Machine Learning for Fatigue Detection using Fitbit Fitness Trackers

Erik Johannes Husom

1 a

, Rustem Dautov

1 b

, Adela-Aniela Nedisan

1,2 c

, Fotis Gonidis

3 d

Spyridon Papatzelos

3 e

and Nikolaos Malamas

3 f

SINTEF Digital, Forskningsveien 1, 0373 Oslo, Norway

University of Oslo, Department of Informatics, Gaustadall

een 23B, 0373 Oslo, Norway

Gnomon Informatics SA, Antoni Tritsi 21, 57001 Thessaloniki, Greece

Keywords:

Machine Learning, Fatigue Detection, Fatigue Assessment Scale, Healthcare, Fitness Trackers, Fitbit.

Abstract:

Fatigue can be a pre-cursor to many illnesses and injuries, and cause fatal work-related incidents. Fatigue de-

tection has been traditionally performed in lab conditions with stationary medical-grade diagnostics equipment

for electroencephalography making it impractical for many in-ﬁeld scenarios. More recently, the ubiquitous

use of wearable sensor-enabled technologies in sports, everyday life or ﬁeldwork has enabled collecting large

amounts of physiological information. According to recent studies, the collected biomarkers related to sleep,

physical activity or heart rate have proven to be in correlation with fatigue, making it a natural ﬁt for apply-

ing automated data analysis using Machine Learning. Accordingly, this paper presents our novel Machine

Learning-driven approach to fatigue detection using biomarkers collected by general-purpose wearable ﬁtness

trackers. The developed method can successfully predict fatigue symptoms among target users, and the overall

methodology can be further extended to other diagnostics scenarios which rely on collected wearable data.

1 INTRODUCTION

Many industries, such as maritime, construction or

oil & gas, still depend on extensive manual labour

to be done in the ﬁeld, i.e. in remote working loca-

tions away from social services and basic healthcare

facilities. Field workers are often exposed to hostile

working conditions, including tough physical work,

lack of recreational activities, homesickness, rough

sea weather, etc. – all these factors often lead to in-

creased fatigue and stress levels. Occupational acci-

dents resulting from poor physical and mental condi-

tions can easily escalate to life-threatening situations,

given that proper medical assistance is not always

accessible. Albeit at a much lower scale, same is-

sues apply to people involved in long-term endurance

sports, such as ocean sailing and mountain hiking, and

expeditions. Once on the route, these people often be-

come completely autonomous and disconnected from

https://orcid.org/0000-0002-9325-1604

https://orcid.org/0000-0002-0260-6343

https://orcid.org/0000-0001-8514-8933

https://orcid.org/0000-0002-5605-4249

https://orcid.org/0000-0002-2199-4089

https://orcid.org/0000-0001-8006-145X

the world for several weeks or even months.

To partially address these challenges, some organ-

isations implement regulatory approaches in order to

control and reduce fatigue-related risks, such as com-

pliance to hours of service (HoS) regulations, alterna-

tively employing a fatigue risk management system

(FRMS) (Gander et al., 2011) and following rostering

principles (S¸ahinkaya and Oktal, 2021). Many indus-

tries also impose mandatory health assessment proce-

dures for their employees before departure allowing

them to work for an approved period of time. Such

one-off checks, however, do not properly reﬂect the

state of affairs over a period of time, as workers might

develop illnesses and other disorders during their ex-

tended ﬁeld work. More frequent health assessments

would address this challenge, but are rarely imple-

mented in practice due to the high costs of hiring and

transporting medical staff and equipment.

In these circumstances, telemedicine and remote

patient monitoring solutions are increasingly used

to automate remote health-related procedures in the

ﬁeld. More speciﬁcally, the increased use of sensor-

rich wearable devices enabled real-time collection of

large amounts of physiological data, which can then

be automatically fed into medical diagnostics soft-

ware systems.

Husom, E., Dautov, R., Nedisan, A., Gonidis, F., Papatzelos, S. and Malamas, N.

Machine Learning for Fatigue Detection using Fitbit Fitness Trackers.

DOI: 10.5220/0011527500003321

In Proceedings of the 10th International Conference on Sport Sciences Research and Technology Support (icSPORTS 2022), pages 41-52

ISBN: 978-989-758-610-1; ISSN: 2184-3201

Traditionally, fatigue detection has relied on the

electroencephalogram (EEG) method to collect data

in lab conditions. The situation is now changing

with the rapid spread of small-size general-purpose

wearable devices, such as smartwatches, ﬁtness track-

ers and chest belts, which are able to collect multi-

dimensional physiological data required to perform

fatigue detection using Machine Learning (ML) tech-

niques in a timely and efﬁcient manner.

Accordingly, the contribution of this paper is two-

fold. First, we propose an automated fatigue de-

tection approach based on the hypothesis that multi-

dimensional biomarkers collected by general-purpose

wearables can be precisely and unambiguously cor-

related with fatigue levels. Second, we implement

this approach using ML techniques to automate the

fatigue detection task. This implementation relies on

pre-collected training data from Fitbit ﬁtness trackers

and fatigue assessment questionnaires manually ﬁlled

in by the users during data collection.

The rest of the paper is organised as follows. Sec-

tion 2 familiarises the reader on the topic of fatigue

and the limitations of the currently adopted tools for

fatigue detection, followed by the description of the

proposed approach. Section 3 overviews the related

works, highlighting existing gaps. Section 4 proceeds

with a detailed description of the data collected and

used for fatigue detection in our work, and explains

the technical details underpinning the data prepara-

tion and model training activities. Section 5 evaluates

the obtained results, critically highlighting identiﬁed

limitations and threats to validity. Section 6 closes the

paper with some concluding remarks and directions

for future work.

2 BACKGROUND, MOTIVATION,

AND PROPOSED APPROACH

2.1 Fatigue

60-80% of workplace accidents are considered to be

the result of stress-induced issues, different manifes-

tations of stress such as fatigue and lack of energy

having an impact on employees’ ability to safely per-

form their work duty (Christ, 2016). Among var-

ious human factors contributing to work accidents

are personal problems, environmental stress, oper-

ational stress, boredom, frustration, fatigue, morale

and health (Gordon, 1998). Even though many con-

cepts further discussed in this paper are relevant to

all of these factors, in the rest of the paper we mostly

focus on fatigue as one of the most critical, yet under-

explored topics.

Fatigue has been attributed many deﬁnitions, of-

ten depending on the context of the experiment it is

used in (Hockey, 2013; Marino, 2019). In our case,

we will use this term to refer to a state of human tired-

ness that does not resolve with rest or sleep, typically

resulting from prolonged physical or mental activity.

When it occurs independently of physical or mental

exertion and does not resolve after rest or sleep, it may

be a symptom of a severe medical condition. There-

fore, fatigue is considered as a major safety hazard

characterised by degraded performance and implicitly

higher error rates. Individual differences and one’s

state of affairs inﬂuence how interventions are experi-

enced by each person, thus calling for a differentiated

user-tailored approach, such as measuring and mod-

elling theoretically relevant individual differences and

contextual variables in an objective way in order to

capture the complex relation between stressors and

well-being (Ganster and Rosen, 2013).

The use of electroencephalograms (EEG) has es-

tablished itself as an accurate and widely used method

for fatigue detection (Karuppusamy and Kang, 2020),

able to determine the onset of fatigue at an early stage

(Kudo et al., 2017). EEGs are used to monitor dif-

ferent brain waves that can be linked to fatigue, using

several frequency bands such as alpha (8–13 Hz), beta

(13–35 Hz), theta (4–7 Hz), and delta waves (0.5–4

Hz) (Stern, 2005). The drawback of using EEG in-

dexes is the hardware itself, which is quite a com-

plex and expensive piece of machinery, often requir-

ing special assistance to operate.

An alternative method is percentage eye openness

tracking (PERCLOS) (Zhang et al., 2021). PERC-

LOS tools rely on continuous video capturing of a

person’s sight and using image processing in order

to determine where the eyes are located, and whether

current eye movements can be correlated to the state

of fatigue. The use of PERCLOS in real-life working

conditions is limited due to several hindering factors,

such as insufﬁcient illumination, objects blocking the

face (e.g. sunglasses or baseball caps), turning the

face to the side, or unusual facial expression or emo-

tions (e.g. crying) (Srivastava and Tiwari, 2021).

2.2 Motivation and Proposed Approach

Fatigue-caused accidents can be avoided provided

that signs of fatigue are recognised early enough

(Hellesøy, 1985). Some humans manage to notice

symptoms of fatigue early on, but in most cases the

signs are recognised only once fatigue affects their

physical capabilities signiﬁcantly. Automating this

task using some standard hardware and software tools

would considerably improve the fatigue diagnostics,

icSPORTS 2022 - 10th International Conference on Sport Sciences Research and Technology Support

Figure 1: Conceptual architecture of the proposed approach.

not only by making it less unbiased and ambiguous,

but also by achieving much faster results computed

based on large amounts of collected data.

While EEG and PERCLOS tools can provide data

for automated diagnosis, they are too heavy-weight

and not portable to be used anywhere but in a lab.

Using such tools frequently is not always convenient,

and the fact that a patient opted for such a lab study to

diagnose fatigue typically means that some health de-

terioration has been developing over a period of time.

Taken together, there are two main limitations of the

current state of practice for fatigue detection tools –

namely, limited portability and infrequent use.

With the rapid advances in microelectronics de-

sign, sensor technology, and signal processing, var-

ious kinds of ‘wearable’ devices found their way to

the market. Ranging from the more traditional smart

watches and wristbands to more innovative smart

clothes and jewellery, their technological underpin-

nings are similar. They all rely on miniature em-

bedded sensors to collect various physiological in-

formation about the human body. These collected

biomarkers are an index of an individual’s physiologi-

cal state, and performance degradation can be approx-

imated based on some of these collectable body indi-

cators. Moreover, such computation more and more

often takes place either directly on sensor devices or

on users’ smartphones, which act as gateways for col-

lected physiological data, thus achieving timely re-

sults (Dautov et al., 2017).

Accordingly, in this paper we aim to address the

two aforementioned limitations by making use of

physiological data collected by wearable devices in

order to diagnose fatigue. As opposed to in-lab sta-

tionary equipment, small general-purpose wearable

devices are more light-weight and user-friendly and

less invasive. The approach puts forward the hy-

pothesis that fatigue, albeit best detected directly us-

ing the traditional in-lab technology, can also be pre-

cisely and unambiguously correlated with some com-

mon physiological biomarkers collected or computed

by wearable sensors. In other words, we argue that

particular combinations of indicators which register

a detrimental change in the physical state of an indi-

vidual could be regarded as fatigue. As we further

discuss in more details below, these biomarkers may

include, for example, sleep activity, daytime physical

activity or heart rate data such as variability (HRV).

HRV (Matuz et al., 2021; Patel et al., 2011) and sleep

patterns (Virk et al., 2022) are highly linked to fatigue

levels, and have the advantage of being easily accessi-

ble through general-purpose ﬁtness trackers equipped

with electrodermal activity sensors. These devices

(e.g. Fitbit) can provide timely feedback on the user’s

stress level based on sweat microbursts, and calcu-

late the so-called stress management score, in general

terms based on exertion balance, sleep patterns and

responsiveness (Watters, 2020). In this context, pro-

cessing of large amounts of collected time-series data

with some hidden patterns and correlations to be iden-

tiﬁed goes beyond the manual capabilities and natu-

rally calls for ML techniques to be applied. The high-

level conceptual architecture of this approach is de-

picted in Figure 1, highlighting the two key phases –

namely, model training and run-time operation. The

technical details of this implementation are further

discussed in Section 4.

3 RELATED WORKS

Fitness technology allows to capture the dynamicity

of activity-related data by collecting digital biomark-

ers and to correlate these cues with the development

of fatigue over time. Current technological advance-

ments in sensors have led to the emergence of a new

Machine Learning for Fatigue Detection using Fitbit Fitness Trackers

class of biomarkers used primarily in aiding the con-

tinuous monitoring of an individual’s health status.

Digital biomarkers are deﬁned as objective, quantiﬁ-

able physiological and behavioural indicators that are

collected and measured by means of digital devices

with the goal of explaining, inﬂuencing and/or pre-

dicting health-related outcomes (Wang et al., 2016;

Villa et al., 2020). Conclusively, clinically meaning-

ful, objective data can be captured by collecting and

analyzing these biomarkers. Some digital biomark-

ers that are common to relevant studies reﬂect knowl-

edge about activity levels (e.g. number of steps, ac-

tivity counts), walking speed (e.g. gait speed), vi-

tal signs (e.g. heart rate, heart rate variability, skin

temperature, respiratory rate) and sleep patterns (e.g.

sleep duration, time in different sleep stages) (Low

et al., 2021), and can be expressed in various units

(Vega et al., 2020). The devices used for collect-

ing these biomarkers are equipped with sensors that

may be placed on the human body, such as smart-

phone sensors (e.g. gyroscope, accelerometer) (Hamy

et al., 2020), wearable sensors (e.g. smartwatch,

ﬁtness bracelet) (Kaewkannate and Kim, 2016), or

sense the surrounding environment, such as home and

ambient sensors (e.g. temperature, luminosity, noise

level, air quality, motion) (Alam et al., 2016; Mielke

et al., 2020; Sheikh et al., 2021). Some common

wearable devices used in studies are the ActiGraph

monitors, Vital Patch, Empatica EM4, Everion, Neu-

rosky Mindware (a type of EEG) and Shimmer IMU

Device. Diverse aspects linked to the quality of life

can be monitored, assessed and managed by contin-

uous, high-resolution, unbiased measurements such

as those provided by biomarkers (Kim et al., 2019;

Wilbur et al., 2018). To give some examples, Acti-

GraphGT3X (Hallman et al., 2015), ActiGraphGT1M

(Merriwether et al., 2018) and ActiGraphGT9X Link-

based studies (Perraudin et al., 2018) show how the

amount of time spent doing speciﬁc activities can lead

to the development of pain. This collected informa-

tion can then aid in improving pain treatment and as-

sessment (Leroux et al., 2021). Fatigue can also be

assessed both subjectively and objectively. Studies

show that multimodal digital data can be used with

success to capture self-reported non-pathological fa-

tigue measures (Luo et al., 2020). Some clinical as-

sessment tools for fatigue include one- or multidimen-

sional self-report instruments, such as the Visual Ana-

logue Scale (VAS) and the Fatigue Assesment Scale

(FAS). Everion devices paired with VAS (Luo et al.,

2020; Lee et al., 1991) and FAS (Michielsen et al.,

2003) reveal interesting correlations between fatigue

levels and speciﬁc measurements such as heart rate

variability, respiration rate, heart rate, activity counts

(sum of different activities), number of steps and en-

ergy expenditure. Other studies show how whole-

body measurements, such as accelerations, inclina-

tion angles, movement variability, duration and rep-

etitions, could also be regarded as fatigue indicators

(Maman et al., 2017). Other indices for fatigue and

poor mental health are sleep quality (Lavidor et al.,

2003), sleep duration (Wang et al., 2018), time spent

outdoors (Petersen et al., 2015), phone usage (Jacob-

son et al., 2020), speech characteristics (Lu et al.,

2012; Milosevic, 1997), unintended weight changes

and loss of interest or pleasure (American Psychiatric

Association, 2013; Sheikh et al., 2021). Fatigue is

also regarded as a symptom when identifying cancer

and is also present after treatment has been initiated

(Hofman et al., 2007). Thus, there are promising ev-

idences to support the conversion of the previously-

mentioned biomarkers and other additional cues (e.g.

age) into a set of categorical/numerical features used

for automated fatigue assessment. Once the potential

of ﬁtness technology is better explored, one should

expect signiﬁcant improvement in the areas of sport

medicine research, support technologies and gener-

ally public health. Using ML for fatigue assessment

has been explored for ECG and actigraphy sensors

in a study (Bai et al., 2020), where the participants

wore two medical-grade devices for seven days, and

assessed their fatigue during this period on a scale

from 0 to 10. Models created with linear regression

and LSTM neural networks were compared, with the

results showing that the latter method had the best per-

formance, using bothwb ECG and actigraphy as input

to the model.

4 IMPLEMENTATION

We now proceed with the explanation of Step 1: ML

Training of the proposed approach (Figure 1). We

ﬁrst describe what the data set consists of and how

we collected it. Next, we explain the required pre-

processing steps and proceed with actual model train-

ing experiments.

4.1 Data Collection

All subjects of the study used a ﬁtness tracker from

the brand Fitbit. The speciﬁc model was Fitbit Charge

worn around the wrist. The sensors in this ﬁt-

ness tracker include a 3-axis accelerometer, an optical

heart rate sensor, red and infrared sensors capable of

https://www.ﬁtbit.com/global/us/products/trackers/

charge5

icSPORTS 2022 - 10th International Conference on Sport Sciences Research and Technology Support

measuring oxygen saturation, and a temperature sen-

sor. Fitbit provides cloud services that compute sev-

eral health- and wellbeing-related variables from the

raw data of the ﬁtness tracker, and these variables can

either be downloaded manually from a user’s online

Fitbit account, or accessed through an API provided

by Fitbit. The Fitbit API does not expose all vari-

ables that are available in the manually downloaded

data. For example, the heart rate variability and var-

ious metrics describing the sleep quality (e.g. rest-

lessness score and duration score) are not available

through the API, but can be accessed when down-

loading the data manually through a user’s online ac-

count. In order to produce models that can be used

in an application integrated with the Fitbit cloud ser-

vices, we have restricted ourselves to using variables

that are available from the API. The variables that are

automatically collected and are available through the

Fitbit API are shown in Table 1 (some variables are

left out from the table because they provide redun-

dant information to the ones listed).Learning In addi-

tion to the data collected automatically by the activity

tracker, each person’s age, weight, height and gender

were also recorded.

Table 1: Available variables from the data set collected with

the Fitbit ﬁtness tracker. The sleep stages include: Deep

sleep, light sleep, REM sleep and awake.

Type Variable Granularity

Activity

Calories burned Daily

Number of ﬂoors Daily

Sedentary minutes Daily

Lightly active

Daily

minutes

Fairly minutes Daily

Very minutes Daily

Number of steps Daily

Distance walked Daily

Heart rate

Heart rate time series 1 second

Resting heart rate Daily

Sleep

Duration Daily

Efﬁciency Daily

Start time Daily

End time Daily

Main sleep or nap Daily

Sleep stage duration 1 second

Number of

Dailyoccurences of

sleep stage

The participants wore the Fitbit ﬁtness tracker in

their daily life for seven days, both during day- and

night-time. They were not told to follow any speciﬁc

study protocol. The data collected from the activity

Figure 2: Distribution of FAS-scores in the collected

dataset.

tracker was then downloaded from each of the sub-

jects’ user accounts. At the end of the seven days,

the participants ﬁlled out a questionnaire in order to

give them a score on the Fatigue Assessment Scale

(FAS) (Michielsen et al., 2003). This is a scale that at-

tempts to assess the level of chronic fatigue for a per-

son, and has been shown to be one of the most promis-

ing fatigue questionnaires (De Vries et al., 2003). The

questionnaire consists of 10 statements about how a

person feels, where each statement can be ranked on

a scale from 1 (never) to 5 (always). The score given

to each of the statements are summed, resulting in a

FAS-score in the range 10-50. Table 2 shows how the

FAS-score can be put in to three distinct categories

indicating the level of chronic fatigue of a subject.

Table 2: Ranges of the Fatigue Assessment Scale (FAS) and

the corresponding categories.

FAS-score Category

10-21 No fatigue

22-34 Fatigue

35-50 Extreme fatigue

The data was collected with consent from all par-

ticipants, and in accordance with the data protection

regulations in Greece. After the collection, the data

was also anonymised (i.e. any personally identiﬁable

information was removed) before sharing it externally

for training. Secure storage and transferring of the

data was ensured at all times to prevent unauthorised

access or disclosure.

4.1.1 Participants and Dataset Distribution

In total, 35 subjects participated in the data collection,

of which 31 were female and 4 were male. The mean

age of the subjects were 45±13 years. Figure 2 shows

the age distribution of the dataset, and Figure 3 shows

the distribution of FAS-scores.

4.2 Data Preprocessing

The data preparation pipeline is based on our previ-

ous work addressing similar challenges (Husom et al.,

Machine Learning for Fatigue Detection using Fitbit Fitness Trackers

Figure 3: Distribution of FAS-scores in the collected

dataset.

Table 3: Features extracted from the Fitbit ﬁtness tracker

data. PCC represents the Pearson Correlation Coefﬁcient

between each feature and the FAS-score. The twelve fea-

tures with the strongest (including both positive and nega-

tive values) PCC is highlighted with bold typeface.

Feature name Unit PCC

Sleep total duration minutes 0.072

Sleep efﬁciency score, 0-100 -0.066

Deep sleep duration minutes -0.054

Light sleep duration minutes 0.173

REM sleep duration minutes -0.059

Awake in bed duration minutes -0.029

Deep sleep count count -0.047

Light sleep count count 0.085

REM sleep count count -0.109

Awake in bed count count -0.012

Sedentary minutes minutes 0.138

Lightly active minutes minutes -0.082

Fairly active minutes minutes 0.027

Very active minutes minutes -0.149

Average heart rate beats per min 0.211

Minimum heart rate beats per min 0.333

Maximum heart rate beats per min 0.027

Resting heart rate beats per min 0.265

Calories burned kcal -0.092

Steps count -0.055

Distance meters -0.068

Age years 0.338

Gender female/male -0.394

Weight kg 0.143

Height cm -0.193

Body Mass Index kg/m

0.275

2022; Sen et al., 2021) and partially repeats some of

the generic reusable steps, while task-speciﬁc tasks,

i.e. related to the Fitbit data format and the target ap-

plication scenario, have been developed from scratch.

From the variables available from the Fitbit API (see

Table 1 we extracted a set of features to use as input

to the ML models. These features are presented in

Table 3, together with the Pearson Correlation Coef-

ﬁcient (PCC) ρ to the FAS-score. The PCC between

two variables X and y is used to evaluate the linear

correlation between them, and is calculated using the

formula (Pearson, 1896):

X,y

cov(X, y)

σ(X)σ(y)

, (1)

where cov is the covariance and σ is the standard

deviation.

While most of the features in Table 3 are taken

directly from the Fitbit API without any feature en-

gineering, we have extracted the average, minimum

and maximum from the heart rate time series. In ad-

dition, both the duration and the number of occur-

rences of the four different sleep stages (deep sleep,

light sleep, REM sleep and awake) are used as input

features. Lastly, we calculated the Body Mass Index

(BMI) for each subject:

BMI =

(2)

where w is the body weight in kilograms, and h is

the height in meters. We added this value as an input

feature, since it showed a mild positive correlation to

the FAS-score (ρ = 0.275).

We selected the twelve features with strongest cor-

relation (either positive or negative) to the FAS-score

to be input features when training our ML models.

These features are highlighted with bold typeface in

Table 3.

The data set was split into a training set (70%)

and a test set (30%). The training data set was used

for building ML models (Step 1 in Figure 1), while

the test data set was used for evaluating the model

performance using the metrics described in Section

4.3 (used to simulate Step 2 in Figure 1). All input

features were scaled down to the range [0, 1], in order

to be given equal weight when processed by the ML

algorithms.

We investigated how the number of input time

steps affected the models’ ability to estimate the FAS-

score. The features are calculated in a way that gives

one data point per day, and the subjects wore the ac-

tivity tracker for seven days, which means that we can

use from one to seven data points as input to the mod-

els.

4.3 Creating Machine Learning Models

We applied and compared six different ML algo-

rithms in our attempt to create a model that can es-

timate the FAS-score based on ﬁtness tracker data:

Decision Tree (DT), Random Forest (RF), XGBoost

(XGB), k-Nearest Neighbor (kNN), Fully-Connected

Neural Network (FCNN) and LSTM (Long Short-

Term Memory) neural network. The goal of all these

icSPORTS 2022 - 10th International Conference on Sport Sciences Research and Technology Support

algorithms is to create a mapping between a set of in-

put features X, in this case the data collected using

the Fitbit tracker, and an output target y, which in our

context is the FAS-score. DT is a method for cre-

ating a model based on decision rules learned from

the input features. RF and XGB are ensemble learn-

ers, which means that the models are combinations of

multiple base estimators. For both ensemble learn-

ers we have used decision trees as the base estimator.

kNN is an algorithm that predicts the output of a given

input based on the average of the k nearest neighbors

in the training data set. FCNN and LSTM are deep

learning algorithms. FCNN is a feed-forward neural

network, where information only passes in one direc-

tion, while LSTM is a type of recurrent neural net-

work, where the units of the network have feedback

connections and are speciﬁcally designed to handle

sequential input data (Hochreiter and Schmidhuber,

1997). To create models with the neural network al-

gorithms, FCNN and LSTM, we used the library Ten-

sorFlow

and the Keras API.

The remaining methods

were used through the Scikit-learn

library.

The various ML algorithms have several hyper-

parameters that control the conﬁguration of the al-

gorithm and the training process. These hyper-

parameters must be tuned in order to create models

with high-performance. This is done by running mul-

tiple experiments with different conﬁgurations, and

using a small part of the training data as validation

data. The validation data will not be used directly

for training, but for measuring the performance of

the model with different choices of hyper-parameters.

The hyper-parameters for each ML algorithm, except

FCNN and LSTM, are shown in Table 4, where we

also present the values and ranges that were a part

of the hyper-parameter search. The hyper-parameter

tuning for the algorithms listed in Table 4 was per-

formed Scikit-learn’s built-in functionality using ran-

dom search. While grid search and manual search for

the best combination of hyper-parameters have been

widely used, random search has proven to be more

efﬁcient (Bergstra and Bengio, 2012). In many cases

the search space for hyper-parameters is enormous,

which makes random search the only viable option.

For the deep learning methods, FCNN and LSTM,

automatic hyper-parameter tuning is very computa-

tionally expensive, because not only does the result

depend on the number of layers, nodes, which activa-

tion function is used etc., but also on the number of

training epochs (i.e. training duration). We opted for

using a manual trial-and-error process for choosing

https://www.tensorﬂow.org/

https://keras.io/

https://scikit-learn.org/

the architecture for the neural networks, since such an

approach is easier to monitor and control. Further-

more, we aimed for keeping the neural network ar-

chitectures simple to keep the computational cost of

running the models low, increasing the usability on

resource-constrained devices (e.g. running ML infer-

ence locally on smartphones in case network connec-

tivity is limited). We started with networks consisting

of few layers and nodes, and expanded the complexity

while monitoring the error metrics until we observed

promising performance. We arrived at the following

conﬁgurations of the deep learning methods:

• FCNN: 1 hidden layer with 8 nodes and Rectiﬁed

Linear Unit (ReLU) activation in each node.

• LSTM: 1 LSTM layer with 8 hidden units and sig-

moid activation in each unit.

To evaluate the performance of the models we use

the Mean Squared Error (MSE), deﬁned as:

MSE =

∑

i=1

( ˆy

− y

)

, (3)

where n is the number of samples, y is the actual

values and ˆy is the predicted values. The MSE is a

common error metric for regression models, and is

typically used when training models since it measures

the difference between the predictions and the ground

truth. We also use R

-score, often referred to as the

coefﬁcient of determination:

= 1 −

∑

i=1

− ˆy

)

∑

i=1

− ¯y)

, (4)

where ¯y is deﬁned as:

¯y =

∑

i=1

, (5)

i.e. the mean of the true observations y. The R

score represents the ratio between the variance ex-

plainable by the model and the total variance. A per-

fect ﬁt will give an R

-score of 1, while a score of 0

will indicate that the model performs equally to pre-

dicting the mean of the actual observations for any

input. This error metric has the advantage of being

interpretable independent of the input variables, un-

like MSE, where the magnitude of the error depends

on the scale of the input data.

Machine Learning for Fatigue Detection using Fitbit Fitness Trackers

Table 4: Hyper-parameters for the ML algorithms and the values that were tested to tune them.

ML algorithm Hyper-parameter Values

Max depth 2, 5, 10, 15, 20, 50, 100

Minimum samples split 2, 5, 10

Minimum samples leaf 1, 3, 5

Max depth 2, 5, 10, 15, 20, 50, 100

Number of estimators 50, 100, 200, 400, 600, 800, 1000, 1200

Minimum samples split 2, 5, 10

Minimum samples leaf 1, 3, 5

XGB

Max depth 2, 5, 10, 15, 20, 50, 100

Number of estimators 50, 100, 200, 400, 600, 800, 1000, 1200

Learning rate 0.3, 0.1, 0.001, 0.0001

kNN

Number of neighbors 2, 4, 5, 6, 10, 15, 20, 30

Weights uniform or distance

Leaf size 10, 30, 50, 80, 100

Algorithm ball tree, kd tree or brute

Table 5: Model performance of the six different ML algorithms for estimating the Fatigue Assessment Score (FAS). d repre-

sents the number of time steps (days) of the input features that were used as input to the model to estimate the FAS-score.

DT RF XGB kNN FCNN LSTM

d MSE R

MSE R

1 141.7 -0.410 159.6 -0.588 127.2 -0.266 84.5 0.159 29.4 0.708 104.3 -0.038

2 136.9 -0.334 156.9 -0.529 123.2 -0.201 91.9 0.104 24.1 0.765 117.8 -0.149

3 150.9 -0.430 154.7 -0.467 125.3 -0.188 102.3 0.030 22.4 0.787 110.3 -0.046

4 194.3 -0.795 147.8 -0.364 121.2 -0.119 108.9 -0.005 21.4 0.803 126.5 -0.169

5 146.7 -0.304 138.6 -0.232 102.8 0.086 115.6 -0.027 20.8 0.815 130.4 -0.159

6 144.2 -0.207 137.7 -0.153 119.5 -0.001 116.7 0.023 25.3 0.788 205.9 -0.724

7 180.8 -0.362 151.6 -0.142 137.2 -0.033 146.0 -0.022 27.4 0.794 135.8 -0.023

Figure 4: Predicted FAS-scores plotted against the actual

values for the best model with an R

score of 0.815: FCNN

using the 5 last days of data as input. The grey line repre-

sents the ideal ﬁt.

5 RESULTS AND DISCUSSION

The results of our analysis are shown in Table 5,

where we compare the error metrics MSE and R

for

the six different ML algorithms. These scores were

produced using the test data set, which consisted of

30% of the complete training data. We ensured that

the test data set contained a similar distribution of

FAS-scores as the training set, meaning that we had

equal ratios of subjects from each of the three FAS

categories (see Table 2) in both sub-sets. Due to the

limited number of subjects, we were unable to keep

the age distribution similar while maintaining a simi-

lar distribution of FAS-scores. The value d in Table 5

represents how many time steps (days) from the input

data that were used as input to the models.

Table 6 shows the hyper-parameters used for each

of the models. The network architectures for FCNN

and LSTM were kept the same for all values of d,

due to the computational cost of running a hyper-

parameter search on neural networks. For the remain-

ing methods, we performed a hyper-parameter search

icSPORTS 2022 - 10th International Conference on Sport Sciences Research and Technology Support

Table 6: Conﬁguration of ML algorithms after hyper-parameter tuning, corresponding to the results shown for each model in

Table 5.

ML algorithm Hyper-parameter d = 1 d = 2 d = 3 d = 4 d = 5 d = 6 d = 7

Max depth 50 20 15 100 20 50 100

Minimum samples split 10 10 10 2 5 10 2

Minimum samples leaf 1 3 3 5 3 1 5

Max depth 2 2 10 10 100 100 10

Number of estimators 600 600 50 200 200 200 100

Minimum samples split 5 5 10 10 2 2 5

Minimum samples leaf 3 3 5 5 5 5 5

XGB

Max depth 50 15 15 15 15 5 20

Number of estimators 400 50 50 50 50 800 400

Learning rate 0.3 0.1 0.1 0.1 0.1 0.3 0.1

kNN

Number of neighbors 30

Weights distance

Leaf size 10

Algorithm kd tree

FCNN

Number of layers 1

Number of nodes in each layer 8

LSTM

Number of units 8

Dropout rate 0.2

for each of the d-values. The hyper-parameters cho-

sen for kNN were the same for any d-value.

The best performing model, made with a FCNN

with d = 5, is highlighted in bold typeface, with MSE

= 20.8 and R

= 0.815. The models created with

DT, RF, XGB, kNN and LSTM all had MSE > 100.

Only FCNN, XGB and kNN were able to produce at

least one model with positive R

-scores, meaning that

the estimations of the rest of the models are worse

than predicting the mean of the scores of the test set.

FCNN had R

> 0.7 for all models, with the best per-

formance using d = 5. This indicates that it is beneﬁ-

cial to have information for a period of multiple days

when using an FCNN to estimate a FAS-score. Sim-

ilar research on fatigue assessment using ML on sen-

sor data (Bai et al., 2020) showed using deep learning

(speciﬁcally a type of LSTM network) gave higher

performance than a traditional ML method (linear re-

gression). However, the differences in both input fea-

tures and fatigue assessment method compared to our

approach makes it challenging to compare these re-

sults directly.

The results from our study indicate that deep

learning can be used to create models for estimat-

ing fatigue, by using multivariate sensor data from

a wearable activity tracker. An intuitive measure of

the model performance is Mean Absolute Percentage

Error, which for our best model (created using FCNN

and d = 5) was 0.18. This means that the model had

an average error of 18% when estimating fatigue

on our test data set.

5.1 Limitations and Threats to Validity

We selected which features to use as input to the

model based on their linear correlation (Pearson Cor-

relation Coefﬁcient) to the target, but none of the

PCCs exceeded 0.4. Since all features showed a rela-

tively low correlation, it is not surprising that none of

the traditional (non-deep learning) ML methods was

able to produce models capable of fatigue estimation.

Even though the best FCNN model had an R

-score

of 0.815, one should be careful about drawing too

strong conclusions from these results, due to the lim-

ited number of participants in the study, and the fact

that four out of six ML algorithms did not achieve a

positive R

-score when evaluated on the test set. Fur-

thermore, the dataset is heavily gender-imbalanced,

with 89% of the subjects being female, which does

not guarantee that the models we have created will

have similar performance on male subjects.

While most of the input features we used

are generic, and can either be collected by most

commercially-available activity trackers or do not de-

pend on an activity tracker at all (e.g. age, gender,

weight, height, heart rate and calories burned), the

features related to active minutes and sleep are com-

puted using Fitbit’s proprietary closed-source algo-

rithms. This means that we are not aware of how these

features are calculated from the raw activity tracker

data, and whether that makes the models created with

these features speciﬁc to the data collected using Fit-

bit activity trackers. Furthermore, future adjustments

or changes to the algorithms by Fitbit might affect

Machine Learning for Fatigue Detection using Fitbit Fitness Trackers

the performance in unpredictable ways for a deployed

model.

6 CONCLUSIONS

Correctly and promptly diagnosing fatigue among re-

mote workers has a signiﬁcant social and healthcare

impact, as timely detection of health deterioration

leads to improved well-being and better medical treat-

ment. From a ﬁnancial perspective, this can also re-

duce potential costs incurred due to patient transporta-

tion, which is especially challenging in offshore mar-

itime conditions (e.g. vessel diversion or evacuation

by a helicopter). Furthermore, reducing such unfore-

seen transportation activities may also minimise the

pollution footprint caused by air and water emissions.

All of these factors emphasise the need for con-

tinuous real-time fatigue detection to pro-actively re-

act to and prevent potential accidents. To achieve this

goal, this paper described our approach to automated

fatigue detection using ML techniques and physiolog-

ical data collected by general-purpose ﬁtness track-

ers (as opposed to medical-grade stationary equip-

ment). The approach is based on the hypothesis that

human fatigue can be correlated with some common

biomarkers, such as sleep activity and heart rate, and

identifying these hidden patterns was done using sev-

eral ML algorithms, among which Fully-Connected

Neural Networks demonstrated best results. Using

this method, we were able to predict the FAS-score

with an average error of 18% when estimating fatigue

on the test data set.

Although the main target users of this approach

are remote in-ﬁeld workers, it is also relevant to var-

ious sports and recreational activities where people

have to spend long time in remote hostile environ-

ments under continuous physical or mental pressure,

with limited or no immediate access to healthcare ser-

vices.

As of today, we have tested the developed ap-

proach on a limited data set collected in lab conditions

in the context of a relatively small-scale data collec-

tion study. While the results are promising, the imme-

diate next step for further work will be to empirically

validate the approach with real-life users over a longer

period of time. It is expected that the provided feed-

back on the accuracy of the predictions as well as new

data will require further tuning of the models, which

is an established practice in ML engineering.

In this respect, a possible addition to this real-

life validation will be to implement the whole ap-

proach as an automated pipeline, where newly col-

lected biomarkers along with FAS questionnaires can

fuel the incremental re-training of the model in au-

tomated manner. Such implementation is possible

using Continual ML (or Life-long ML) techniques

(Liu, 2017). This will, however, require signiﬁcant

architecture design and implementation efforts for the

whole application stack and the data pipeline from

wearable sensors through smartphone gateways to

cloud platforms. As an alternative to such a ‘verti-

cal’ architecture (Dautov and Distefano, 2017; Dau-

tov et al., 2019), we will also explore the distributed

architecture for ML training (Dautov and Distefano,

2019) in the absence of a centralised cloud by apply-

ing Federated ML techniques – an emerging paradigm

for training ML models in a distributed manner on

several nodes using local data, and then merging these

individual elements into a global shared model (Yang

et al., 2019). While keeping the sensitive personal in-

formation locally (which is especially important for

healthcare-related scenarios), this will still yield a

fully-functional ML model.

ACKNOWLEDGEMENTS

The research leading to these results has been sup-

ported by a grant from Iceland, Liechtenstein and

Norway through the EEA Grants Greece 2014-2021,

in the frame of the “Business Innovation Greece”

programme. This work was also partly supported

by the InterQ project (958357) and the DAT4.ZERO

project (958363) funded by the European Commis-

sion within the Horizon 2020 research and innovation

programme.

REFERENCES

Alam, M. G. R., Abedin, S. F., Al Ameen, M., and Hong,

C. S. (2016). Web of objects based ambient assisted

living framework for emergency psychiatric state pre-

diction. Sensors, 16(9):1431.

American Psychiatric Association (2013). Diagnostic and

statistical manual of mental disorders: DSM-5. Amer-

ican Psychiatric Association, Washington, DC, 5 edi-

tion.

Bai, Y., Guan, Y., and Ng, W.-F. (2020). Fatigue assessment

using ecg and actigraphy sensors. In Proceedings of

the 2020 International Symposium on Wearable Com-

puters, pages 12–16.

Bergstra, J. and Bengio, Y. (2012). Random search for

hyper-parameter optimization. Journal of machine

learning research, 13(2).

Christ, G. (2016). Burnt Out: Stress on the Job [Info-

graphic]. https://www.ehstoday.com/health/article/

21917550/burnt-out-stress-on-the-job-infographic.

[Online; accessed 7-June-2022].

icSPORTS 2022 - 10th International Conference on Sport Sciences Research and Technology Support

Dautov, R. and Distefano, S. (2017). Three-level hierarchi-

cal data fusion through the IoT, edge, and cloud com-

puting. In Proceedings of the 1st International Con-

ference on Internet of Things and Machine Learning,

pages 1–5.

Dautov, R. and Distefano, S. (2019). Automating IoT data-

intensive application allocation in clustered edge com-

puting. IEEE Transactions on Knowledge and Data

Engineering, 33(1):55–69.

Dautov, R., Distefano, S., Bruneo, D., Longo, F., Mer-

lino, G., and Puliaﬁto, A. (2017). Pushing intelli-

gence to the edge with a stream processing architec-

ture. In 2017 IEEE International Conference on Inter-

net of Things (iThings) and IEEE Green Computing

and Communications (GreenCom) and IEEE Cyber,

Physical and Social Computing (CPSCom) and IEEE

Smart Data (SmartData), pages 792–799. IEEE.

Dautov, R., Distefano, S., and Buyya, R. (2019). Hierarchi-

cal data fusion for Smart Healthcare. Journal of Big

Data, 6(1):1–23.

De Vries, J., Michielsen, H. J., and Van Heck, G. L. (2003).

Assessment of fatigue among working people: a com-

parison of six questionnaires. Occupational and envi-

ronmental medicine, 60(suppl 1):i10–i15.

Gander, P., Hartley, L., Powell, D., Cabon, P., Hitchcock,

E., Mills, A., and Popkin, S. (2011). Fatigue risk man-

agement: Organizational factors at the regulatory and

industry/company level. Accident Analysis & Preven-

tion, 43(2):573–590.

Ganster, D. C. and Rosen, C. C. (2013). Work stress and

employee health: A multidisciplinary review. Journal

of management, 39(5):1085–1122.

Gordon, R. P. (1998). The contribution of human factors

to accidents in the offshore oil industry. Reliability

Engineering & System Safety, 61(1-2):95–108.

Hallman, D. M., Gupta, N., Mathiassen, S. E., and Holter-

mann, A. (2015). Association between objectively

measured sitting time and neck–shoulder pain among

blue-collar workers. International Archives of Oc-

cupational and Environmental Health, 88(8):1031–

1042.

Hamy, V., Garcia-Gancedo, L., Pollard, A., Myatt, A.,

Liu, J., Howland, A., Beineke, P., Quattrocchi, E.,

Williams, R., and Crouthamel, M. (2020). Develop-

ing smartphone-based objective assessments of phys-

ical function in rheumatoid arthritis patients: the PA-

RADE study. Digital biomarkers, 4(1):26–44.

Hellesøy, O. H. (1985). Work Environment, Statfjord Field:

Work Environment, Health, and Safety on a North Sea

Oil Platform. Universitetsforlaget.

Hochreiter, S. and Schmidhuber, J. (1997). Long short-term

memory. Neural computation, 9(8):1735–1780.

Hockey, R. (2013). The psychology of fatigue: Work, effort

and control. Cambridge University Press.

Hofman, M., Ryan, J. L., Figueroa-Moseley, C. D., Jean-

Pierre, P., and Morrow, G. R. (2007). Cancer-related

fatigue: the scale of the problem. The oncologist,

12(S1):4–10.

Husom, E. J. B. G., Bernab

e, P., and Sen, S. (2022). Deep

learning to predict power output from respiratory in-

ductive plethysmography data. Applied AI Letters,

3(2):e65.

Jacobson, N. C., Summers, B., and Wilhelm, S. (2020).

Digital biomarkers of social anxiety severity: digital

phenotyping using passive smartphone sensors. Jour-

nal of medical Internet research, 22(5):e16875.

Kaewkannate, K. and Kim, S. (2016). A comparison of

wearable ﬁtness devices. BMC public health, 16(1):1–

16.

Karuppusamy, N. S. and Kang, B.-Y. (2020). Multi-

modal System to Detect Driver Fatigue Using EEG,

Gyroscope, and Image Processing. IEEE Access,

8:129645–129667.

Kim, D. H., Nam, K. H., Choi, B. K., Han, I. H., Jeon,

T. J., and Park, S. Y. (2019). The Usefulness of a

Wearable Device in Daily Physical Activity Monitor-

ing for the Hospitalized Patients Undergoing Lumbar

Surgery. Journal of Korean Neurosurgical Society,

62(5):561.

Kudo, Y., Arai, M., and Miki, N. (2017). Fatigue assess-

ment by electroencephalogram measured with candle-

like dry microneedle electrodes. Micro & Nano Let-

ters, 12(8):545–549.

Lavidor, M., Weller, A., and Babkoff, H. (2003). How sleep

is related to fatigue. British journal of health psychol-

ogy, 8(1):95–105.

Lee, K. A., Hicks, G., and Nino-Murcia, G. (1991). Validity

and reliability of a scale to assess fatigue. Psychiatry

research, 36(3):291–298.

Leroux, A., Rzasa-Lynn, R., Crainiceanu, C., and Sharma,

T. (2021). Wearable devices: current status and oppor-

tunities in pain assessment and management. Digital

Biomarkers, 5(1):89–102.

Liu, B. (2017). Lifelong machine learning: a paradigm for

continuous learning. Frontiers of Computer Science,

11(3):359–361.

Low, C. A., Li, M., Vega, J., Durica, K. C., Ferreira, D.,

Tam, V., Hogg, M., Zeh III, H., Doryab, A., and Dey,

A. K. (2021). Digital biomarkers of symptom bur-

den self-reported by perioperative patients undergo-

ing pancreatic surgery: prospective longitudinal study.

JMIR cancer, 7(2).

Lu, H., Frauendorfer, D., Rabbi, M., Mast, M. S., Chit-

taranjan, G. T., Campbell, A. T., Gatica-Perez, D., and

Choudhury, T. (2012). StressSense: Detecting stress

in unconstrained acoustic environments using smart-

phones. In Proceedings of the 2012 ACM conference

on ubiquitous computing, pages 351–360.

Luo, H., Lee, P.-A., Clay, I., Jaggi, M., and De Luca, V.

(2020). Assessment of fatigue using wearable sensors:

a pilot study. Digital biomarkers, 4(1):59–72.

Maman, Z. S., Yazdi, M. A. A., Cavuoto, L. A., and Mega-

hed, F. M. (2017). A data-driven approach to model-

ing physical fatigue in the workplace using wearable

sensors. Applied ergonomics, 65:515–529.

Marino, F. E. (2019). Human Fatigue: Evolution, Health

and Performance. Routledge.

Matuz, A., van der Linden, D., Zsolt, K., Hernadi, I.,

Kazmer, K., and Csatho, A. (2021). Enhanced cardiac

vagal tone in mental fatigue: Analysis of heart rate

Machine Learning for Fatigue Detection using Fitbit Fitness Trackers

variability in Time-on-Task, recovery, and reactivity.

Plos one, 16(3):e0238670.

Merriwether, E. N., Frey-Law, L. A., Rakel, B. A., Zim-

merman, M. B., Dailey, D. L., Vance, C. G., Golchha,

M., Geasland, K. M., Chimenti, R., Crofford, L. J.,

et al. (2018). Physical activity is related to function

and fatigue but not pain in women with ﬁbromyal-

gia: baseline analyses from the Fibromyalgia Activ-

ity Study with TENS (FAST). Arthritis research &

therapy, 20(1):1–13.

Michielsen, H. J., De Vries, J., and Van Heck, G. L. (2003).

Psychometric qualities of a brief self-rated fatigue

measure: The fatigue assessment scale. Journal of

psychosomatic research, 54(4):345–352.

Mielke, C., Antons, R., and Haux, R. (2020). Detection

of psychomotor agitation pattern from motion sensor

data in a living environment of a patient with demen-

tia. In Digital Personalized Health and Medicine,

pages 746–750. IOS Press.

Milosevic, S. (1997). Drivers’ fatigue studies. Ergonomics,

40(3):381–389.

Patel, M., Lal, S. K., Kavanagh, D., and Rossiter, P. (2011).

Applying neural network analysis on heart rate vari-

ability data to assess driver fatigue. Expert systems

with Applications, 38(6):7235–7242.

Pearson, K. (1896). VII. Mathematical contributions to the

theory of evolution. – III. Regression, heredity, and

panmixia. Philosophical Transactions of the Royal

Society of London. Series A, containing papers of a

mathematical or physical character, (187):253–318.

Perraudin, C. G., Illiano, V. P., Calvo, F., O’Hare, E., Don-

nelly, S. C., Mullan, R. H., Sander, O., Caulﬁeld, B.,

and Dorn, J. F. (2018). Observational Study of a Wear-

able Sensor and Smartphone Application Supporting

Unsupervised Exercises to Assess Pain and Stiffness.

Digital Biomarkers, 2(3):106–125.

Petersen, J., Austin, D., Mattek, N., and Kaye, J. (2015).

Time out-of-home and cognitive, physical, and emo-

tional wellbeing of older adults: a longitudinal mixed

effects model. PloS one, 10(10):e0139643.

S¸ahinkaya, B. and Oktal, H. (2021). Modeling of Aircrew

Rostering Problem with Fatigue Risk Management

Approach. The International Journal of Aerospace

Psychology, 31(3):269–282.

Sen, S., Bernab

e, P., and Husom, E. J. B. (2021). Deepventi-

lation: learning to predict physical effort from breath-

ing. In Proceedings of the Twenty-Ninth International

Conference on International Joint Conferences on Ar-

tiﬁcial Intelligence, pages 5231–5233.

Sheikh, M., Qassem, M., and Kyriacou, P. A. (2021). Wear-

able, Environmental, and Smartphone-Based Passive

Sensing for Mental Health Monitoring. Frontiers in

Digital Health, page 33.

Srivastava, D. K. and Tiwari, P. K. (2021). Prevention of

road accidents due to drowsiness detection using PER-

CLOS algorithm. Journal of discrete mathematical

sciences and cryptography, 24(5):1429–1437.

Stern, J. M. (2005). Atlas of EEG patterns. Lippincott

Williams & Wilkins.

Vega, J., Li, M., Aguillera, K., Goel, N., Joshi, E., Durica,

K., Kunta, A., and Low, C. (2020). RAPIDS: Repro-

ducible Analysis Pipeline for Data Streams Collected

with Mobile Devices. Journal of Medical Internet Re-

search.

Villa, M. G., Palsson, T. S., Royo, A. C., Bjarkam, C. R.,

and Boudreau, S. A. (2020). Digital pain mapping

and tracking in patients with chronic pain: Longitu-

dinal study. Journal of Medical Internet Research,

22(10):e21475.

Virk, J. S., Singh, M., Panjwani, U., and Ray, K. (2022). An

intelligent framework for detection of fatigue induced

by sleep-deprivation. Journal of Intelligent & Fuzzy

Systems, 42(2):1223–1233.

Wang, R., Wang, W., DaSilva, A., Huckins, J. F., Kelley,

W. M., Heatherton, T. F., and Campbell, A. T. (2018).

Tracking depression dynamics in college students us-

ing mobile phone and wearable sensing. Proceed-

ings of the ACM on Interactive, Mobile, Wearable and

Ubiquitous Technologies, 2(1):1–26.

Wang, T., Azad, T., and Rajan, R. (2016). The

emerging inﬂuence of digital biomarkers on health-

care. https://rockhealth.com/insights/the-emerging-

inﬂuence-of-digital-biomarkers-on-healthcare/. [On-

line; accessed 7-June-2022].

Watters, E. (2020). From Fitbit to Mindﬁt: The Stress Man-

agement Score Helps People Recognize How Their

Body Reacts to Stress. https://blog.ﬁtbit.com/ﬁtbit-

stress-management-approach. [Online; accessed 7-

June-2022].

Wilbur, R. E., Grifﬁn, J. S., Sorensen, M., and Furberg,

R. D. (2018). Establishing digital biomarkers for oc-

cupational health assessment in commercial salmon

ﬁshermen: protocol for a mixed-methods study. JMIR

Research Protocols, 7(12):e10215.

Yang, Q., Liu, Y., Cheng, Y., Kang, Y., Chen, T., and Yu, H.

(2019). Federated learning. Synthesis Lectures on Ar-

tiﬁcial Intelligence and Machine Learning, 13(3):1–

207.

Zhang, J., Chen, Z., Liu, W., Ding, P., and Wu, Q. (2021).

A Field Study of Work Type Inﬂuence on Air Traf-

ﬁc Controllers’ Fatigue Based on Data-Driven PERC-

LOS Detection. International journal of environmen-

tal research and public health, 18(22):11937.

icSPORTS 2022 - 10th International Conference on Sport Sciences Research and Technology Support