# A Survey on Machine Learning Approaches for Personalized Coaching with Human Digital Twins
## 5 Principal Results
### Findings on Human Digital Twin Dataset Considerations
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Category** & \multicolumn{2}{c}{**Individuals**} & **Features** & **Frequency** & **Data Type** \\ \hline Anomaly Detection & [64] & 200 & Signal & Stream & Physiological \\  & [70] & N/A & N/A & Stream & Physiological+Behavior \\ \hline Classification & [21] & 48 & 4 & Periodical & Behavior \\  & [77] & 257 & 52 & Single & Physiological \\  & [48] & 400 & 25 & Single & Physiological \\  & [53] & 10 & N/A & Periodical & Behavior \\  & [42] & 10 & Image & Single & Physiological \\  & [11] & 11 & 22 & Periodical & Behavior+Context \\  & [56] & 48 & 3 & Stream & Physiological \\  & [76] & 48 & Signal & Stream & Physiological \\  & [73] & N/A & 8 & Single & Physiological+Behavior \\  & [65] & 17 & 94 & Stream & Behavior+Context \\ \hline Clustering & [49] & 2 to 500 & Signal & Stream & Physiological \\  & [75] & 656 & 51 & Single & Physiological \\ \hline Clustering and Classification & [74] & 297 & 16 & Single & Physiological \\  & [78] & 43 & 4 & Periodical & Behavior \\ \hline Regression & [68] & 10 & 5 & Periodical & Physiological+Behavior \\  & [66] & 116 & 11 & Single & Physiological+Behavior \\ \hline Reinforcement Learning & [72] & 100 & N/A & Periodical & Behavior \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Application objective and dataset characteristics. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Category** & \multicolumn{2}{c}{**Publications**} \\ \hline Dataset considerations & General & [2; 9; 11; 84; 85; 86; 87; 88; 89; 90; 91; 92; 93; 94] \\ \cline{2-3}  & Feature evaluation & [70; 85; 86; 87; 89; 90; 91] \\ \cline{2-3}  & Meta-learning & [61; 62; 78; 92; 93; 94] \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Articles found on dataset considerations. feature selection an essential part of improving the efficiency and accuracy of the learning task. In the article by [PERSON] et al., one of the suggestions made for further research on algorithm improvement is to investigate the implementation of deep learning models such as DNN, CNN, Autoencoder, RBM, RNN, and independent component analysis algorithms for automatic feature extraction and classification. Feature selection is a well-studied field in the context of data science, particularly when analyzing large datasets with a substantial number of features. In our survey, we found that feature selection is considered crucial to making the learning process more efficient and precise. It can be challenging to determine the most suitable algorithm from the many available and _\"often relies on the expertise of a human or a random trial-and-error approach\"_[86]. In the context of HDTs, the challenge often lies in the fact that the number of features is relatively large compared to the number of samples. In [87], an approach is proposed to achieve meaningful information for feature selection in this situation. When the sample size is larger, one of the techniques used is meta-learning, where the characteristics of the dataset are used as input for selection processes [94]. Most often, meta-learning is employed in preprocessing and feature selection, as well as in algorithm selection, particularly when addressing classification problems. In [93], a method is proposed to apply meta-learning to recommend the most suitable classification algorithm for a given situation. In this method, both structural and statistical information are used to identify the nearest neighbors within a labeled set of datasets. Based on these nearest neighbors, the appropriate classification algorithm is recommended. The results of this study demonstrate that meta-learning can be effective in selecting classification algorithms. However, in our research, we have not found an application of meta-learning in the context of HDTs. In our recent work [78], we discuss the possibility of clustering-based meta-learning for the VFC platform as a first step to explore the opportunities that meta-learning offers in the context of HDTs.