Really Can’t Hold On Anymore? Physiological Indicators Versus

Self-Reported Motivation Drop During Jogging

Shiyao Zhang

1 a

, Sergei Kolensnikov

, Till Rennspieß

, Robert Porzel

2 b

, Tanja Schultz

1 c

and Hui Liu

1 d

Cognitive System Lab, University of Bremen, Bibliothekstraße 1, Bremen, Germany

Digital Media Lab, University of Bremen, Bibliothekstraße 1, Bremen, Germany

Keywords:

Motivation, Self-Determination, SDT, Biosignals, ECG, sEMG, Respiration Rate, LSTM, HRV Analysis,

Causal Relationship.

Abstract:

Motivational dynamics in jogging constitute a pivotal factor inﬂuencing a runner’s performance, persistence,

and overall engagement in the running activity. The manifestation of diminished motivation is concomi-

tant with a cascade of physiological responses, capable of being represented through biological signals, for

which biosignal monitoring, a common practice in evaluating athletic performance, emerges as a valuable tool.

Should biosignals, as dynamic indicators during exercise, exhibit discernible shifts correlating with changes

in motivation, the prospect of actively modulating motivation levels and intervening in athletes’ performance

during exercise becomes feasible. This study consists of collecting comprehensive biological data, including

electrocardiogram (ECG), surface electromyogram (sEMG), and respiration signals (RSP), from runners who

participated in a 20-minute running session. Participants were asked to self-report a decrease in motivation

during jogging. Using heart rate variability analysis, self-similarity matrix and deep learning methodolo-

gies, this work seeks to explore whether the discomforts reported and triggered by decreased motivation had

discernible effects on monitored physiological signals, thus advancing our understanding of the nuanced rela-

tionship between physiological responses and motivational states in running.

1 INTRODUCTION

Etymologically, the word motivation is derived from

the Latin word movere, which denotes the driving

force behind an individual’s actions (Ryan et al.,

2010).

Given the pivotal role of motivation as a behav-

ioral driver, researchers have aimed to comprehend

how motivational processes manifest in people’s par-

ticipation and performance in sports, consequently

seeking avenues for intervention in sports perfor-

mance. Motivation has been proposed and conﬁrmed

to contribute signiﬁcantly to student achievement in

physical education (PE) and participation in sports

(Melliti et al., 2016). As dropout from sport is often

attributed to a lack of motivation and self-regulation

skills, it is essential to understand the underlying mo-

tivational processes (Weiss and Williams, 2004).

As examples, incorporating kinetic music in the

https://orcid.org/0000-0001-5965-0428

https://orcid.org/0000-0002-7686-2921

https://orcid.org/0000-0002-9809-7028

https://orcid.org/0000-0002-6850-9570

gym and providing continuous verbal encouragement

from the coach to the endurance athlete during exer-

cise are effective strategies for inﬂuencing individu-

als to engage in and sustain physical activity. These

methods enhance motivation levels, particularly in the

context of endurance sports, where they are frequently

employed. Endurance sports are characterized by re-

peated isotonic contractions of large skeletal muscle

groups. Classical examples include running, swim-

ming, and cycling among summer sports, and cross-

country skiing or speed skating among winter sports

(Morici et al., 2016). Motivation, as a driving force, is

highly manipulative of the athlete, performance, and

outcome of the competition during endurance sports.

Athletes must repeatedly override this inherent mo-

tivational response, which refers to the instinctive or

natural inclination to avoid discomfort, fatigue, or the

negative effects associated with resisting temptation,

to persevere and succeed in endurance sports(Taylor

et al., 2018).

Optimizing endurance performance involves ad-

dressing motivational interventions and their sus-

tained maintenance. Individuals experiencing a de-

cline in motivation during exercise often express

Zhang, S., Kolensnikov, S., Rennspieß, T., Porzel, R., Schultz, T. and Liu, H.

Really Can’t Hold On Anymore? Physiological Indicators Versus Self-Reported Motivation Drop During Jogging.

DOI: 10.5220/0012577300003657

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 17th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2024) - Volume 1, pages 821-831

ISBN: 978-989-758-688-0; ISSN: 2184-4305

821

physiological discomfort, prompting the need to dis-

cern whether it stems from an actual physiological

change or acts as a trigger for perceived discomfort

leading to thoughts of quitting. Understanding this is

crucial, as persisting in exercise despite genuine phys-

iological discomfort may exacerbate issues, while ef-

fective management of motivation becomes essential

for maintaining or enhancing the exercise state.

This study delves into the impact of altered phys-

iological states, speciﬁcally discomfort reported and

triggered by decreased motivation, on monitored

physiological signals. A hypothesis was formulated,

suggesting that the discomfort individuals express

when their exercise motivation drops is reﬂected by

abnormalities in physiological signals. The inquiry

seeks to determine whether the physiological discom-

fort resulting from decreased motivation inﬂuences

exercisers’ health status, thus enhancing the preci-

sion of motivational interventions. The ﬁndings aim

to shed light on the intricate relationship between

motivation-induced discomfort and physiological sig-

nals, contributing to a deeper understanding of the

dynamics at play during exercise. Electrical biolog-

ical signals (ECG, sEMG, RSP) are utilized in this

study obtained from wearable devices, ensuring min-

imal disruption to exercisers and paving the way for

innovative integration of motivation-related functions

into smart exercise devices.

2 RELATED WORK

Considerable progress has been made in investigat-

ing how motivation affects sports performance, ath-

lete behavior, and competitiveness in competitions.

Individual sports are motivated to sport because of in-

ternal factors (such as enjoyment or skill development

and mastery) and external factors (such as rewards,

health, and appearance) (Moradi et al., 2020). Those

factors are full of uncertainty and variability. As a

personalized drive, motivation is highly individual-

ized and the interaction with external environmental

factors is complex. Most researchers agree on the ex-

istence of individual differences in motivational pref-

erences or traits (Kanfer and Ackerman, 2000).

Despite individual differences, the importance of

maintaining or boosting motivation during exercise

is crucial. The incorporation of physiological signal

tracking in wearable devices has become a founda-

tional aspect of exercise, enhancing the relevance of

apps or products aimed at improving motivation for

users. The researcher tries to realize the function of

boosting motivation by using music when the mood

is low by making mood judgment based on physio-

logical signal EDA in wearable devices (Baldassarri

et al., 2023). (Bauer and Kratschmar, 2015) presents

the application requirements needed to increase run-

ners’ motivation and control training, based on heart

rate monitoring and using the music for regulating the

runners pace. While mental state identiﬁcation has

progressed personalized motivation enhancement, the

signiﬁcance of physiological states during low moti-

vation levels is underexplored. Physiological discom-

fort, emphasized when individuals halt exercise due

to decreased motivation, underscores the need to in-

tegrate the exerciser’s physiological state in person-

alized motivational enhancement systems. The ab-

normality of reported physiological discomfort dur-

ing low motivation levels in terms of physiological

signaling remains uncertain, warranting analysis and

tracking of physiological signals for insights into the

state during motivation drops.

The selection of representative biosignals allows

individually monitoring of physiological changes pro-

duced in organ systems during endurance exercise.

The cardiovascular and musculoskeletal systems are

the two main organ systems affected during aerobic

exercise. Electrocardiography (ECG) is a tool that can

be used to study electrical abnormalities in patients

with cardiac disease (Krittayaphong et al., 2019).

The surface Electromyographic (sEMG) signal is

a biomedical signal that measures the electric currents

generated in muscles during their contraction that rep-

resent neuromuscular activities. EMG signals are one

of the most commonly used data for studying human

activities and behaviors (Liu and Schultz, 2022; Hart-

mann et al., 2022; Cai et al., 2023; Liu et al., 2023;

Hartmann et al., 2023; Cai et al., 2024). The nervous

system always controls muscle activity (contraction /

relaxation) (Reaz et al., 2006).

Furthermore, the researchers also proposed that

changes in motivation may also be reﬂected in

changes in reported respiration rate (Martin et al.,

2018). Currently, physiological signals are widely

used in the ﬁeld of sports psychology. Multimodal

information and multichannel physiological signals

to measure emotional responses offer more informa-

tion for emotion recognition. Possible physiological

signals include ECG, electromyogram (EMG), elec-

trooculogram (EOG), electroencephalogram (EEG),

skin conductance response (SCR), galvanic skin re-

sponse (GSR), pulseoximetry, skin temperature, an-

terial blood pressure (ABP), blood volume pulse

(BVP), and electrodermal activity (EDA), among oth-

ers (Wu et al., 2015; Shi et al., 2023).

The relationship between various types of physio-

logical signals (e.g.,ECG, sEMG, and RSP) and mo-

tivation levels has not been explored, which becomes

BIOSIGNALS 2024 - 17th International Conference on Bio-inspired Systems and Signal Processing

822

this study’s research topic, focusing on whether phys-

iological signals are associated with decreased self-

reported motivation.

3 DATA

Acknowledging the gap in dedicated databases for

investigating motivational dimensions in jogging, a

methodologically rigorous experiment has been de-

signed. The primary objective is to induce a notice-

able reduction in motivation during physical exertion,

simultaneously capturing biometric signals from par-

ticipating individuals. This systematic paradigm is

crafted to build a comprehensive database, enabling

an in-depth exploration of the interrelationship be-

tween motivational states and physiological signals

within the jogging context.

3.1 Experiment Design

3.1.1 Selections of Biosignals

Aerobic training requires the perfect matching of the

respiratory and cardiovascular systems, in order to

provide the muscles with the necessary supply of en-

ergy to be transformed into mechanical work (Beh,

1990). The state of the respiratory, cardiovascu-

lar, and skeletal muscle systems during jogging is

closely related to the current performance of the ath-

lete. Changes in these three physiological systems are

signiﬁcant when exercisers are exercising, attempt-

ing to quit, or after quitting exercise. Based on my

own exercise experience, observation and communi-

cation with jogging athletes, shortness of breath, el-

evated heart rate, and muscle fatigue are commonly

cited physiological discomforts during exercise. It’s

also proved by this study’s post-questionnaire, which

is presented in section 5. The selection of biological

signals representative of all three physiological sys-

tems is undertaken to characterize the present state

of the system. These signals include ECG, RSP, and

sEMG.

3.1.2 Self-Reported Motivation Drops

The waning of motivation, serving as a behavioral

driver, signiﬁcantly inﬂuences an individual’s deter-

mination to persist in exercise. Consequently, we de-

ﬁne a drop in motivation as the inclination not to con-

tinue with the exercise, which may arise from factors

such as shortness of breathing, rapid heartbeat, anx-

iety, fatigue, among others. In the experiment, the

exercisers’ diminished motivation implied their reluc-

tance to continue exercising.

To capture instances of motivation drop without

disrupting the jogging process, each participant held a

small rubber duck smaller than the palm of their hand.

When an exerciser experienced a deﬁned drop in mo-

tivation, they were required to squeeze the rubber

duck, thereby completing a self-report of the dimin-

ished motivation. Throughout the designated exercise

period (20 minutes), participants were permitted to

squeeze the little yellow duck an unlimited number of

times, yet were obligated to persist until the comple-

tion of the predeﬁned exercise duration. The purpose

of this design is that it is desired to collect as much

physiological data about motivation drops as possible

from participants.

3.1.3 Experiment Procedure

The study conducted a jogging experiment, capturing

ECG, RSP, and sEMG signals, selected for their rel-

evance to an exerciser’s ongoing performance. Par-

ticipants jogged on a treadmill under speciﬁed condi-

tions, completing pre- and post-questionnaires cover-

ing personal details, motivation levels, emotional re-

sponses, and physiological sensations.

The running duration of 20 minutes was deter-

mined to allow runners of diverse backgrounds to

experience at least one power drop at an appro-

priate speed. The treadmill settings included 11

evenly divided subsections, with each mode further

divided into 3 phases: 2 warm-up segments at 6 km/h

for approximately 3.6 minutes and 8 constant-speed

running segments lasting around 15 minutes. Two

modes, moderate (7 km/h) and high (8 km/h), along

with a relaxation mode (5 km/h for about 1.5 min-

utes), were designated. These settings applied to male

participants, and adjustments were made for female

participants by reducing speed by 0.5 km/h in each

phase. Additional details are provided in Table 1.

Running speed varied according to individual run-

ner backgrounds, and the trials were conducted in-

doors on a treadmill to control environmental vari-

ables. Recognizing motivation’s sensitivity to envi-

ronmental factors, the experiment implemented mea-

sures to minimize external stimuli, such as covering

the treadmill screen to create a silent environment.

Subjects refrained from wearing headphones to elim-

inate temporal cues during exercise, and communica-

tion between subjects and experimenters was limited,

intentionally cultivating a ”boring and monotonous”

experimental setting.

The goal was to induce as many motivation drops

as possible, ensuring a balanced database. Two tread-

mill modes were conﬁgured: the ”high” mode, de-

signed to induce motivation drops even for habitual

exercisers (comprising over 60% of subjects exercis-

Really Can’t Hold On Anymore? Physiological Indicators Versus Self-Reported Motivation Drop During Jogging

823

ing 3 hours or more per week), and the ”moderate”

mode, more suitable for non-habitual exercisers.

Before the experiment, participants were briefed

on the deﬁnition of ”motivation drops,” and instances

of these feelings were self-reported by squeezing a

rubber duck. The entire session was audio-recorded,

and debrieﬁng timings were manually documented.

The rubber duck, chosen for its ergonomic size,

ensured minimal additional exertion and avoided gen-

erating abnormal physiological signals during report-

ing. All participants provided informed consent,

signed a data protection agreement, and agreed to

audio recording during the experimental sessions.

To ensure safety, participants were required to pro-

vide information about their health status in the pre-

questionnaire, including their medical history, and

were informed they could voluntarily halt the exper-

iment if they experienced any abnormalities intolera-

ble to their body.

Table 1: Two mode of running.

Mode 1 Mode 2

Setting

Pace

(km/h)

Incline

(

◦

)

Pace

(km/h)

Incline

(

◦

)

Warm-Up 6 6 5.5 5

Moderate 7 7 6.5 6

High 8 8 7.5 7

Cooldown 5 5 4.5 5

3.2 Data Acquisition

In this study, 11 subjects, free from known car-

diac diseases, participated in data collection activi-

ties, comprising six females and ﬁve males aged 22

to 37, with weights ranging from 51 kg to 100 kg.

Eight subjects engaged in regular endurance exercise,

while two did not have a ﬁxed exercise routine. The

participation time for each subject was approximately

35 minutes, covering various activities. All subjects

provided written informed consent for data storage

and subsequent studies, with the dataset shared in

anonymized form.

The study employed ECG and sEMG sensors

from PLUX’s Cardio BAN and Muscle BAN, along

with a wired RSP sensor connected to PLUX’s

Hub, using OpenSignals software for data collection.

These sensors facilitated signal acquisition during ex-

ercise, and the collected data were pseudonymized to

ensure participant anonymity.

3.3 Dataset

The dataset, adhering to the h5-format, was systemat-

ically recorded for 11 subjects, encompassing a total

duration of 211 minutes and 36 seconds (equivalent

to 3 hours, 31 minutes, 36 seconds) of data.

One subject’s data did not include a self-reported

motivation drop; however, for the efﬁcient allocation

of academic resources, it was retained in the dataset.

In the following Table 2, 11 subjects reported a to-

tal of 68 motivation drops. Male subjects reported 41

motivation drops, while female subjects reported 24

motivation drops. On average, each male subject re-

ported 8.2 times motivation drops, while the average

number of motivation drops reported by female sub-

jects was only half of that reported by male subjects.

Table 2: Dataset statistical results.

Male Female

Numbers 5 6

Amounts of MDs 41 24

Averaged number of MDs 8.2±4.83 4±4.43

Notably, one participant exhibited a sustained mo-

tivational state throughout the entirety of the experi-

ment. Among instances wherein motivation drops oc-

curred, the temporal spectrum for reaching the ﬁrst

episode varied, with the swiftest occurrence transpir-

ing at 86 seconds and the most protracted interval ex-

tending to 1061 seconds (17 minutes and 41 seconds)

1. The manifestation of diminished motivation ap-

pears to be subject to individual variability.

Figure 1: The time it takes for the ﬁrst motivational drop to

occur among different subjects.

4 METHODOLOGY

In this section, the methodologies employed for the

analysis and detection of occurrences of diminished

motivation are delineated, encompassing HRV anal-

ysis, and a deep learning model, speciﬁcally Long

Short-Term Memory (LSTM). An overview of anal-

ysis framework used in this study are shown in the

ﬁgure 2.

BIOSIGNALS 2024 - 17th International Conference on Bio-inspired Systems and Signal Processing

824

Figure 2: The framework for investigating relationship be-

tween self-reported Motivation drops and biosignals.

4.1 HRV Analysis

HRV consists of great value in sports due to the syn-

ergy between the heart and numerous systems. Heart

rate variability is often mentioned by exercisers af-

ter exercise, and this feedback is also reﬂected in the

post-questionnaire. HRV may be recognized as the re-

sponse of the heart towards any kind of stimuli so that

it compensates the situations accordingly, and thus,

its variation may be used as warning signs of cardiac

diseases (ChuDuc et al., 2013). If a person quits ex-

ercising because of decreased motivation, he or she

often complains of cardiac distress, such as tachycar-

dia, chest tightness, etc.

HRV is one of the means to ﬁnd out the state of

the automatic nerve system (ANS). The variation be-

tween heartbeat is low in sympathetic activation and

high in parasympathetic mode. It has been observed

that low HRV indicates cardiovascular diseases such

as hypertension, whereas high HRV shows higher car-

diac ﬁtness (Tiwari et al., 2021). Abnormalities in

HRV analysis can be used as a basis for abnormal

physiologic signals.

4.2 Self-Similarity Matrix Anlysis

The purpose of the Self-Similarity Matrix (SSM) is

to compare each sample of the signals with all the

other samples. To calculate the SSM, the dot prod-

uct between the transposed Feature Matrix (FM) and

itself is performed. Hence, each column of the ma-

trix is compared with every other column, yielding

a similarity score (Santos et al., 2021). Columns

sharing common feature values exhibit higher similar-

ity scores, while those with diverging feature values

manifest lower similarity scores (Paulus et al., 2010)

(Bello et al., 2018). Each column of the matrix signi-

ﬁes the characterization of each segment of the signal

selected during the sliding window process.

The value of the principal diagonal represents the

highest similarity value. Block structures signify ho-

mogeneous regions in the signal, and when a block

structure transitions to a different one along the di-

agonal, it indicates a change in the signal’s behav-

ior. The presence of additional diagonals in the ma-

trix, aside from the primary diagonal, suggests that

the columns and rows divided by the secondary diag-

onal share similar properties.

It’s worth noting that, a widely used biomedical

signal, ECG, has testiﬁed to the feasibility of SSM

(Rodrigues et al., 2022). This analysis enables the

scrutiny of the correlation between decreased motiva-

tion for autonomous reporting and abnormal physio-

logical signals. Detailed results of the self-similarity

matrix analysis are elucidated in the Results section.

4.3 LSTM Model

The choice of LSTM was inspired by research ”Ro-

bust ECG R-peak detection using LSTM”. This study

discusses the use of LSTM models to learn long-time

dependencies in temporal signal ECGs and to iden-

tify R-peaks(Laitala et al., 2020). LSTM networks

are known for their ability to capture long tempo-

ral dependencies, to learn complex pattern and their

robustness to irregularity. LSTM has the process-

ing power before or without feature extraction (Wang

et al., 2024). The data employed in this study consist

entirely of time-series signals, with motivation levels

characterized by changes over time. In the case of

complex and elusive signal patterns, the LSTM model

excels in learning the structural features of intricate

signals through supervised learning on pre-labeled

segments.

4.3.1 Annotation Based on Self-Reported

Using a sampling frequency of 1000Hz, the moment

of rubber duck squeezing as the central point was con-

sidered. Subsequently, we marked 500 adjacent sam-

pling points to this moment as 1, while the remaining

points were labeled as 0. The conceptualization of

motivation drop occurrence extends beyond a singu-

lar moment, viewing it as a process with a speciﬁc

duration. It’s also important to note that the act of

squeezing the rubber duck lags behind the moment of

perceiving the motivation drop, and the drop does not

immediately dissipate upon squeezing.

4.3.2 Time Series Data Splitting and Cross

Validation

The training approach adapts to individual motiva-

tional proﬁles, employing an individual-dependent

model due to motivational variations. A sliding win-

dow approach is employed for homogeneous cross-

Really Can’t Hold On Anymore? Physiological Indicators Versus Self-Reported Motivation Drop During Jogging

825

Figure 3: Data proportion (Motivation Drops (MDs)).

validation, initially dividing each subject’s data into

80% training, validation, and 20% test sets. Sub-

sequently, 80% of the training set undergoes a four-

segment division, with the initial 80% forming train-

ing sets and the remaining 20% constituting valida-

tion sets, resulting in a 4-fold cross-veriﬁcation.

4.4 Motivation Drop Prediction: LTSM

Model

The bidirectional LSTM layer and a dense layer of

the constructed sequence model. Each layer has 64

units, and the ﬁnal dense layer contains only one out-

put unit. The hyperbolic tangent is used as the activa-

tion function of all other layers, except that the ﬁnal

output layer uses sigmoid as the activation function.

This model, which has proven effective in the study

of R-peak recognition, serves as a reference for train-

ing in this study (Laitala et al., 2020) aiming to derive

initial conclusions using a relatively compact network

structure. The model was not optimized in this phase,

as the primary objective of this study was to iden-

tify the presence of physiological abnormalities. The

input sequence is three channels, respectively ECG,

RSP and sEMG. The data length of each sequence is

4000, and the overlap between each two sequences is

50%.

5 RESULTS AND DISCUSSION

5.1 Questionnaire

As evident from the post-questionnaire statistics in

ﬁgure 4, the three most frequently reported physiolog-

ical discomforts during motivational drops are short-

ness of breath, rapid heartbeat, and profuse sweat-

ing. Among the 11 subjects, 6 mentioned experi-

encing shortness of breath, 8 reported a rapid heart-

beat, and 9 noted an increase in sweating during self-

reported motivation drops. This implies that the cho-

sen ECG and respiratory signals for the experiment

are apt for describing the physiological discomfort as-

sociated with declining motivation.

However, it’s worth noting that increased sweat-

ing, mentioned by the majority of subjects, was not

captured by relevant physiological signals such as

Electrodermal Activity (EDA), which can measure

skin changes. EDA is a common feature integrated

into many healthcare monitoring devices, including

smartwatches.

Additionally, only one person reported muscle

soreness and fatigue during the autonomous debrief-

ing of motivation drops. Interestingly, vertigo and

inattention were more frequently mentioned. This

provides a rationale for subsequent analysis focusing

on neurologically related indicators.

Figure 4: Results from post-questionnaire about how sub-

jects felt when motivation drops happened.

5.2 RR-Interval, Instantaneous Heart

Rate (IHR) and LF/HF Ratio

5.2.1 RR-Interval

Heart rate variability (HRV) is one of the health in-

dicators used worldwide (Litscher et al., 2014). It

is important to assess the variation in heart rate for

evaluating cardiac conditions by studying the ﬂuctu-

ation in RR intervals (Tiwari et al., 2021). RR in-

tervals (R-wave peak to R-wave peak in electrocar-

diograms, RRI) represent the measurements of the

sinus heart period in chronological or heartbeat or-

der (Electrophysiology, 1996). RR-Interval and IHR

were generated in real-time by Heart Rate Variabil-

ity (HRV) Add-on while acquisition

. The ﬂuctua-

tion interval of RR-Interval is depicted in the ﬁgure 5

through box plots, wherein the RR-Interval at the mo-

ment (s) of motivational drops is highlighted as a red

dot. The results illustrate variations in the ﬂuctuation

range and magnitude of RR-Interval among differ-

ent subjects, highlighting the individual independence

https://www.pluxbiosignals.com/products/heart-rate-

variability-hrv-add-on (accessed 13.Jan.2024)

BIOSIGNALS 2024 - 17th International Conference on Bio-inspired Systems and Signal Processing

826

of RR-interval data. Among the RR-intervals corre-

sponding to moments of motivational drops, only one

fell within the range of outliers, while the data for

the remaining self-reported motivation drop instances

were distributed between the maximum and minimum

values. This suggests that RR-interval did not exhibit

observable anomalies when individuals became aware

of motivational drops.

Figure 5: Box plots of the range of variation in subjects’

RR-interval, and the RR-interval corresponding to the onset

of motivational drops.

5.2.2 Instantaneous Heart Rate (IHR)

HRV helps not only in identifying the risk of cardiac

diseases but also in the state of ANS (Tiwari et al.,

2021). Instantaneous heart rate provides a direct mea-

sure of vagal nerve and sympathetic nervous system

activity and is of substantial use in a number of an-

alyzes and applications (Jarchi and Casson, 2017).

Initially, the box plot in ﬁgure 6 illustrates the time-

domain distribution of IHR for each subject, provid-

ing insight into the range of IHR distribution. Con-

currently, the IHR at the onset of motivational drops

is denoted by a red dot in the plot. Similar to the RR-

Interval distribution plot, the distribution interval var-

ied for each subject, underscoring the individual inde-

pendence of the data. Notably, the IHR corresponding

to the moment of motivational drops did not qualify as

an outlier for all subjects, despite rapid heartbeat be-

ing was reported as a physiological discomfort in the

Post-questionnaire by eight subjects.

5.2.3 LF/HF Ratio

Empirical evidence suggests that the activity of

theSympathetic Nervous System (SNS) inﬂuences the

low frequency band (LF) of the HRV, from 0.04 to

0.15 Hz, while the Parasympathetic Nervous Sys-

tem (PNS) is predominantly reﬂected in the high

frequency band (HF), from 0.15 to 0.4 Hz, and

Figure 6: Box plots of the range of variation in subjects’

IHR, and the IHR corresponding to the onset of motiva-

tional drops.

also possibly in a proportion of LF (Malik et al.,

1996). By comparing the timepoint at which the

subjects reported in the experiment that the Motiva-

tion drop occurred (i.e., the point in time at which

they felt a physical or psychological change) with

the changes in sympathetic-vagal balance that were

found through the HRV analysis. The low-Frequency

power/ high-Frequency power (LF/HF) ratio assumes

that a low LF/HF ratio reﬂects parasympathetic dom-

inance. This is seen when we conserve energy and

engage in tend-and-befriend behaviors. In contrast,

a high LF/HF ratio indicates sympathetic dominance,

which occurs when we engage in ﬁght-or-ﬂight be-

haviors or parasympathetic withdrawal (Shaffer and

Ginsberg, 2017). Pagani proposed to combine low

frequency band (LF) and high frequency band (HF)

into the low-to-high frequency ratio (LF/HF) as an in-

dex for the sympathovagal balance between the two

nervous systems (Pagani et al., 1986). But this theory

also received some criticism. Such as in a compre-

hensive study by Billman, it was conclusively shown

that sympathovagal balance cannot be quantiﬁed by

a single number, the LF/HF, which assumes a sim-

plistic linear relationship between the activity of the

nervous systems and the frequency bands (Billman,

2013). From the results of HRV analysis in frequency

domain, among the 68 reported motivation drops, 44

occurred when the LF/HF ratio was below 0.5. In

healthy adult, during rest ratio (LF/HF) is 1:2 (Ti-

wari et al., 2021). However, not all instances of self-

reported motivation drops coincide with a low LF/HF

ratio. Subject 4 exhibited a consistent absence of mo-

tivation drops during running, concomitant with the

sustained maintenance of an LF/HF ratio below 0.5.

Unfortunately, these ﬁndings in the ECG signal do not

form a consistent indicator that directly points to the

occurrence of decreased motivation.

Really Can’t Hold On Anymore? Physiological Indicators Versus Self-Reported Motivation Drop During Jogging

827

5.3 sEMG and RSP Analysis and

Observation

During jogging, the frequency of breathing and the

motion state of the upper and lower limb muscles of-

ten play a crucial role in determining whether to cease

exercise. The time nodes of self-reported motivation

drops were marked on the respiration rate graph and

the thigh sEMG signal graph. Through observation, it

was noted that the peak physiological signals, namely

the peak respiration rate and muscle signal, did not

consistently align with the self-reported motivation

drop points. Despite subjects repeatedly reaching the

peak of the biological signal, interpreted as the phys-

iological limit value for the current exercise (deﬁned

here as the highest peak value not breached during

exercise), they did not report a decrease in motivation

each time the limit value was reached. This suggests

a lack of signiﬁcant association between respiration

signals and sEMG signals on motivation drops.

The respiration rate chart clearly illustrates that

subjects proactively adjust their respiration rate after

reporting motivation drops, reducing it through con-

scious modulation. This aligns with the subject’s def-

inition of decreased motivation, and the decrease in

breathing rate aids in continuing the exercise. Con-

versely, excessive breathing rate or shortness of breath

may induce a sense of decreased motivation.

However, in sEMG from the thigh, this modula-

tion consciousness is less evident. This observation

may be linked to the experimental environment, as

jogging on the treadmill aligns the running rhythm

closely with the treadmill setting, limiting the vari-

ability in thigh muscle movement.

5.4 Self-Similarity Matrix Analysis

Results

Self-similarity matrix reports abnormal segments of

physiological signals and segments with similar pat-

tern or features through diagonal and blocks showed

in matrix in Figure 7. This results are produced by

subject 6 who has 4 self-reported motivation drops

time interval 800s-900s out of the whole 20min jog-

ging. In total, subject 6 reported 15 times motiva-

tion drops in 20min jogging. The top row shows the

raw data of ECG, sEMG and RSP, the middle part is

the self-similarity matrix and in the bottom row, those

peak values represents changes happened in signals.

The self-similarity matrix of ECG presents clearly the

periodicity, in contrast, the matrix of sEMG point to

non-periodicity and very little similarity. And the ma-

trix of respiration indicates respiration system stayed

in a stable state.

However, all three analysis (diagonal matrix,

blocks and secondary matrix) mentioned in last sec-

tion shows that there isn’t some signiﬁcant abnor-

mal pattern when participants self-reported motiva-

tion drops. Peak values can suggests certain changes,

but the difference between the peak and the mean is

not signiﬁcant. Not enough to reveal an abnormal sig-

nal fragment. Also, the frequency of peaks was much

higher than the number of participant complaints, so

changes in physiological signals could not be recog-

nized as antecedents or consequences of motivation

drops.

Figure 7: Subject 6: Self-Similarity Matrix (SSM) of ECG,

sEMG, RSP, Window length: 2s, overlap: 50%.

5.5 LSTM Prediction

The predicted probability of motivation drops based

on labeled data by self-reported motivation drops dur-

ing jogging is shown in Figure 8. Firstly, the gener-

ally low predicted values (ranging from 0 to 1) stem

from the extreme imbalance within the database. .

Secondly, the occurrence frequency of time points

predicted to have relatively high probabilities signiﬁ-

cantly surpasses the frequency of subjects experienc-

ing motivation drops. This suggests that, according to

the LSTM learning results, signal fragments resem-

bling the characteristics of the fragment labeled at the

time of motivation drops occur much more frequently

than autonomously reported motivation drops. This

also indicates that the pattern of the signal corre-

sponding to autonomously reported motivation drops

is not rare in the LSTM model’s learning results.

Meanwhile, in both the validation and test sets,

only a limited number of signal segments at the time

points labeled as motivation drops were predicted to

share similar characteristics with those labeled as 1 in

the training set, and this occurred with higher proba-

bility. This illustrates that the commonality between

the labeled segments is not apparent. However, it is

essential to note that the extreme imbalance in the

database and the person-dependent nature of the re-

search methodology might hinder deﬁnitive conclu-

sions regarding whether the lack of commonality is

intrinsic to the signals or if it results from insufﬁcient

BIOSIGNALS 2024 - 17th International Conference on Bio-inspired Systems and Signal Processing

828

data at the labeled motivation drop, thereby impact-

ing optimal learning. An observable periodicity is

evident, potentially linked to the periodic nature of

ECG and respiration signals. Remarkably, this peri-

odicity remains unaffected by the presence or absence

of motivation drops. Partial ﬁndings indicate that the

aggregate predicted value tends to be lower than in-

stances of self-reported motivation drops during seg-

ments when no motivational drops is reported.

Figure 8: Left to right: Subject1-4 (row 1), Subject 5-8

(row 2), Subject 9-11 (row 3), windowlength: 4000, over-

lap 50%, Predicted results on validation set. Blue square:

self-reported motivation drops. Left: y-scale: 0-1, Right:

y-scale: 0-0.5.

5.6 Discussion

This study aimed to explore whether the discom-

forts reported and triggered by decreased motiva-

tion had discernible effects on monitored physio-

logical signals. A comprehensive analysis frame-

work was proposed in this study which inlcudes data

acquisition, pre-/post- questionnaire, HRV analysis,

self-similarity matrix analysis and machine leanring

with LSTM model. ECG and RSP serve as rep-

resentative signals in the experiments, enabling the

characterization of the proposed main physiologi-

cal discomforts. These signals can be collected in

real-time without interfering with exercise, making

them justiﬁed for studying motivation drops during

physical activity. Although EDA, mentioned sev-

eral times as a physiological discomfort, was not ex-

plored in the experiments, its widespread use in health

monitoring devices suggests potential applications in

motivation-related studies, opening avenues for inte-

grating motivation-related functions into health mon-

itoring devices.

In HRV analysis, ECG signal segments corre-

sponding to the onset of autonomously reported mo-

tivation decrements were analyzed in both time and

frequency domains. The resulting indicators (RR-

Interval, IHR, LF/HF ratio) fell within the nor-

mal range, indicating that these signal segments

lacked observable abnormalities. On the contrary,

the self-similarity matrix focused on detecting mu-

tated abnormal segments in the three signals (ECG,

sEMG, RSP). Results demonstrated signiﬁcant pat-

tern changes in all three signals at the moment of

motivation drops. However, compared to motor seg-

ments without reported motivation decrements, the

signals corresponding to the onset of self-reported

motivation decrements showed no observable pattern

changes, suggesting that no abnormal physiological

signals were generated alongside motivation decre-

ments.

The prediction results of the LSTM were not

highly convincing, attributed to database size and bal-

ancing issues. For a single subject, the LSTM model

did not sufﬁciently learn features of physiological sig-

nal segments corresponding to autonomous reporting

of motivation drops. Improvements could be made

by increasing single-subject data length and balancing

the database. Nonetheless, the learning results still

indicate that signal fragments with high similarity to

those labeled as motivation drops appear frequently

and at non-subject-mentioned time points. The dif-

ﬁculty in discriminating the model when signal frag-

ments labeled as motivation drops appeared in the test

set was due to low similarity of representative features

between these fragments.

The experimental design did not account for gen-

der differences in the autonomous expression of mo-

tivational decrements, with male subjects reporting

them twice as often as female subjects. Females

tended to hesitate in reporting and employed positive

mental cues, while males exhibited a stronger need

for communication when bored, potentially inﬂuenc-

ing the frequency of reported motivation drops. De-

spite efforts to control the experimental environment,

self-reported data’s highly subjective nature reveals

signiﬁcant variability in subjects’ processing and re-

action to the environment and decision-making.

Really Can’t Hold On Anymore? Physiological Indicators Versus Self-Reported Motivation Drop During Jogging

829

6 CONCLUSION AND FUTURE

WORK

Contrary to hyphothesis, our ﬁndings reveal the ab-

sence of a direct link between self-reported motiva-

tion and biological signals. Speciﬁcally, biological

signals prove inadequate as reliable indicators of per-

sonalized motivation drops. Instances of motivation

drops do not elicit abnormal biological signals, and

certain biological signals peak without corresponding

to motivation drops.

While diminished motivation prompts exercise

cessation, it does not manifest in discernible alter-

ations in biological signals. Our study introduces a

pioneering hypothesis, aiming to explore whether mo-

tivation drop elicits abnormalities in biological sig-

nals or self-comparative irregularities. Future in-

vestigations should also prioritize database equilib-

rium, striving to achieve a balanced representation.

Addressing the data balancing issue before training

may enhance the interpretability and credibility of

the predicted results. The focus ought to shift from

singularly scrutinizing predictive probability values

to emphasizing trends foreseen by the algorithm.

In essence, attention should pivot towards instances

when the probability of motivation drop exhibits con-

tinuous or relatively substantial escalation, rather than

ﬁxating on discrete probability values at isolated time

points.

Assessing the trustworthiness of self-reporting is

challenging due to the strongly subjective nature of

self-reported motivation drops. In future studies,

objective descriptions of motivation drops can miti-

gate the effects of personal dependence brought about

by subjective awareness in studies exploring whether

motivational drop causes abnormal physiological sig-

nals. Natural facial expressions (Bian et al., 2023;

Veldanda et al., 2024), as an auxiliary to physiologi-

cal signals, also have the potential to provide another

dimension of information.

REFERENCES

Baldassarri, S., Garc

ıa de Quir

os, J., Beltr

an, J. R., and

Alvarez, P. (2023). Wearables and machine learning

for improving runners’ motivation from an affective

perspective. Sensors, 23(3):1608.

Bauer, C. and Kratschmar, A. (2015). Designing a music-

controlled running application. Proceedings of the

33rd Annual ACM Conference Extended Abstracts on

Human Factors in Computing Systems.

Beh, H. C. (1990). Achievement motivation, performance

and cardiovascular activity. International Journal of

Psychophysiology, 10(1):39–45.

Bello, J. P., Grosche, P., M

uller, M., and Weiss, R. (2018).

Content-based methods for knowledge discovery in

music. Springer Handbook of Systematic Musicology,

page 823–840.

Bian, Y., K

uster, D., Liu, H., and Krumhuber, E. G.

(2023). Understanding naturalistic facial expressions

with deep learning and multimodal large language

models. Sensors, 24(1):126.

Billman, G. E. (2013). The lf/hf ratio does not accurately

measure cardiac sympatho-vagal balance. Frontiers in

Physiology, 4.

Cai, L., Yan, S., Ouyang, C., Zhang, T., Zhu, J., Chen,

L., and Liu, H. (2024). Associating endpoint accu-

racy and similarity of muscle synergies. In BIOSIG-

NALS 2024 — 17th International Conference on Bio-

Inspired Systems and Signal Processing. INSTICC,

SciTePress.

Cai, L., Yan, S., Ouyang, C., Zhang, T., Zhu, J., Chen,

L., Ma, X., and Liu, H. (2023). Muscle synergies

in joystick manipulation. Frontiers in Physiology,

14:1282295.

ChuDuc, H., NguyenPhan, K., and NguyenViet, D. (2013).

A review of heart rate variability and its applications.

APCBEE Procedia, 7:80–85.

Electrophysiology, T. F. (1996). Heart rate variability. Cir-

culation, 93(5):1043–1065.

Hartmann, Y., Liu, H., Lahrberg, S., and Schultz, T. (2022).

Interpretable high-level features for human activity

recognition. In Proceedings of the 15th Interna-

tional Joint Conference on Biomedical Engineering

Systems and Technologies (BIOSTEC 2022) - Volume

4: BIOSIGNALS, pages 40–49.

Hartmann, Y., Liu, H., and Schultz, T. (2023). High-level

features for human activity recognition and model-

ing. In Roque, A. C. A., Gracanin, D., Lorenz, R.,

Tsanas, A., Bier, N., Fred, A., and Gamboa, H., edi-

tors, Biomedical Engineering Systems and Technolo-

gies, pages 141–163, Cham. Springer Nature Switzer-

land.

Jarchi, D. and Casson, A. J. (2017). Towards

photoplethysmography-based estimation of in-

stantaneous heart rate during physical activity.

IEEE Transactions on Biomedical Engineering,

64(9):2042–2053.

Kanfer, R. and Ackerman, P. (2000). Individual differences

in work motivation: Further explorations of a trait

framework. Applied Psychology, 49(3):470–482.

Krittayaphong, R., Muenkaew, M., Chiewvit, P., Ratanasit,

N., Kaolawanich, Y., Phrommintikul, A., and CORE

Investigators (2019). Electrocardiographic predictors

of cardiovascular events in patients at high cardiovas-

cular risk: a multicenter study. J. Geriatr. Cardiol.,

16(8):630–638.

Laitala, J., Jiang, M., Syrj

a, E., Naeini, E. K., Airola, A.,

Rahmani, A. M., Dutt, N. D., and Liljeberg, P. (2020).

Robust ecg r-peak detection using lstm. Proceedings

of the 35th Annual ACM Symposium on Applied Com-

puting.

Litscher, G., He, W., Yi, S.-H., and Wang, L.

(2014). Heart rate variability and complementary

BIOSIGNALS 2024 - 17th International Conference on Bio-inspired Systems and Signal Processing

830

medicine. Evidence-Based Complementary and Al-

ternative Medicine, 2014:1–2.

Liu, H. and Schultz, T. (2022). How long are various types

of daily activities? statistical analysis of a multimodal

wearable sensor-based human activity dataset. In Pro-

ceedings of the 15th International Joint Conference

on Biomedical Engineering Systems and Technologies

(BIOSTEC 2022) - Volume 5: HEALTHINF, pages

680–688.

Liu, H., Xue, T., and Schultz, T. (2023). On a real real-time

wearable human activity recognition system. In Pro-

ceedings of the 16th International Joint Conference

on Biomedical Engineering Systems and Technologies

(BIOSTEC 2023) - WHC, pages 711–720.

Malik, M., Bigger, J. T., Camm, A. J., Kleiger, R. E.,

Malliani, A., Moss, A. J., and Schwartz, P. J. (1996).

Heart rate variability: Standards of measurement,

physiological interpretation, and clinical use. Euro-

pean Heart Journal, 17(3):354–381.

Martin, K., Meeusen, R., Thompson, K. G., Keegan, R.,

and Rattray, B. (2018). Mental fatigue impairs en-

durance performance: A physiological explanation.

Sports Medicine, 48(9):2041–2051.

Melliti, N., Zarrouk, F., and Souissi, N. (2016). Motiva-

tion expectations and motivational styles adopted by

the physical education teacher towards his students:

A study in a natural context of teaching and learning.

Creative Education, 07(15):2226–2250.

Moradi, J., Bahrami, A., and Dana, A. (2020). Motivation

for participation in sports based on athletes in team

and individual sports. Physical Culture and Sport.

Studies and Research, 85(1):14–21.

Morici, G., Gruttad’Auria, C. I., Baiamonte, P., Mazzuca,

E., Castrogiovanni, A., and Bonsignore, M. R. (2016).

Endurance training: Is it bad for&you?; Breathe,

12(2):140–147.

Pagani, M., Lombardi, F., Guzzetti, S., Rimoldi, O.,

Furlan, R., Pizzinelli, P., Sandrone, G., Malfatto, G.,

Dell’Orto, S., and Piccaluga, E. (1986). Power spec-

tral analysis of heart rate and arterial pressure vari-

abilities as a marker of sympatho-vagal interaction

in man and conscious dog. Circulation Research,

59(2):178–193.

Paulus, J., M

uller, M., and Klapuri, A. (2010). State of

the art report: Audio-based music structure analysis.

pages 625–636.

Reaz, M. B., Hussain, M. S., and Mohd-Yasin, F. (2006).

Techniques of emg signal analysis: Detection, pro-

cessing, classiﬁcation and applications. Biological

Procedures Online, 8(1):11–35.

Rodrigues, J., Liu, H., Folgado, D., Belo, D., Schultz,

T., and Gamboa, H. (2022). Feature-based informa-

tion retrieval of multimodal biosignals with a self-

similarity matrix: Focus on automatic segmentation.

Biosensors, 12(12):1182.

Ryan, R. M., Lynch, M. F., Vansteenkiste, M., and Deci,

E. L. (2010). Motivation and autonomy in counseling,

psychotherapy, and behavior change: A look at the-

ory and practice 1ψ7. The Counseling Psychologist,

39(2):193–260.

Santos, A., Rodrigues, J., Folgado, D., Santos, S., Fuj

ao,

C., and Gamboa, H. (2021). Self-similarity matrix

of morphological features for motion data analysis in

manufacturing scenarios. Proceedings of the 14th In-

ternational Joint Conference on Biomedical Engineer-

ing Systems and Technologies.

Shaffer, F. and Ginsberg, J. P. (2017). An overview of heart

rate variability metrics and norms. Frontiers in Public

Health, 5.

Shi, W., Zhou, C., Zhang, Y., Li, K., Ren, X., Liu, H., and

Ye, X. (2023). Hybrid modeling on reconstitution of

continuous arterial blood pressure using ﬁnger photo-

plethysmography. Biomedical Signal Processing and

Control, 85:104972.

Taylor, I. M., Boat, R., and Murphy, S. L. (2018). Integrat-

ing theories of self-control and motivation to advance

endurance performance. International Review of Sport

and Exercise Psychology, 13(1):1–20.

Tiwari, R., Kumar, R., Malik, S., Raj, T., and Kumar, P.

(2021). Analysis of heart rate variability and implica-

tion of different factors on heart rate variability. Cur-

rent Cardiology Reviews, 17(5).

Veldanda, A., Liu, H., Koschke, R., Schultz, T., and K

uster,

D. (2024). Can electromyography alone reveal facial

action units? a pilot emg-based action unit recogni-

tion study with real-time validation. In BIODEVICES

2024 — 17th International Conference on Biomedical

Electronics and Devices. INSTICC, SciTePress.

Wang, H., Li, K., Liu, H., Ye, X., and Zhou, C. (2024).

Comfort assessment method of eeg-based exoskele-

ton walkingassistive device. In BIOSIGNALS 2024

— 17th International Conference on Bio-Inspired Sys-

tems and Signal Processing. INSTICC, SciTePress.

Weiss, M. R. and Williams, L. (2004). The Why of Youth

Sport Involvement: A Developmental Perspective on

Motivational Processes., page 223–268. Fitness In-

formation Technology.

Wu, C., Huang, Y., and Hwang, J. (2015). Review of af-

fective computing in education/learning: Trends and

challenges. British Journal of Educational Technol-

ogy, 47(6):1304–1323.

Really Can’t Hold On Anymore? Physiological Indicators Versus Self-Reported Motivation Drop During Jogging

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