June 2021

Measuring Health Conditions Every
Morning using a Smart Toothbrush with a
Gas Sensor1
Satoshi YOSHIMURA a , Teruhiro MIZUMOTO a , Yuki MATSUDA b , Keita UEDA c
and Akira TAKEYAMA ca Osaka University, Suita-shi, Osaka 565-0871, Japanb Nara Institute of Science and Technology, Ikoma-shi, Nara 630-0192, Japanc NOVENINE, Inc., Osaka-shi, Osaka 542-0081, Japan
Abstract. Mental illness is a serious health problem that the COVID-19 pandemic
has exacerbated due to the new teleworking style. This work style forces isolation and reduces physical activities leading to a work efficiency drop and impaired
work-life balance. Despite the current proliferation of IoT-based health measurement means, most of these devices are developed for physicians or cannot build adaily habit and keep the user’s motivation due to design issues. Therefore, mental health measurement tools that can transparently assess the wellness of users
daily basis are necessary. This paper proposes a mental health assessment systemthat smoothly integrates with the user’s toothbrush providing a new generation ofsmart toothbrushes. Specifically, the designed system combines a gas sensor withthe toothbrush for collecting halitosis data (odor, temperature, humidity, pressure)when brushing teeth. The collected data are then leveraged to train Random Forestmodels that estimate three indexes for two recoveries and one work engagement. To
evaluate the proposed method, we collected realistic halitosis data using the smart
toothbrush from 12 subjects every day over two months. The results show that the
proposed system can obtain F-score of 0.84, 0.81, and 0.80 of Dedication, Vigor,
and Absorption, respectively.
Keywords. recovery, work engagement, smart toothbrush, measurement system

1. Introduction
Mental illness is one of the most serious global health problems which is continuously
increasing. The COVID-19 pandemic [1] has participated in raising the problem severity
due to the new norms forced by the COVID, including remote working style, social
distance, forced isolation, and/or reduced physical activity. All these policies were foundto negatively affect physical and mental health leading to a drop in work efficiency andimpairing work-life balance [2].
For this, enterprises and researchers on industrial insurance have taken other policies that ensure work engagement and recovery to ameliorate mental health and improve
1 This research is partially supported by Initiative for Life Design Innovation (iLDi) Platform for Society 5.0
and JSPS KAKENHI Grant Number 19K11924.

June 2021

work performance. Recovery refers to the process of recovering from a stressed state [3].
It has been suggested that the process of recovery from work stress may be related toindividual health and well-being and work performance [4]. Work engagement is defined as a positive, fulfilling, work-related state of mind that is characterized by vigor,
dedication, and absorption [5]. Work engagement is related to a decrease in ill-healthand increases in life satisfaction and job performance [6]. Therefore, regular and longterm checks focusing on recovery and engagement are important to work while healthy.
However, the daily assessment system for such mental health has not been established.
Whereas, using IoT devices for measuring health conditions has become popular inrecent years. These IoT devices can collect sensor data such as heart rate and sleep quality
to measure health from various perspectives. However, if a user actively uses multiple
IoT devices, he/she must understand how to use each device and manage applications foreach device. These periodically troublesome procedures reduce the motivation of users
to continue health measurements; therefore, a way to continue using IoT devices in thelong term is necessary [7].
In this study, to reduce the measurement burden on the user, we focus on the movements of daily life and aim to realize health measurement in a natural flow. One of the
movements of daily life is brushing teeth, and people habitually do it when they wakeup. Halitosis is said to correlate with stress; therefore, we assume it also relates to work
engagement and recovery correlated with stress. This paper proposes a system that usesa smart toothbrush equipped with a gas sensor to collect halitosis and estimate daily recovery and work engagement by the natural flow during tooth brushing behavior when
waking up. Since toothbrushes are necessary for daily life, using smart toothbrushes reduces the trouble of learning and managing special equipment for health measurement.
In addition, monitoring daily mental state and work engagement leads to early detectionof mental illness.
To evaluate the proposed method, we collected daily halitosis data of 12 subjects forapproximately two months. Then, we constructed a machine learning model to estimate
each item of the questionnaires by Random Forest based on the halitosis data. In the experiment, each subject used a smart toothbrush (SMASH) with a gas sensor and collected
halitosis data before brushing in the morning. In addition, as the ground truth of the training model, each subject answered the questionnaire about recovery used in research onindustrial insurance [8] [9] [6] [3] in / after tooth brushing. Then, we constructed threekinds of models that estimate each recovery and work engagement scale with two classes
from the values of the questionnaire and compared the accuracy by leave-one-person-out
cross-validation. As a result of the experiment, we achieved an F-score of 0.84, 0.81, and0.80 in ”Dedication”, ”Vigor”, and ”Absorption”, which are indexes of Work Engagement, estimated by Random Forest with binary classification.

2. Related Works
2.1. Health status measurement using IoT devices
In recent years, a variety of health monitoring systems using IoT devices have been proposed [10]. Zhang et al. [11] develop a necklace-type IoT device monitoring human eating habits such as the number of feeding to detect eating disorders and real-time inter-

June 2021

vention. Leng et al. [12] propose a drowsiness detection system using a wristband typedevice. When the driver wears this device, it acquires sensor data and extracts features
such as heart rate, pulse fluctuation, and respiratory rate based on the sensor data to detect drowsiness. Bui et al. [13] analyze whether it is possible to intervene in diabetic patients from IoT devices. Proposals for lifestyle improvement from IoT devices improve
glycemic control by 0.8 percent on average in one year compared to conventional carefor patients with type 2 diabetes. Inan et al. [14] provide remote monitoring of heart
failure patients using wearable devices. Evaluating whether or not hospitalization is necessary for patients with heart failure using the lifelog of cardiac function collected fromwearable terminals enables adjusting the treatment specific to the patient and reducing
the number of hospitalized patients.
Thus, using IoT devices, including wearable terminals, enables users to measure
their health over a long period. However, more frequent management of IoT devices andmore actions required for health measurements can harm users.
Bonai [7] examines the possibility of diabetes treatment using IoT, and it is expectedthat long-term utilization of IoT affects improving blood glucose. However, the IoT utilization rate without support will be low; more than 10 percent stop using it immediately
after the introduction of IoT (within two weeks). Therefore, utilizing IoT devices formore than a year is not easy.
2.2. Relationship between oral condition and health
There are studies aimed at monitoring oral conditions. Shetty et al. [15] have developeda Remote Oral Behaviors Assessment System (ROBAS) using an electric toothbrush anda smartphone. It shows the possibility of accurately and reliably monitoring the brushingpattern in the home for a long time. Islam et al. [16] have proposed a system for monitoring pH in the oral cavity. They mention the system can monitor a decrease in PH, an
indicator of bacterial accumulation in the oral cavity, using a piezoelectric dental crown,
while compensating for lost teeth.
On the other hand, various studies were conducted on the relationship between oral
condition and the psychological condition and quality of life. A study investigating therelationship between depression, stress, self-esteem, and the short-form oral health impact profile (OHIP-14) in middle-aged women has reported that the lower the stress andthe higher the self-esteem, the higher the oral health impact index [17]. A study of 452
university students investigating the relationship between stress and oral symptoms using a self-reported questionnaire has reported that stress has a profound effect on thesymptoms of dry mouth, bad breath, and temporomandibular joint pain [18].
A study also investigates the relationship between oral health and general healthand quality of life in elderly male cancer patients [19]. It concludes elderly male cancer
patients who have problems with their mouth and teeth and have difficulty eating may
have a lower quality of life, poorer mental health, and lower levels of physical function
than those without these problems.
Based on these, it can be seen that the relationship between oral condition and stressis mentioned in a wide range of age groups. Understanding the oral condition is also
important for improving one’s mental and quality of life. In this study, we think that therelationship between bad breath and health conditions also occurs for recovery and work
engagement.

June 2021

Smart toothbrushFeature Sets

Feature Extraction

Classifier• Odor• Temperature• Humidity• Barometric Pressure
Evaluation value ofn Recovery Experimentn Recovery Staten Work Engagement

Figure 1. overview of the estimation model

Figure 2. a smart toothbrush “SMASH” developed by NOVENINE Co., Ltd.

3. Proposed Method
3.1. System Overview
In this study, we propose a system that collects halitosis and estimates recovery during
tooth brushing that many people perform every morning. Generally, a dedicated device isrequired to collect halitosis, but we use a smart toothbrush that integrates brushing teeth
and collecting halitosis in the proposed method. Since toothbrushes are indispensable indaily life, users can reduce the trouble of managing and charging new equipment. Allusers have to do to collect halitosis is two actions: to press the button on the toothbrushand blow for 3-5 seconds. Using halitosis collected from a smart toothbrush, users’ recovery and work engagement is estimated. There is a correlation between halitosis andpoor lifestyle and stress [20], and we assume that there is also a correlation between recovery and halitosis. In this study, we search for features that correlate with recovery andwork engagement and use them for estimation.
3.2. Estimation Model
Figure 1 shows the outline of the estimation model. The smart toothbrush collects thebreath blown by the user and then, extracts four types of features, odor, temperature, humidity, and barometric pressure with 1Hz sampling rate. The halitosis value is calculatedby the ratio of the maximum value and the minimum value of the resistance of the gas
sensor value in one measurement. For temperature, humidity, and barometric pressure,the maximum value, minimum value, median value, and standard deviation in one measurement are taken as feature quantities. The maximum, minimum, median, and standard
deviations of temperature, humidity, and barometric pressure are taken in as features.
The output of the model is a two-class evaluation of the sub-scales of recovery andwork engagement. We use the questionnaire about the recovery and work engagement
used in industrial insurance as the ground truth. We add up the evaluation values of theanswered questionnaires for each scale. They are divided into two classes, one is larger
than the center of the range that can be taken when the evaluation values are added up,
and the other is smaller.

4. Questionnaire about Recovery and Work Engagement
We use the questionnaire about recovery and work engagement. Recovery refers to theprocess of recovering from a stressed state [3]. It has been suggested that the process ofJune 2021
Table 1. recovery experience questionnaire
Table 2. recovery state questionnaire

I feel like I can decide for myself what to do.

COI learn new things.

MA

This morning I was able to physically refresh

I forget about work.

PD

This morning, I was able to mentally refresh

I decide my own schedule.

COHow do you rate your sleep quality as a whole?

I don’t think about work at all.

PD

I kick back and relax.

RE

I seek out intellectual challenges.

MA

I do things that challenge me.

MA

At my work, I feel bursting with energy

I determine for myself how I will spend my time.

CO

At my job, I feel strong and vigorous

VI

I distance myself from my work.

PD

I am enthusiastic about my job

DE

I do relaxing things.

RE

My job inspires meDE

I use the time to relax.

RE

When I get up in the morning, I feel like going to work

VI

I take care of things the way that I want them done.

COI feel happy when I am working intensely

AB

I take time for leisure.

PD

I am proud on the work that I do

DE

I do something to broaden my horizons.

MA

I am immersed in my workAB

I get a break from the demands of work.

RE

I get carried away when I’m working

ABPD = Psychological detachment , RE = Relaxation
MA = Mastery , CO = Control

Table 3. work engagement questionnaire
VI

VI = Vigor , DE = Dedication , AB = Absorption

recovery from work stress may be related to individual health and well-being and work
performance [4]. There are two indicators for recovery: (1) recovery experience and (2)recovery state. Work engagement is defined as a positive, fulfilling, work-related state ofmind that is characterized by vigor, dedication, and absorption [5].
4.1. Recovery Experience
Recovery experience is an index of experience of recovering from stress such as work.
Although the behavior for recovery varies from person to person, the underlying experience is divided into four distinct measures by Sonnentag et al. [3]. The four measures
are Psychological detachment, Relaxation, Mastery, and Control.
Psychological detachment means to leave work in a psychological sense. Relaxation
is a measure related to leisure activities. Mastery refers to how much you are engaged
in non-work activities such as rewarding experiences and learning opportunities. Control
indicates the degree to which you can decide what kind of activity you want to do during
your leisure time.
In this study, we use a questionnaire shown in Table 1 based on reference [3] [6].
All questions are rated on a scale of 5 from “1. Not applicable at all” to “5. Very wellapplicable”.
4.2. Recovery State
Recovery state refers to the state after recovery during the leisure period. There is acorrelation between morning recovery and work performance for the day [8]. Therefore,checking the recovery status in the morning is important for workers to face their work. In
this study, we created a questionnaire quoting the mental and physical refreshment used
in Reference [8] and the questions about sleep quality from Reference [9]. Reference [9]is widely used as an evaluation of sleep disorders.
Table 2 shows the actual questions and their order. There are two questions about
refreshing mentally and physically: “This morning I was able to physically refresh” and“This morning, I was able to mentally refresh”. These questions are rated on a scale ofJune 2021

5 from “1. Not applicable at all” to “5. Very well-applicable”. The question about sleep
quality is “How do you rate your sleep quality as a whole?”. This question is rated on afour-point scale from “1. very bad” to “4. very good”.
4.3. Work Engagement
Work engagement is defined as a positive, fulfilling, work-related state of mind that ischaracterized by vigor, dedication, and absorption [5]. Work engagement is related to adecrease in ill-health and to increases in both life satisfaction and job performance [6].
Schaufeli et al. define vigor, dedication, and absorption [5]. Based on Schaufeli et al., we
explain them below. Vigor is characterized by high levels of energy and mental resilience
while working, the willingness to invest effort in one’s work, and persistence even in theface of difficulties. Dedication is characterized by a sense of significance, enthusiasm,
inspiration, pride, and challenge. Absorption is characterized by being fully concentrated
and deeply engrossed in one’s work.
In this study, we use the Japanese version of the Utrecht Work Engagement Scale(UWES-9) to measure work engagement [21]. Table 3 shows the questionnaire items of
UWES-9. These questionnaire items are evaluated on a 7-point scale from ”1: none” to”7: always feel”.

5. Experiment
In this section, we describe the experiment to evaluate the proposed method. The purposeof this experiment is to verify estimating recovery and work engagement from halitosis
data collected when waking up. The experimental period was two and a half months, and12 subjects were recruited.
5.1. Data Collection
In this section, we describe how to collect data. To collect halitosis data, we used a smart
toothbrush “SMASH” developed by NOVENINE Co., Ltd.2 , that can measure halitosis
by a gas sensor(Fig. 2). The subjects blew on the smart toothbrush to measure halitosis
data before tooth brushing in the morning. The subjects collected their halitosis data using SMASH as soon as getting up every morning. All the subjects have to do for measuring is press the button on SMASH and blow for about 5 seconds before tooth brushing.
In addition, they answered the questionnaires shown in Section 4 by smartphone in/after
tooth brushing. In the experiment period of approximately two and a half months, wecollected valid data of 581 days.
The questionnaire used as the ground truth was divided into two values for eachindex. About recovery experience and work engagement, the values of the questionnaires
on each index were summed and classified according to whether they were larger orsmaller than the median of the possible range. About the recovery state, the answers
obtained with 4 or 5 values were divided into 2 values.
2 NOVENINE Co., Ltd. “SMASH”: https://novenine.com/

June 2021

5.2. Model Evaluation
This evaluation constructed and compared estimation models for each index using Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and Random Forest (RF). For
SVM and KNN, we selected the features with significant differences between the twoclasses based on a statistical test as inputs to model. For Random Forest, we used allfeatures as inputs because the algorithm classifies based on feature importance. Table
4 shows the results of the Kruskal-Walis test for each index. We used all features withsignificant difference for each index.
We verified models by Leave-One-Person-Out Cross-Validation. We used F-score,
the harmonic mean of precision and recall, to evaluate models. Precision indicates thepercentage that the results predicted by the model were correct. Recall indicates the percentage of the actual results that were predicted correctly. The imbalanced data was adjusted by downsampling. Table 5 shows three types of model evaluations in the recovery
experience. It can be seen that the F-score is high for Mastery and Psychological detachment. We assume that this is because many features have a significant difference between
the classes of these two scales. In addition, F-score of the random-forest estimation ishigher than that of the other models for all indexes. Table 6 shows three types of model
evaluations in the recovery experience. The KNN estimation model has the highest Fscore for sleep quality among the three measures, and the SVM estimation model has
the highest F-score for mental refresh among the three measures. F-score of the randomforest estimation is higher than that of the other models for all indexes. Table 7 shows
three types of model evaluations in the recovery experience. F-score in Dedication is thehighest among the three indexes in any estimation model. We infer that many featuresthat show significant differences between the two classes of Dedication are the factor. In
addition, the estimation accuracy by random forest is high for all indexes.
Table 4. Kruskal-Wallis test on the questionnaireand each featureindex

features

Controlt med, t min, bP med, bP min, h max, t max, bP max

Masteryodor, t med, t min, t max, bP med, bP min, bP max

PDh med, h min, h max, bP med, bP min, bP max

RelaxationbP med, bP min, bP max

Physical refresht med, t min, bP std, bP med, bP min, t max, bP max

Mental refreshbP std, bP med, bP min, bP max

Sleep quality

bP med, bP min , bP std, bP max

Vigor

h med, h min, h max, bP std, t std

Dedication

odor, h med, bP min, bP std, h min, h max, bP max, bP med

Absorption

h med, h min, odor, t med, h max, t max

t = temperature , h = humidity , bP = barometricPressure

5.3. Discussion
Based on the results of model evaluation, the estimation by Random-Forest was shown
to be the most useful among the three proposed models. In addition, considering thehigh estimation accuracy of random forest for all indexes, it is important to consider notJune 2021
Table 5. F-score of recovery experience
KNN

RF

SVM

Table 6. F-score of recovery state

Control

0.36

0.76

0.43

KNN

RF

SVM

Mastery

0.55

0.79

0.54

Physical refresh

0.53

0.73

0.55

PD

0.58

0.79

0.57

Mental refresh

0.46

0.73

0.57

Relaxation
0.42
0.69
0.50
PD = Psychological detachment

Sleep quality

0.54

0.73

0.53

Table 8. data number of each class

Table 7. F-score of work engagement
KNN

RF

SVM

Vigor

0.50

0.81

0.60

Dedication

0.57

0.84

0.61

Absorption
0.41
0.80
RF = Random Forest

0.50

class 0 (low)class 1 (high)Control

75

467

Mastery

346

178

PD

220

307

Relaxation

117

421

Physical refresh

101

317

Mental refresh

101

323

Sleep quality

178

403

Vigor

325

143

Dedication

286

217

Absorption

279

200

PD = Psychological detachment

only the relationship between single features but also various relationships among them.
Table 9, table 10, table 11 and table 12 show the confusion matrix in the estimation of
the recovery experience by Random-Forest. For Control and Relax, we can see that the
precision for 0 is low. This may be due to the biased number of data about Control andRelax. Table 8 shows the number of data for each class. We can see that the number ofminority data is smaller for Control and Relax than for the other indexes. As for work
engagement, table 8 shows that there is little difference in the number of data, and itis thought that the precision of a minority class did not decrease more than recovery
experience and recovery state.
We also checked the importance of the features for the estimation model in the Random Forest. For the recovery experience, the importance of odor, humidity, and barometric pressure was high, while the importance of temperature was relatively low. In the statistical test, there was a significant difference in temperature, but the importance was not
so large, which could be one of the reasons for the low accuracy in the estimation modelby SVM and KNN. For the recovery state, no feature was considered to be outstandingly
important compared to the other indexes. Therefore, we consider that the accuracy improvement needs more data and other features. In the case of work engagement, the importance of odor and humidity was large. The humidity showed a significant differencein the statistical test, which contributes considerably to estimating work engagement.
In general, even when there was no significant difference between the classes for asingle feature, there were some features that had large importance in the Random forestand contributed to the improvement of accuracy. Therefore, it was shown that estimation
by Random Forest using the features used in this study is effective in recovery and work

June 2021
Table 9. Control

True
Label

0
1

Predicted Label0 (low)
1 (high)
62
13
67
400

Table 12. Relaxation

True
Label

0
1

Predicted Label0 (low)
1 (high)
80
37
93
328

Table 10. Mastery

True
Label

Table 13. Physical refresh

True
Label

Table 15. Sleep quality

True
Label

0
1

Predicted Label0 (low)
1 (high)
130
48
95
308

0
1

Table 11. PD

Predicted Label0 (low)
1 (high)
296
50
47
131

0
1

Predicted Label0 (low)
1 (high)
81
20
78
239

Table 16. Vigor

True
Label

0
1

True
Label

0
1

Predicted Label0 (low)
1 (high)
177
43
66
241

Table 14. Mental refresh

True
Label

0
1

Predicted Label0 (low)
1 (high)
84
17
82
241

Table 17. Dedication

Predicted Label0 (low)
1 (high)
292
33
42
101

True
Label

0
1

Predicted Label0 (low)
1 (high)
264
22
56
161

Table 18. Absorption
Predicted Label0 (low)
1 (high)
True
Label

0
1

246
60

33
140

engagement. In particular, F-score of over 0.80 was achieved for all measures of work
engagement, which greatly contributed to the estimation of engagement from halitosis.

6. Conclusion
In this paper, we proposed a system that periodically estimates recovery and work engagement through tooth brushing, an activity of daily living. Based on the assumptionthat the effect of recovery and engagement in the previous day strongly appears to be thewake-up halitosis, the proposed system collects halitosis data by a smart toothbrush witha gas sensor and then estimates recovery and engagement scales.
To evaluate the proposed method, we collected halitosis data and questionnaires
about recovery and work engagement from 12 subjects every day for approximately twomonths and constructed three models to estimate each index of the questionnaires basedon the halitosis data. As a result, we achieved a F-score of 0.84, 0.81, and 0.80 in ”Dedication”, ”Vigor”, and ”Absorption”, which are indexes of Work Engagement, estimatedby Random Forest with binary classification.
Our future work is the collection of minority data and the increase of the number offeatures. We are planning to continue our experiments with the smart toothbrush. In addition, we would like to increase the number of features that can be collected from daily
life activities to further improve accuracy. Furthermore, we try to deal with individual
differences by building a model for each individual.

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