A Survey on Machine Learning Approaches for Personalized Coaching with Human Digital Twins
Human Digital Twins are an emerging type of Digital Twin used in healthcare to provide personalized support. Following this trend, we intend to elevate our virtual fitness coach, a coaching platform using wearable data on physical activity, to the level of a personalized Human Digital Twin. Prelimin...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-07-01
|
| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/15/13/7528 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849704649469198336 |
|---|---|
| author | Harald H. Rietdijk Patricia Conde-Cespedes Talko B. Dijkhuis Hilbrand K. E. Oldenhuis Maria Trocan |
| author_facet | Harald H. Rietdijk Patricia Conde-Cespedes Talko B. Dijkhuis Hilbrand K. E. Oldenhuis Maria Trocan |
| author_sort | Harald H. Rietdijk |
| collection | DOAJ |
| description | Human Digital Twins are an emerging type of Digital Twin used in healthcare to provide personalized support. Following this trend, we intend to elevate our virtual fitness coach, a coaching platform using wearable data on physical activity, to the level of a personalized Human Digital Twin. Preliminary investigations revealed a significant difference in performance, as measured by prediction accuracy and F1-score, between the optimal choice of machine learning algorithms for generalized and personalized processing of the available data. Based on these findings, this survey aims to establish the state of the art in the selection and application of machine learning algorithms in Human Digital Twin applications in healthcare. The survey reveals that, unlike general machine learning applications, there is a limited body of literature on optimization and the application of meta-learning in personalized Human Digital Twin solutions. As a conclusion, we provide direction for further research, formulated in the following research question: how can the optimization of human data feature engineering and personalized model selection be achieved in Human Digital Twins and can techniques such as meta-learning be of use in this context? |
| format | Article |
| id | doaj-art-9b06eb54a2ee4e909480fad8d6d3b71c |
| institution | DOAJ |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-9b06eb54a2ee4e909480fad8d6d3b71c2025-08-20T03:16:42ZengMDPI AGApplied Sciences2076-34172025-07-011513752810.3390/app15137528A Survey on Machine Learning Approaches for Personalized Coaching with Human Digital TwinsHarald H. Rietdijk0Patricia Conde-Cespedes1Talko B. Dijkhuis2Hilbrand K. E. Oldenhuis3Maria Trocan4Lectoraat Digital Transformation, Hanze University of Applied Sciences, 9747AS Groningen, The NetherlandsLisite, Institut Supérieur D’électronique De Paris (Isep), 92130 Issy-les-Moulineaux, FranceLectoraat Digital Transformation, Hanze University of Applied Sciences, 9747AS Groningen, The NetherlandsLectoraat Digital Transformation, Hanze University of Applied Sciences, 9747AS Groningen, The NetherlandsLisite, Institut Supérieur D’électronique De Paris (Isep), 92130 Issy-les-Moulineaux, FranceHuman Digital Twins are an emerging type of Digital Twin used in healthcare to provide personalized support. Following this trend, we intend to elevate our virtual fitness coach, a coaching platform using wearable data on physical activity, to the level of a personalized Human Digital Twin. Preliminary investigations revealed a significant difference in performance, as measured by prediction accuracy and F1-score, between the optimal choice of machine learning algorithms for generalized and personalized processing of the available data. Based on these findings, this survey aims to establish the state of the art in the selection and application of machine learning algorithms in Human Digital Twin applications in healthcare. The survey reveals that, unlike general machine learning applications, there is a limited body of literature on optimization and the application of meta-learning in personalized Human Digital Twin solutions. As a conclusion, we provide direction for further research, formulated in the following research question: how can the optimization of human data feature engineering and personalized model selection be achieved in Human Digital Twins and can techniques such as meta-learning be of use in this context?https://www.mdpi.com/2076-3417/15/13/7528personalizationhuman digital twinmachine learninghealthcareartificial intelligencecoaching |
| spellingShingle | Harald H. Rietdijk Patricia Conde-Cespedes Talko B. Dijkhuis Hilbrand K. E. Oldenhuis Maria Trocan A Survey on Machine Learning Approaches for Personalized Coaching with Human Digital Twins Applied Sciences personalization human digital twin machine learning healthcare artificial intelligence coaching |
| title | A Survey on Machine Learning Approaches for Personalized Coaching with Human Digital Twins |
| title_full | A Survey on Machine Learning Approaches for Personalized Coaching with Human Digital Twins |
| title_fullStr | A Survey on Machine Learning Approaches for Personalized Coaching with Human Digital Twins |
| title_full_unstemmed | A Survey on Machine Learning Approaches for Personalized Coaching with Human Digital Twins |
| title_short | A Survey on Machine Learning Approaches for Personalized Coaching with Human Digital Twins |
| title_sort | survey on machine learning approaches for personalized coaching with human digital twins |
| topic | personalization human digital twin machine learning healthcare artificial intelligence coaching |
| url | https://www.mdpi.com/2076-3417/15/13/7528 |
| work_keys_str_mv | AT haraldhrietdijk asurveyonmachinelearningapproachesforpersonalizedcoachingwithhumandigitaltwins AT patriciacondecespedes asurveyonmachinelearningapproachesforpersonalizedcoachingwithhumandigitaltwins AT talkobdijkhuis asurveyonmachinelearningapproachesforpersonalizedcoachingwithhumandigitaltwins AT hilbrandkeoldenhuis asurveyonmachinelearningapproachesforpersonalizedcoachingwithhumandigitaltwins AT mariatrocan asurveyonmachinelearningapproachesforpersonalizedcoachingwithhumandigitaltwins AT haraldhrietdijk surveyonmachinelearningapproachesforpersonalizedcoachingwithhumandigitaltwins AT patriciacondecespedes surveyonmachinelearningapproachesforpersonalizedcoachingwithhumandigitaltwins AT talkobdijkhuis surveyonmachinelearningapproachesforpersonalizedcoachingwithhumandigitaltwins AT hilbrandkeoldenhuis surveyonmachinelearningapproachesforpersonalizedcoachingwithhumandigitaltwins AT mariatrocan surveyonmachinelearningapproachesforpersonalizedcoachingwithhumandigitaltwins |