21 research outputs found
Ritual Slavonic dances «skoki»
No full textУ статті розкривається семантика реліктових весільних танців українців та
білорусів, пов’язаних з обрядами випікання короваю й першої шлюбної ночі. Автор
вбачає в архаїчних «скоках» пережитки язичницької продукуючої магії, покликаної
забезпечити успішне продовження роду.В статье раскрывается семантика реликтовых свадебных танцев украинцев
и белорусов, связанных с обрядами выпекания каравая и первой брачной ночи.
Автор видит в архаических «скоках» пережитки языческой продуцирующей магии,
призванной обеспечить успешное продолжение рода.The article discloses semantics of relict wedding dances of Ukrainians and Byelorussians
associated with rituals of round loaf baking and the first married night. The author sees in
archaic «skoki» vestiges of pagan producing magic urged to ensure successful procreation
Sheet Music Transcription from Audio Record of a Song Played on Sopile
No full textSopila je tradicionalni, ručno izradeni puhački instrument, uobičajeno sviran u paru, karakterističan za Istarski poluotok u zapadnoj Hrvatskoj. Njezin prodoran zvuk, zajedno sa dvoglasnim pjevanjem u Istarskoj ljestvici sačinjenoj od šest tonova, proglašen je UNESCOvom ne materijalnom baštinom čovječanstva. Postupak pretvorbe audio zapisa u ljudski prihvatljivi oblik - note, uključuje prepoznavanje više tonova i njihove visine. Predloženo rješenje rješava spomenute probleme izvlačeći svojstva frekvencijskog spektra, koristeći tehnike strojnog učenja pod nadzorom, te dodatnom obradom nakon predvidanja. Najbolji model za predvidanje visine tona, odabran je koristeći mrežno pretraživanje za dvije ML tehnike, dodajući, po izboru, izvučena svojstva frekvencijskog spektra. Model je ostvario obečavajuću preciznost za obje postavke, monofonu i polifonu, na novostvorenom skupu podataka (namijenjenom ovoj svrsi). Cjelokupno AMT rješenje, koje se sastoji od mobilne aplikacije i poslužiteljskog API-ja, je dodatno razvijeno. Mobilna aplikacija služi snimanju, označavanju, i učitavanju zvučnih datoteka, dok poslužitelj sadrži logiku i postupke obrade datoteka u notni zapis koji se vraća kao rezultat. Na taj način je pokazano kako prikupljanje i očuvanje tradicionalne glazbe sopila, može biti jednostavno primijenjeno u stvarnoj upotrebi.Sopila is a traditional hand-made woodwind instrument, which is usually played in pair. It can be found in western parts of Croatia, along with two-part singing in the hexatonic Istrian scale. Both sopile and singing, were registered in the UNESCO Representative List of the Intangible Cultural Heritage of Humanity. This thesis serves as a insight study of automatic music transcription (AMT) for sopile instrument. In order to do that, pitch detection, along with its onsets and offsets had to be provided. This was achieved using frequency-features extraction, supervised machine learning (ML) algorithms, and postprocessing correction methods. With grid search, the best tone-prediction model was determined, optionally using frequency-feature extraction. Datasets used for training and testing were generated for the purpose of this work and performance of the proposed models, for both monophonic and polyphonic setups, were very satisfying. In order to provide insight into how proposed model could be used, proof-of-concept AMT system was developed. It consisted from mobile application for recording, tagging, and uploading audio sources, as well as back-end server that contained ML, preprocessing and postprocessing logic for creating music sheets. End result was pushed back to the mobile application, and thus was demonstrated how preserving traditional music could be done on-the-go and effortlessly
Procjena karakteristika i oporavka igrača u nogometu zasnovana na podatcima : doktorski rad
No full textData collection in soccer encompasses a variety of methods, ranging from simple daily ques
tionnaires that monitor subjective well-being to more sophisticated approaches such as wearable
sensors that track external load during training and matches. While subjective measures like
wellness questionnaires and Rate of Perceived Exertion (RPE) offer insights into players’ inter
nal states, they fall short of providing comprehensive assessments of players’ physical fitness
and performance. Consequently, the use of wearable sensors has become prevalent, offering
detailed metrics such as distance covered, sprints, accelerations, and energy expenditure. These
metrics enable coaches to better manage training loads, plan sessions and monitor the return-to
play (RTP) process after injuries.
However, traditional methods that aggregate data across entire matches often hide the vari
ations in players’ performance throughout the game. This dissertation addresses this issue by
implementing a minute-by-minute analysis of player physical intensity using wearable data, re
vealing more details about players’ fitness levels. By examining how physical performance
fluctuates within different game contexts, the study provides a more accurate reflection of a
player’s condition and readiness.
The relationship between cognitive load (CL) and physical performance is another critical
aspect explored in this work. WhileCL’simpactontacticalandtechnicalaspects is well-studied,
its effects on physical performance, particularly in real-world settings, remain unexplored. Us
ing data fromtheNASA-TLXquestionnaire, thisresearchinvestigates how varying levels of CL
influence physical metrics such as total distance, high-speed running, and deceleration. More
over, the study applies machine learning (ML) algorithms to classify players into motivation
clusters, offering insights into how different motivational factors might affect performance un
der cognitive stress.
Injury recovery is a crucial area in sports, and the RTP process is crucial in ensuring players
return to competition safely. This dissertation advances the understanding of injury manage
ment by developing ML models to predict recovery duration, using data collected by medical
staff. These models are compared with traditional expert-based predictions, demonstrating that
the integration of expert input with ML techniques enhances the accuracy of recovery time es
timates.
In addition to injury recovery, the research focuses on developing individualized fatigue
and recovery profiles. These profiles are essential for conditioning coaches to tailor training regimens and ensure that players return to their pre-injury fitness levels. By providing a de
tailed, data-driven approach to monitoring physical recovery, this dissertation contributes valu
able tools for optimizing player performance and reducing the risk of re-injury.
This work provides a comprehensive framework for soccer performance and recovery anal
ysis, integrating wearable sensor data, cognitive load assessments, and ML techniques. The
findings offer practical applications for coaches and sports scientists, helping them to better un
derstand and manage the complex dynamics of player fitness and performance throughout the
season.Prikupljanje podataka u nogometu obuhvaća različite metode, od jednostavnih dnevnih upit
nika koji prate subjektivan dojam opterećenja pojedinca, do skupljih tehnoloških pristupa poput
nosivih senzora koji prate vanjsko opterećenje sportaša tijekom treninga i utakmica. Dok sub
jektivne mjere poput upitnika za praćenje dobrobiti i ocjene doživljenog napora (RPE) pružaju
uvid u unutarnje stanje igrača, one nisu dovoljne za sveobuhvatnu sliku karakteristika igrača.
Iz tog razloga je upotreba nosivih senzora postala raširena, pružajući podatke o prijeđenoj udal
jenosti, broju sprintova, ubrzanjima i potrošnji energije. Ove informacije omogućavaju trener
ima bolju kontrolu opterećenja tijekom treninga, planiranje istih i praćenje procesa povratka na
teren nakon ozljeda.
Međutim,tradicionalnemetodeprikupljajupodatkenarazinicijeleutakmicečimeseonemogućava
pregled izvedbe igrača tijekom samog trajanja utakmice. Ova disertacija nastoji riješiti taj prob
lem primjenom podatkovne analize intenziteta igrača minutu-kroz-minutu koristeći podatke no
sivih senzora. Pregledom promjena u fizičkim izvedbama tijekom različnih trenutaka u igri,
pristup pruža precizniji odraz stanja i spremnosti igrača.
Odnos između kognitivnog opterećenja i fizičkog opterećenja igrača također je jedan od
fokusa ovog rada. Dok je utjecaj kognitivnog opterećenja na taktičke i tehničke izvedbe igrača
dobro istražen, njegov utjecaj na njihovo fizičko opterećenje, osobito tijekom utakmica, ostaje
neistražen. Koristeći podatke iz NASA-TLX upitnika, ispituje se kako različite razine kogni
tivnog opterećenja utječu na fizičke mjere poput ukupne prijeđene udaljenosti, sprinta, ubrza
vanja i usporavanja. Nadalje, primjenjuju se algoritmi strojnog učenja za klasifikaciju igrača u
motivacijske grupe, pružajući uvid u to kako različiti motivacijski čimbenici mogu utjecati na
izvedbe pod mentalnim opterećenjem.
Oporavak od ozljeda je jedan od ključnih čimbenika u sportu, a dobro planiranje procesa
oporavka je presudno za siguran povratak igrača na teren. Razvijanjem modela strojnog učenja
koji predviđaju trajanje oporavka nakon mišićnih ozljeda, poboljšava se točnost u samom plani
ranju. Modeli strojnog učenja uspoređeni su s procjenama stručnjaka, gdje se pokazalo kako je
upravo kombinacija modela i znanja stručnjaka najbolja za procjenu vremena oporavka.
Kondicijski treneri moraju dobro poznavati igrače kako bi iz svakog izvukli maksimum i
pripremili ih za sezonu. Iz tog razloga, vrlo je korisno imati uvid u individualni profil fizičke
spreme igrača kako bi treneri mogli prilagoditi program treninga. Podatkovni pristup doprinosi
lakšem praćenju opterećenja i smanjivanju rizika igrača od ozljede. Korištenjem podataka iz različitih izvora, kreirane su metode koje treneri, analitičari, i sve
osobekojeradeunogometumogukoristitikakobidobilidetaljnijeinformacijeosvojimigračima.
Podatci su dobiveni korištenjem nosivih senzora i upitnika za procjenu kognitivnog opterećenja
te su isti iskorišteni na različiti načine uz primjenu tehnika strojnog učenja i optimizacije. Cilj
ovakvog pristupa je razvijanje objektivnih metoda koje se mogu automatizirati i olakšati posao
ljudima koji rade u nogometu
Procjena karakteristika i oporavka igrača u nogometu zasnovana na podatcima : doktorski rad
No full textData collection in soccer encompasses a variety of methods, ranging from simple daily ques
tionnaires that monitor subjective well-being to more sophisticated approaches such as wearable
sensors that track external load during training and matches. While subjective measures like
wellness questionnaires and Rate of Perceived Exertion (RPE) offer insights into players’ inter
nal states, they fall short of providing comprehensive assessments of players’ physical fitness
and performance. Consequently, the use of wearable sensors has become prevalent, offering
detailed metrics such as distance covered, sprints, accelerations, and energy expenditure. These
metrics enable coaches to better manage training loads, plan sessions and monitor the return-to
play (RTP) process after injuries.
However, traditional methods that aggregate data across entire matches often hide the vari
ations in players’ performance throughout the game. This dissertation addresses this issue by
implementing a minute-by-minute analysis of player physical intensity using wearable data, re
vealing more details about players’ fitness levels. By examining how physical performance
fluctuates within different game contexts, the study provides a more accurate reflection of a
player’s condition and readiness.
The relationship between cognitive load (CL) and physical performance is another critical
aspect explored in this work. WhileCL’simpactontacticalandtechnicalaspects is well-studied,
its effects on physical performance, particularly in real-world settings, remain unexplored. Us
ing data fromtheNASA-TLXquestionnaire, thisresearchinvestigates how varying levels of CL
influence physical metrics such as total distance, high-speed running, and deceleration. More
over, the study applies machine learning (ML) algorithms to classify players into motivation
clusters, offering insights into how different motivational factors might affect performance un
der cognitive stress.
Injury recovery is a crucial area in sports, and the RTP process is crucial in ensuring players
return to competition safely. This dissertation advances the understanding of injury manage
ment by developing ML models to predict recovery duration, using data collected by medical
staff. These models are compared with traditional expert-based predictions, demonstrating that
the integration of expert input with ML techniques enhances the accuracy of recovery time es
timates.
In addition to injury recovery, the research focuses on developing individualized fatigue
and recovery profiles. These profiles are essential for conditioning coaches to tailor training regimens and ensure that players return to their pre-injury fitness levels. By providing a de
tailed, data-driven approach to monitoring physical recovery, this dissertation contributes valu
able tools for optimizing player performance and reducing the risk of re-injury.
This work provides a comprehensive framework for soccer performance and recovery anal
ysis, integrating wearable sensor data, cognitive load assessments, and ML techniques. The
findings offer practical applications for coaches and sports scientists, helping them to better un
derstand and manage the complex dynamics of player fitness and performance throughout the
season.Prikupljanje podataka u nogometu obuhvaća različite metode, od jednostavnih dnevnih upit
nika koji prate subjektivan dojam opterećenja pojedinca, do skupljih tehnoloških pristupa poput
nosivih senzora koji prate vanjsko opterećenje sportaša tijekom treninga i utakmica. Dok sub
jektivne mjere poput upitnika za praćenje dobrobiti i ocjene doživljenog napora (RPE) pružaju
uvid u unutarnje stanje igrača, one nisu dovoljne za sveobuhvatnu sliku karakteristika igrača.
Iz tog razloga je upotreba nosivih senzora postala raširena, pružajući podatke o prijeđenoj udal
jenosti, broju sprintova, ubrzanjima i potrošnji energije. Ove informacije omogućavaju trener
ima bolju kontrolu opterećenja tijekom treninga, planiranje istih i praćenje procesa povratka na
teren nakon ozljeda.
Međutim,tradicionalnemetodeprikupljajupodatkenarazinicijeleutakmicečimeseonemogućava
pregled izvedbe igrača tijekom samog trajanja utakmice. Ova disertacija nastoji riješiti taj prob
lem primjenom podatkovne analize intenziteta igrača minutu-kroz-minutu koristeći podatke no
sivih senzora. Pregledom promjena u fizičkim izvedbama tijekom različnih trenutaka u igri,
pristup pruža precizniji odraz stanja i spremnosti igrača.
Odnos između kognitivnog opterećenja i fizičkog opterećenja igrača također je jedan od
fokusa ovog rada. Dok je utjecaj kognitivnog opterećenja na taktičke i tehničke izvedbe igrača
dobro istražen, njegov utjecaj na njihovo fizičko opterećenje, osobito tijekom utakmica, ostaje
neistražen. Koristeći podatke iz NASA-TLX upitnika, ispituje se kako različite razine kogni
tivnog opterećenja utječu na fizičke mjere poput ukupne prijeđene udaljenosti, sprinta, ubrza
vanja i usporavanja. Nadalje, primjenjuju se algoritmi strojnog učenja za klasifikaciju igrača u
motivacijske grupe, pružajući uvid u to kako različiti motivacijski čimbenici mogu utjecati na
izvedbe pod mentalnim opterećenjem.
Oporavak od ozljeda je jedan od ključnih čimbenika u sportu, a dobro planiranje procesa
oporavka je presudno za siguran povratak igrača na teren. Razvijanjem modela strojnog učenja
koji predviđaju trajanje oporavka nakon mišićnih ozljeda, poboljšava se točnost u samom plani
ranju. Modeli strojnog učenja uspoređeni su s procjenama stručnjaka, gdje se pokazalo kako je
upravo kombinacija modela i znanja stručnjaka najbolja za procjenu vremena oporavka.
Kondicijski treneri moraju dobro poznavati igrače kako bi iz svakog izvukli maksimum i
pripremili ih za sezonu. Iz tog razloga, vrlo je korisno imati uvid u individualni profil fizičke
spreme igrača kako bi treneri mogli prilagoditi program treninga. Podatkovni pristup doprinosi
lakšem praćenju opterećenja i smanjivanju rizika igrača od ozljede. Korištenjem podataka iz različitih izvora, kreirane su metode koje treneri, analitičari, i sve
osobekojeradeunogometumogukoristitikakobidobilidetaljnijeinformacijeosvojimigračima.
Podatci su dobiveni korištenjem nosivih senzora i upitnika za procjenu kognitivnog opterećenja
te su isti iskorišteni na različiti načine uz primjenu tehnika strojnog učenja i optimizacije. Cilj
ovakvog pristupa je razvijanje objektivnih metoda koje se mogu automatizirati i olakšati posao
ljudima koji rade u nogometu
Procjena karakteristika i oporavka igrača u nogometu zasnovana na podatcima : doktorski rad
No full textData collection in soccer encompasses a variety of methods, ranging from simple daily ques
tionnaires that monitor subjective well-being to more sophisticated approaches such as wearable
sensors that track external load during training and matches. While subjective measures like
wellness questionnaires and Rate of Perceived Exertion (RPE) offer insights into players’ inter
nal states, they fall short of providing comprehensive assessments of players’ physical fitness
and performance. Consequently, the use of wearable sensors has become prevalent, offering
detailed metrics such as distance covered, sprints, accelerations, and energy expenditure. These
metrics enable coaches to better manage training loads, plan sessions and monitor the return-to
play (RTP) process after injuries.
However, traditional methods that aggregate data across entire matches often hide the vari
ations in players’ performance throughout the game. This dissertation addresses this issue by
implementing a minute-by-minute analysis of player physical intensity using wearable data, re
vealing more details about players’ fitness levels. By examining how physical performance
fluctuates within different game contexts, the study provides a more accurate reflection of a
player’s condition and readiness.
The relationship between cognitive load (CL) and physical performance is another critical
aspect explored in this work. WhileCL’simpactontacticalandtechnicalaspects is well-studied,
its effects on physical performance, particularly in real-world settings, remain unexplored. Us
ing data fromtheNASA-TLXquestionnaire, thisresearchinvestigates how varying levels of CL
influence physical metrics such as total distance, high-speed running, and deceleration. More
over, the study applies machine learning (ML) algorithms to classify players into motivation
clusters, offering insights into how different motivational factors might affect performance un
der cognitive stress.
Injury recovery is a crucial area in sports, and the RTP process is crucial in ensuring players
return to competition safely. This dissertation advances the understanding of injury manage
ment by developing ML models to predict recovery duration, using data collected by medical
staff. These models are compared with traditional expert-based predictions, demonstrating that
the integration of expert input with ML techniques enhances the accuracy of recovery time es
timates.
In addition to injury recovery, the research focuses on developing individualized fatigue
and recovery profiles. These profiles are essential for conditioning coaches to tailor training regimens and ensure that players return to their pre-injury fitness levels. By providing a de
tailed, data-driven approach to monitoring physical recovery, this dissertation contributes valu
able tools for optimizing player performance and reducing the risk of re-injury.
This work provides a comprehensive framework for soccer performance and recovery anal
ysis, integrating wearable sensor data, cognitive load assessments, and ML techniques. The
findings offer practical applications for coaches and sports scientists, helping them to better un
derstand and manage the complex dynamics of player fitness and performance throughout the
season.Prikupljanje podataka u nogometu obuhvaća različite metode, od jednostavnih dnevnih upit
nika koji prate subjektivan dojam opterećenja pojedinca, do skupljih tehnoloških pristupa poput
nosivih senzora koji prate vanjsko opterećenje sportaša tijekom treninga i utakmica. Dok sub
jektivne mjere poput upitnika za praćenje dobrobiti i ocjene doživljenog napora (RPE) pružaju
uvid u unutarnje stanje igrača, one nisu dovoljne za sveobuhvatnu sliku karakteristika igrača.
Iz tog razloga je upotreba nosivih senzora postala raširena, pružajući podatke o prijeđenoj udal
jenosti, broju sprintova, ubrzanjima i potrošnji energije. Ove informacije omogućavaju trener
ima bolju kontrolu opterećenja tijekom treninga, planiranje istih i praćenje procesa povratka na
teren nakon ozljeda.
Međutim,tradicionalnemetodeprikupljajupodatkenarazinicijeleutakmicečimeseonemogućava
pregled izvedbe igrača tijekom samog trajanja utakmice. Ova disertacija nastoji riješiti taj prob
lem primjenom podatkovne analize intenziteta igrača minutu-kroz-minutu koristeći podatke no
sivih senzora. Pregledom promjena u fizičkim izvedbama tijekom različnih trenutaka u igri,
pristup pruža precizniji odraz stanja i spremnosti igrača.
Odnos između kognitivnog opterećenja i fizičkog opterećenja igrača također je jedan od
fokusa ovog rada. Dok je utjecaj kognitivnog opterećenja na taktičke i tehničke izvedbe igrača
dobro istražen, njegov utjecaj na njihovo fizičko opterećenje, osobito tijekom utakmica, ostaje
neistražen. Koristeći podatke iz NASA-TLX upitnika, ispituje se kako različite razine kogni
tivnog opterećenja utječu na fizičke mjere poput ukupne prijeđene udaljenosti, sprinta, ubrza
vanja i usporavanja. Nadalje, primjenjuju se algoritmi strojnog učenja za klasifikaciju igrača u
motivacijske grupe, pružajući uvid u to kako različiti motivacijski čimbenici mogu utjecati na
izvedbe pod mentalnim opterećenjem.
Oporavak od ozljeda je jedan od ključnih čimbenika u sportu, a dobro planiranje procesa
oporavka je presudno za siguran povratak igrača na teren. Razvijanjem modela strojnog učenja
koji predviđaju trajanje oporavka nakon mišićnih ozljeda, poboljšava se točnost u samom plani
ranju. Modeli strojnog učenja uspoređeni su s procjenama stručnjaka, gdje se pokazalo kako je
upravo kombinacija modela i znanja stručnjaka najbolja za procjenu vremena oporavka.
Kondicijski treneri moraju dobro poznavati igrače kako bi iz svakog izvukli maksimum i
pripremili ih za sezonu. Iz tog razloga, vrlo je korisno imati uvid u individualni profil fizičke
spreme igrača kako bi treneri mogli prilagoditi program treninga. Podatkovni pristup doprinosi
lakšem praćenju opterećenja i smanjivanju rizika igrača od ozljede. Korištenjem podataka iz različitih izvora, kreirane su metode koje treneri, analitičari, i sve
osobekojeradeunogometumogukoristitikakobidobilidetaljnijeinformacijeosvojimigračima.
Podatci su dobiveni korištenjem nosivih senzora i upitnika za procjenu kognitivnog opterećenja
te su isti iskorišteni na različiti načine uz primjenu tehnika strojnog učenja i optimizacije. Cilj
ovakvog pristupa je razvijanje objektivnih metoda koje se mogu automatizirati i olakšati posao
ljudima koji rade u nogometu
Blended learning in the engineering field
No full textBlended Learning (BL) is defined as a combination of face-to-face and digital activities that, in recent years, has been adopted more and more frequently by Higher Educational Institutions (HEIs). In the engineering field, the adoption of BL allows creating challenging situations for students with industry-like problems to foster the acquisition of advanced problem-solving skills. Thus, it can be used to enhance traditional learning by enriching it with new aspects, allowing to update the Intended Learning Outcomes traditionally defined by teachers. Although prior coronavirus disease 2019 (COVID-19) teachers had the time to prepare and programme the transition to BL, during the pandemic they had to abruptly move to the full digital delivery of the content, requiring technological and organizational adaptation, as well as change in the content teaching and assessment methods. Through a systematic literature review, this paper aims to understand how BL has been implemented in the engineering field by HEIs, discussing if and how the learning expectations of teachers (evaluated through Bloom\u27s Taxonomy) change when using different mixes of face-to-face and digital activities and when the target audience changes. More specifically, the investigation addresses how content and learning expectations are split and set in face-to-face and digital settings. Additionally, the interest is towards understanding how COVID-19 impacted the adoption of BL, not only during the pandemic but also after
Italian via email: From an online project of learning and teaching towards the development of a multi‐cultural discourse community
No full textThis paper seeks to illustrate how the use of Internet resources (specifically email and the Web) can affect and enhance language learning and cultural understanding, modify the learning environment, reduce the barriers which time, space and societal differences may create, be a source of motivation, and redefine the role of teachers and learners. Although it is based on an on‐going project, it already provides practical evidence of some advantages email and Internet resources can bring to the language learner and to the teacher. A detailed evaluation of the language outcomes is under way, but incomplete at the time of writing. This paper is nevertheless more concerned with other variables of language learning and teaching which the author considers fundamental to reach a successful degree of language use
Development of an Agile Blended Learning Framework for Engineering Higher Educational Instutions post Covid-19
No full textExtended Energy-Expenditure Model in Soccer: Evaluating Player Performance in the Context of the Game
No full textEvery soccer game influences each player’s performance differently. Many studies have tried to explain the influence of different parameters on the game; however, none went deeper into the core and examined it minute-by-minute. The goal of this study is to use data derived from GPS wearable devices to present a new framework for performance analysis. A player’s energy expenditure is analyzed using data analytics and K-means clustering of low-, middle-, and high-intensity periods distributed in 1 min segments. Our framework exhibits a higher explanatory power compared to usual game metrics (e.g., high-speed running and sprinting), explaining 45.91% of the coefficient of variation vs. 21.32% for high-, 30.66% vs. 16.82% for middle-, and 24.41% vs. 19.12% for low-intensity periods. The proposed methods enable deeper game analysis, which can help strength and conditioning coaches and managers in gaining better insights into the players’ responses to various game situations
Enhancing Biophysical Muscle Fatigue Model in the Dynamic Context of Soccer
No full textIn the field of muscle fatigue models (MFMs), the prior research has demonstrated success in fitting data in specific contexts, but it falls short in addressing the diverse efforts and rapid changes in exertion typical of soccer matches. This study builds upon the existing model, aiming to enhance its applicability and robustness to dynamic demand shifts. The objective is to encapsulate the complexities of soccer dynamics with a streamlined set of parameters. Our refined model achieved a slight improvement in the R2 score in the maximum hand-grip test, increasing from 0.87 to 0.89 compared to the existing model. It also demonstrated dynamic change robustness in a soccer-specific 1 min drill and 15 min treadmill protocol extracted from the literature. Through individualized fitting on a 10-repetition 80 m sprint test for a soccer player, the model exhibited R2 scores between 0.62 and 0.80. Furthermore, when tested with actual soccer match data, it maintained a robust performance, with the average R2 scores ranging from 0.70 to 0.72. The proposed approach holds the potential to advance the understanding of tactical decisions by correlating them with real-time physical performance, offering opportunities for more informed strategies and ultimately enhancing team performance
