# A Survey on Machine Learning Approaches for Personalized Coaching with Human Digital Twins
## Abbreviations  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} DT & Digital Twin \\ HDT & Human Digital Twin \\ AI & Artificial Intelligence \\ ML & Machine Learning \\ IoT & Internet of Things \\ VFC & Virtual Fitness Coach \\ SL & Supervised Learning \\ USL & Unsupervised Learning \\ SSL & Semi-Supervised Learning \\ RL & Reinforcement Learning \\ AUC & Area under the curve \\ CCI & correctly classified instances \\ ICI & incorrectly classified instances \\ RMSE & Root Mean Square Error \\ MSE & Mean Squared Error \\ MAE & Mean Absolute Error \\ ROC & Receiver Operating Characteristics \\ R2 & Coefficient of Determination \\ \end{tabular}  
\begin{table}
\begin{tabular}{c l l l} \hline
**Publication Date** & **Dataset Type Dataset Size** & **Algorithms Evaluation Method/Metric** & **Results** \\ \hline  & Physical information such as gender, & & \\  & blood pressure, and cholesterol was used & & \\  & with the RF classifier. Facial image data & & \\  & was used with K-Means and 2 CNN & & \\  & 2019-12 & & \\  & & & \\  & & 10 participants. & & \\ \hline  & & Various datasets concerning anomalous & \\  & behavior detection for elderly care. & & \\  & Various. & & \\  & & & \\ \hline  & & 22 features, containing athletes’ activity, & \\  & mood, and  
\begin{table}
\begin{tabular}{c l l l} \hline \hline
**Publication Date** & **Dataset Type Dataset Size** & **Algorithms Evaluation Method/Metric** & **Results** \\ \hline \multirow{4}{*}{[56] 2021-12} & \multirow{4}{*}{\
\begin{tabular}{} \end{tabular} } & Electroncephalography, one channel & \multirow{4}{*}{
\begin{tabular}{} \end{tabular} } \\  & vertical electro-culogram and one & & & \\  & channel chin electro-myogram. & & & \\ \cline{1  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Publication Date** & **Dataset Type Dataset Size** & **Algorithms Evaluation Method/Metric** & **Results** \\ \hline
[23] & N/A & N/A & This study provides key design features and an architectural framework to implement HDTs. It also highlights some technical challenges. \\ \hline
[75] & 51 plasma EV-miRNAs.s. & K-Means. & In this study, clustering is used to identify lung health. The focus lies on the usability of the given dataset to achieve this goal, but it does not expand on the ML techniques used. \\ \cline{2-3}
[68] & Weight, activity and diet. & SARIMAX, LSTM, GRU, Transformer. & Based on computational time and the RMSE results, this study concludes that GRU or LSTM is best to be used in a production environment. \\ \hline
[66] &
\begin{tabular}{c} Blood samples, demographic, \\ anthropometric, and clinical data. \\ \end{tabular} & LR, DTC, RFR, GBA. & Although the results for the individual models are presented by the authors, rather than selecting one optimal algorithm, the approach in this article is to combine the results of the four different models. \\ \hline
[65] & \
\begin{tabular}{c} Facial imaging and body movement \\ data. \\ \end{tabular} &
\begin{tabular}{c} Bagged trees, KNN, SVM (linear and cubic). \\ \end{tabular} & The authors perform an in-depth analysis of the data structure and qualities of the models and conclude _Due to the nature of the data, the non-linear algorithms produced consistent findings. All the classification methods consistently performed under the Begged Trees and k-NN_. \\ \hline
[78] &
\begin{tabular}{c} Step data. \\ \end{tabular} & Manual clustering and RF. & This study demonstrates that a clustering approach to personalization is a viable concept that can reduce computational time without significant loss of accuracy. \\ \cline{2-3}
[50] & N/A & Several clustering, dimensionality reduction, and anomaly detection techniques. \\ \hline \hline \end{tabular}
\end{table}
Table 4: A chronological overview of the articles found on algorithm selection and performance evaluation for HDTs.