towards Predicting Daytime Sleepiness based on Body Temperature. In Adjunct Proceedings of the 2020 ACM International Joint Conference on Pervasive
and Ubiquitous Computing and Proceedings of the 2020 ACM International
Symposium on Wearable Computers (UbiComp/ISWC ’20 Adjunct), September 12–16, 2020, Virtual Event, Mexico. ACM, New York, NY, USA, 4 pages.
https://doi.org/10.1145/3410530.3414415

1

INTRODUCTION

Have you ever had a situation where it was hard to keep your eyes
open? Then, you have most likely experienced an episode of excessive daytime sleepiness. We define daytime sleepiness as the difficulty of maintaining an alert waking state even during the day[20].
Permission to make digital or hard copies of all or part of this work for personal orclassroom use is granted without fee provided that copies are not made or distributedfor profit or commercial advantage and that copies bear this notice and the full citationon the first page. Copyrights for components of this work owned by others than ACM
must be honored. Abstracting with credit is permitted. To copy otherwise, or republish,
to post on servers or to redistribute to lists, requires prior specific permission and/or afee. Request permissions from permissions@acm.org.
UbiComp/ISWC ’20 Adjunct, September 12–16, 2020, Virtual Event, Mexico© 2020 Association for Computing Machinery.
ACM ISBN 978-1-4503-8076-8/20/09. . . $15.00
https://doi.org/10.1145/3410530.3414415

Figure 1: The iButton Ds1922L temperature sensor worn on
the left wrist of participants during the experiment.
This paper presents a first step towards this direction. The contributions are as follows:(1) We present an open dataset of 7 participants recording theirwrist/forehead/in-ear temperature of the day together witha reaction time test (PVT) to measure their alertness.
(2) We explore the dataset and show correlations between thedifferent temperature measures and the alertness of the participants.

10UbiComp/ISWC ’20 Adjunct, September 12–16, 2020, Virtual Event, Mexico

2

RELATED WORK

There are multiple methods to measure sleepiness. We can groupmost of them into subjective and objective measurement methods.
The subjective measurements are mostly validated self-rated scales,such as the Stanford Sleepiness Scale(SSS)[11] and the Epworth
Sleepiness Scale (ESS)[14]. While some objective measurements
method uses physiologic measures, for example, the visual analog
scale(VAS)[18], the Multiple Sleep Latency Test[5] and Psychomotor
Vigilance Task (PVT).
To detect drowsiness, various studies are physiological based,
vehicle-based, and behavioral-based[15, 21]. Physiological methods
such as heartbeat, pulse rate, and Electrocardiogram(ECG) are used
to detect fatigue level[13, 19]. Vehicle-based methods include accelerator pattern, acceleration and steering movements. Behavioral
methods[15, 21]include yawn, Eye Closure, Eye Blinking, etc. Mostof the traditional methods are based on behavioral aspects whilesome are intrusive and may distract drivers, while some require
expensive sensors. And the research about sleepiness in daily living
is still limited.
Using mobile phones or wearable sensings to detect cognitivefluctuations has become a trend in Ubicomp community recently.
Abdullah et al. proved how mobile phone sensors correlate withthe alertness of users [1]. Tag et al. used smart glasses to estimate
alertness in everyday situations [22]. There is also some work relating temperature to sleepiness detection. Wei et al. utilize bodytemperature to identify whether a user is in the sleeping state[23].
Our research goal is to assess daily sleepiness from both body temperature in a real-life condition.

3(2) An infrared medical thermometer( Dretec, JP) and(See inFigure 3): Participants were asked to report their foreheadtemperatures and in-ear temperatures once an hour by using
this infrared medical thermometer.
(3) A Mi Band 4: Mi Band 4 was used to record participants’sleep length before the test day.
Besides data collection from sensors, all participants were asked
to do a PVT task lasting for one minute once an hour during the testperiod. After they completed PVT, participants were requested tomeasure and record their forehead temperature and in-ear temperature at this time by themselves. We also asked them to track theiractivities such as having a meal, taking a nap, and even drinking acup of coffee.
Eight healthy students(male:3,female:5) aged 23-32 years old
were recruited. Because of data loss due to the sensor storage’s
limit, one male’s data was excluded. Data were collected from February 2020 to June 2020. We record each participant’s data for 2days(more than 12 hours per day) of their everyday life We also
asked participants to stay at home and avoid any vigorous activity
during the test period.

APPROACH

As already mentioned in the related work, there are several methods
to detect sleepiness and related cognitive states such as fatigue,alertness in the laboratory. Our research focuses on approaches todetect cognitive activities in real life [2, 6, 16, 22].
Core temperature performs stable when people are staying in
steady environment and changes in the opposite direction (coolingor warming) of the skin temperature[12]. When sleepy or sleeping, core temperature drops, while skin temperature (especiallytemperature on the extremities) increases noticeably.
We want to use this effect to monitor sleepiness over the day withunobtrusive devices, measuring temperature on several body locations easily augmented with a wearable device: in-ear (headphones),
wrist (smart watch), fore-head (glasses). To this end, we present an
initial experimental protocol and results of 7 participants.

4Bao and Han, et al.

Figure 3: Control Measures of the forehead and in-ear temperature

EXPERIMENTS

All participants had a one-on-one orientation session. After fillingthe consent form, all participants were asked to download the designated PVT application. We gave all participants 20s’ trial before
the formal test and generally explained sensors used in this study:(1) An iButton Ds1922L temperature sensor( Maxim, Dallas,US)(See in Figure 1): Participants were asked to wear theiButton temperature sensor on their left wrists for a whole
day from waking up till going to bed. Wrist temperature wascollected once a minute.

participants

rpvalue

f1
f2
f3
f4
f5
m1
m2

0.067
0.402
0.430
0.094
-0.290-0.316-0.009

0.636
0.009
0.046
0.662
0.135
0.057
0.971

Table 1: Correlation between wrist temperature and PVTmean of 7 participants

5

RESULT AND DISCUSSION

In this study, we use the mean value of PVT in each session as its
PVT score to evaluate the subject’s sleepiness. Among the three11Sleepy Watch – Towards Predicting Daytime Sleepiness based on Body Temperature

UbiComp/ISWC ’20 Adjunct, September 12–16, 2020, Virtual Event, Mexico

Figure 2: Three types of body temperatures and PVT data in a day from 3 subjects whose wrist data were proved to correlatewith corresponding mean value PVT scores.Subject f2 and subject f3 are females, while subject m1 is male.
types of body temperatures, we focused on exploring wrist temperature change because it can be tracked precisely without subject
interaction. Also, different operations of the infrared thermometer,
such as measuring angles, seem to affect the accuracy of in-ear
temperatures and forehead temperatures. Therefore, we only use
in-ear temperatures and forehead temperatures as supplementarydata. We also recorded room temperatures of some participants
data during the test period. Most room temperatures fluctuatedwithin 0.5◦ C to 1.4◦ C which has larger fluctuation than in-ear andforehead range.
We tested the correlation between overall wrist temperaturesand mean PVT task scores by calculating Pearson’s Correlation
Coefficient. As Figure 4 shows, no linear correlation was foundbetween wrist temperature and PVT mean value ( r = 0.026, p =0.263). However, we found for certain participants, who are subject
f2, subject f3, and subject m1, moderate linear correlations existed.
And each subject’s result was summarized in Table 1. Figure 2 shows
three types of body temperatures’ changes and corresponding PVT
values for subject f2, subject f3, and subject m1. Compare within-ear and forehead temperature, the wrist temperature fluctuatedmore frequently and to a larger extent.

From the results above, though we failed to prove the correlations
between wrist temperatures and PVT scores by using data mixed,
we still found correlations exist within certain subjects. This may
imply that the individual difference and corresponding calibrations
can not be ignored when researchers try to estimate sleepinessin daily life. Furthermore, comparing with the change of in-ear
temperature, forehead temperature, and wrist temperature (See inFigure 2), wrist temperature is more sensitive to alertness. Therefore,
this may prove we can use wrist temperature sensors to detect evenminor changes in alertness promptly.

67Figure 4: Correlation between wrist temperature and PVTCONCLUSION AND FUTURE WORKIn this paper, we propose a novel method to track people’s sleepiness in daily life. Our results show some correlations between wrist
temperatures and PVT scores within-subject. However, not all initial results were not all promising. Our findings still indicate thattracking wrist temperature to detect sleepiness might be possible
in some participants. More data is required to conclude.
Data in this study were all collected from a real-life condition andwe will make the data-set open to using for other researchers. Thismethod of data collection is closer to a practical concept, but also
inevitably led to some limitations. First of all, due to the long periodof data collection, we only had 8 subjects joining this experiment
so that it would be hard to train a real model for detection andprediction. Additionally, measuring temperature by participants
themselves turned out to reduce data accuracy. In a follow up study
we will prepare a training protocol on how to measure in-ear andforehead temperature for the participants.
In the future, we will adjust the experiment design and making anoverall setup and data recording protocol more precise. We also plan
to embed small contact-less temperature sensors in headphones,watches, and glasses to make the measurements less obtrusive andcontinuous in daily life [2, 24]. Correlating temperature measurements from different on-body locations could help improve theaccurate detection of sleepiness in everyday situations.

ACKNOWLEDGMENTS

We want to thanks the participants of the study for their help.
The work is partly supported by JSPS Kakenhi Kiban B Grant No.
18H03278.

12UbiComp/ISWC ’20 Adjunct, September 12–16, 2020, Virtual Event, Mexico

REFERENCES[1] Saeed Abdullah, Elizabeth L Murnane, Mark Matthews, Matthew Kay, Julie A
Kientz, Geri Gay, and Tanzeem Choudhury. 2016. Cognitive rhythms: unobtrusiveand continuous sensing of alertness using a mobile phone. In Proceedings of the2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing.
178–189.
[2] Oliver Amft, Florian Wahl, Shoya Ishimaru, and Kai Kunze. 2015. Making regulareyeglasses smart. IEEE Pervasive Computing 14, 3 (2015), 32–43.
[3] Naomi Breslau, Thomas Roth, Leon Rosenthal, and Patricia Andreski. 1997. Daytime sleepiness: an epidemiological study of young adults. American Journal ofPublic Health 87, 10 (1997), 1649–1653.
[4] Aurelie Campagne, Thierry Pebayle, and Alain Muzet. 2004. Correlation between
driving errors and vigilance level: influence of the driver’s age. Physiology &behavior 80, 4 (2004), 515–524.
[5] Mary A Carskadon and William C Dement. 1975. Sleep studies on a 90-minuteday. Electroencephalography and clinical neurophysiology 39, 2 (1975), 145–155.
[6] George Chernyshov, Kirill Ragozin, Benjamin Tag, and Kai Kunze. 2019. EOG
Glasses: an Eyewear Platform for Cognitive and Social Interaction Assessments inthe Wild. In Proceedings of the 21st International Conference on Human-Computer
Interaction with Mobile Devices and Services. 1–5.
[7] Anupama Gopalakrishnan, Li Li Ji, and Chiara Cirelli. 2004. Sleep deprivationand cellular responses to oxidative stress. Sleep 27, 1 (2004), 27–35.
[8] Michael Gradisar and Leon Lack. 2004. Relationships between the circadianrhythms of finger temperature, core temperature, sleep latency, and subjectivesleepiness. Journal of biological rhythms 19, 2 (2004), 157–163.
[9] M Hafner et al. 2017. The economic costs of insufficient sleep: a cross-country
comparative analysis. Rand Health (2017).
[10] David Hillman, Scott Mitchell, Jared Streatfeild, Chloe Burns, Dorothy Bruck,and Lynne Pezzullo. 2018. The economic cost of inadequate sleep. Sleep 41, 8(2018), zsy083.
[11] E Hoddes, Vincent Zarcone, Hugh Smythe, Roger Phillips, and William C Dement.
1973. Quantification of sleepiness: a new approach. Psychophysiology 10, 4 (1973),431–436.
[12] Charlie Huizenga, Hui Zhang, Edward Arens, and Danni Wang. 2004. Skinand core temperature response to partial-and whole-body heating and cooling.
Journal of thermal biology 29, 7-8 (2004), 549–558.

Bao and Han, et al.

[13] Taeho Hwang, Miyoung Kim, Seunghyeok Hong, and Kwang Suk Park. 2016.
Driver drowsiness detection using the in-ear EEG. In 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).
IEEE, 4646–4649.
[14] Murrayb W Johns. 1992. Reliability and factor analysis of the Epworth Sleepiness
Scale. Sleep 15, 4 (1992), 376–381.
[15] Ashish Kumar and Rusha Patra. 2018. Driver drowsiness monitoring system using
visual behaviour and machine learning. In 2018 IEEE Symposium on Computer
Applications & Industrial Electronics (ISCAIE). IEEE, 339–344.
[16] Kai Kunze, Masakazu Iwamura, Koichi Kise, Seiichi Uchida, and Shinichiro
Omachi. 2013. Activity recognition for the mind: Toward a cognitive" QuantifiedSelf". Computer 46, 10 (2013), 105–108.
[17] Li-yu Lin, Jie Wang, Xiao-yong Ou-yang, Qing Miao, Rui Chen, Feng-xia Liang,Yang-pu Zhang, Qing Tang, and Ting Wang. 2020. The immediate impact of the2019 novel coronavirus (COVID-19) outbreak on subjective sleep status. Sleep
Medicine (2020).
[18] Timothy H Monk. 1989. A visual analogue scale technique to measure global
vigor and affect. Psychiatry research 27, 1 (1989), 89–99.
[19] Muhammad Ramzan, Hikmat Ullah Khan, Shahid Mahmood Awan, Amina Ismail, Mahwish Ilyas, and Ahsan Mahmood. 2019. A survey on state-of-the-art
drowsiness detection techniques. IEEE Access 7 (2019), 61904–61919.
[20] Timothy Roehrs. 2005. Daytime sleepiness and alertness. Principles and practiceof sleep medicine (2005), 39–50.
[21] Mr Shailesh Sangle, Bharat Rathore, Rishabh Rathod, Aakashkumar Yadav, andAbhishek Yadav. 2018. Real Time Drowsiness Detection System. IOSR Journal ofComputer Engineering (IOSR-JCE) (2018).
[22] Benjamin Tag, Andrew W Vargo, Aman Gupta, George Chernyshov, Kai Kunze,
and Tilman Dingler. 2019. Continuous alertness assessments: Using EOG glasses
to unobtrusively monitor fatigue levels In-The-Wild. In Proceedings of the 2019
CHI Conference on Human Factors in Computing Systems. 1–12.
[23] Jing Wei, Jin Zhang, and Jennifer Boger. 2018. What Wrist Temperature Tells Us
When We Sleep Late: A New Perspective of Sleep Health. In 2018 IEEE SmartWorld,
Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable
Computing & Communications, Cloud & Big Data Computing, Internet of People
and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI).
IEEE, 764–771.
[24] Weiwei Wu and Hossam Haick. 2018. Materials and wearable devices for autonomous monitoring of physiological markers. Advanced Materials 30, 41 (2018),1705024.