# A Survey on Machine Learning Approaches for Personalized Coaching with Human Digital Twins
## 6 Conclusions, Perspectives, and Discussion  
We discussed that one of the significant advantages of HDT is its ability to provide personalized support in healthcare. Creating efficient, personalized healthcare through the use of HDTs presents several challenges. Data quality and quantity can be an issue, as can noise and confounding variables. Furthermore, considering that the physical twin is a cognitive entity that adapts to its environment and the Digital Twin's output demands constant monitoring of the accuracy of its virtual counterpart.  
As can be seen from a comparison between the findings on ML and HDT and the findings on datasets, there is a sharp contrast between the approach taken to feature selection and, in a lesser way, to algorithm selection in the field of data science, compared to the way selection processes are conducted for HDT applications. Although studies have been conducted for nearly two decades on the subject of meta-learning using dataset characteristics, there seems to be a total absence of its application in HDTs. Since data quality and algorithm performance are crucial factors in the success of HDTs, and in many cases, regular model updates are necessary to compensate for drift, we identify opportunities for improvement in this area.  
Furthermore, we have established that there is a lack of knowledge regarding the selection and evaluation of personalized models. The most common approach is to use generalized models, and there are no truly personalized assessment methods. This is essential, considering individual adaptation, to keep the virtual object optimally adapted for the physical object in the HDT configuration.  
_Further Research_  
Based on the discussion in the previous section, we suggest the following research question for further 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?_  
The focus here should lie on investigating the challenges that arise from the fact that each human, and thus each physical twin in the Digital Twin system, is a unique, evolving individual, requiring continuous personalized adaptation of the ML components of the HDT application. By answering this question, more insight should be gained for ways to improve HDT's accuracy. Higher precision is essential for better diagnosis results, used, for example, for precision medicine. Furthermore, improved behavior prediction, utilized in a wide range of coaching and supervision applications, will enhance the trust and motivation of patients or participants using these applications.  
Conceptualization, H.H.R., P.C.-C. and M.T.; methodology, H.H.R. and P.C.-C.; software, H.H.R.; validation, P.C.-C., T.B.D., H.K.E.O. and M.T.; formal analysis, H.H.R., T.B.D. and P.C.-C.; investigation, H.H.R.; resources, T.B.D.; data curation, H.H.R. and T.B.D.; writing--original draft preparation, H.H.R.; writing--review and editing, P.C.-C., T.B.D., H.K.E.O. and M.T.; visualization, H.H.R.; supervision, P.C.-C., H.K.E.O. and M.T.; project administration, H.K.E.O. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflicts of interest.