# A Survey on Machine Learning Approaches for Personalized Coaching with Human Digital Twins
## 5 Principal Results
### Findings on Machine Learning Algorithm Selection in Human Digital Twins  
In Table 5, a list of the relevant articles found for questions 2 and 3 can be found. A total of 34 articles were found that discuss HDTs or aspects of personalized systems. Seven articles were found that addressed performance issues for HDTs. The articles listed in the general subcategory have been discussed in the previous sections. For the articles in the other three subcategories, an overview of the algorithms discussed, the datasets used, and the most relevant findings can be found in Tables A1-A4 in Appendix A. As described in Section 3, HDTs generally consist of several components or layers. In Figure 2, we can see that ML solutions can be implemented in most of these layers. The choices for the data acquisition or storage layer will, in general, be made by examining the options provided by algorithms suited for the data category. For the user-interaction layer, the most appropriate approach is found in the solutions supplied by RL-type algorithms. The implementation of the computation component is determined by the kind of modeling used in HDTs, the modeling of the human body or organ, or the modeling of behavior [12], but also by the objective of the computation component, as defined in Table 4.  
The focus in this Section, and the next one, lies on the use of solution-type algorithms in HDTs. Section 5.4 will focus on the data-type algorithms in HDT implementations. In Table 6, several examples of HDT or healthcare applications and their corresponding problem types are given. In [11], a team of HDTs is used to predict the physical condition of participants during training for a team of athletes. Their work illustrates the challenges encountered when dealing with human data, specifically missing data, and much attention is therefore paid to data imputation. In [56; 64; 66], examples of human body or organ twinning are given. As is often the case, training data is usually based on patient databases and is therefore less personalized. As can be seen from the examples in this table, all types of problems within the solution category find applications in HDTs or healthcare settings. Clustering examples, in general, involve USL models applied to data obtained from large groups of patients, used to identify patient groups. These models have traditionally not been used in personalized HDT applications; however, in [50], [PERSON] et al. provide an \begin{table}
\begin{tabular}{l l} \hline \hline
**Type** & **Example** \\ \hline \multirow{4}{*}{Classification} & Condition score in fitness management [11], emotion recognition in healthcare [42; 65], user behavior modeling [53], disease classification [48; 77], diagnose heart disease and detect heart problems [56; 73; 76], coaching applications [21] \\ \hline \multirow{2}{*}{Anomaly Detection} & Ischemic heart diseases and stroke detection [64], anomalous behavior detection for elderly care [70] \\ \hline \multirow{2}{*}{Regression} & Diagnosis and progression of cancer [66], metabolism models [68] \\ \hline \multirow{2}{*}{Reinforcement Learning} & Personalized medicine prescription [28], personalized health interventions [72], coaching applications [21] \\ \hline \multirow{2}{*}{Clustering} & Osteoarthritis endotype discovery [74], identifying biomarkers of lung health [75], unsupervised ECG analysis [49], personalized coaching applications [78] \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Problem types from the solution categories and examples in HDTs. \begin{table}
\begin{tabular}{c c} \hline \hline
**Category** & **Publications** \\ \hline Machine Learning and & general & [4; 5; 6; 12; 13; 14; 16; 22; 24; 35; 36; 38] \\ \cline{2-3}  & single case example & [11; 42; 64; 65; 66; 67] \\ \cline{2-3}  & personalization & [21; 23; 28; 37; 50; 53; 68; 69; 70; 71; 72; 73; 74; 75] \\ \cline{2-3}  & performance & [15; 28; 48; 76; 77; 78; 15; 76; 77; 78] \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Articles found on personalization and Human Digital Twins.