# A Survey on Machine Learning Approaches for Personalized Coaching with Human Digital Twins
## 5 Principal Results
### Findings on Human Digital Twin Dataset Considerations  
A significant concern in HDT implementations is the quality and quantity of data. In Table 9, the 23 articles that discussed dataset considerations can be found. A recurring theme in the studies we examined is the issue of missing data. For example, in [11], a study on improving data quality by data imputation can be found. In the work of [PERSON] et al. [2], when discussing the challenges in obtaining useful data, it is stated that _\"It needs to be quality data that are noise-free with a constant, uninterrupted data stream.\"_ Since in HDTs some of the data, such as daily surveys, depends on the consistency of the participants, such quality requirements are challenging to meet [79]. Some of these concerns are also highlighted in the survey by [PERSON] et al. [9] on the datasets used in human behavior analysis. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Category** & \multicolumn{2}{c}{**Evaluation Metric**} \\ \hline Anomaly Detection & [64] & Accuracy. \\  & [70] & Accuracy, precision, recall and F-scores. \\ \hline Classification & [21] & Accuracy and F1-score of classification using generalized or personal models. \\  & [77] & Confusion matrix. \\  & [48] & Accuracy, precision, recall, F-measures, and execution time. \\  & [53] & Precision, recall, F-measures, kappa, CCI, ICI, ROC, MAE, and RMSE. \\  & [42] & Accuracy. \\  & [11] & Classification loss. \\  & [56] & Accuracy, sensitivity, specificity, precision, AUC. \\  & [76] & Most metrics applicable to classification. \\  & [73] & F1-score, AUC and accuracy. \\  & [65] & Accuracy, precision, recall and F1-score. \\ \hline Clustering & [49] & Accuracy. \\  & [75] & N/A \\ \hline Clustering and & [74] & Cluster stability by expanding the number of clusters from 3 to 5. \\ Classification & [78] & Computational time, accuracy and F1-score. \\ \hline Regression & [68] & RMSE and computational time. \\  & [66] & MSE, RMSE, MAE and R2. \\ \hline Reinforcement Learning & [72] & Cumulative reward. \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Application objective and evaluation metric. By examining the frequencies and data types of the applications listed, we can observe that, in general, DTs that focus on disease detection and diagnosis utilize physiological data, such as MRI scans (single observations) or ECG data (streams). DTs that perform coaching tasks rely on behavioral data such as activity data or step data, which can be complemented by context or physiological data. By examining the number of patients or participants, we observe that in most cases, the number of individuals in each dataset is limited. However, the number of records per individual can be quite extensive. In general, the number of individuals and the resulting data quantity are not considered problematic; however, some articles suggest that the results could be different or improved if more individual datasets were available [53; 68]. Data quality is discussed in most articles, but only [11] provides a detailed discussion on the measures taken to improve data quality. Another problem with human data, identified by [PERSON] et al. [70], is caused by confounding variables and activities. In some cases, activities that are very different can yield similar data readings from sensors, such as smartwatches or IoT devices. This is called confounding activities. Confounding variables arise when an unmeasured variable influences several others, creating apparent causal connections. These phenomena make