# A Survey on Machine Learning Approaches for Personalized Coaching with Human Digital Twins
## 5 Principal Results
### Findings on Human Digital Twin Performance Evaluation  
Because humans are generally an active part of the system as patients or participants, one of the concerns that arises is that trust and motivation to participate play a crucial role in the data collection process, as well as in how output is received and responded to [15].  
Motivational and trust issues can arise when the user experience is not optimal. The quality of this experience is determined by the choices made in the HDT's user interface layer. A good example of a survey with a more specific focus on ML implementations in this context is the work by [PERSON] et al. [28]. Among other things, they concluded that further comparison of RL algorithms is needed and that the development of standardized personalization datasets and simulation environments is a promising direction for future research.  
Another concern with personalized healthcare that must be taken into account is that, unlike industrial applications of DT, the physical twin itself is a cognitive being that interacts dynamically with its environment. The object of the DT is itself a learning subject, changing its behavioral patterns over time, which can lead to model or data drift. This means that adaptability and flexibility are essential considerations when designing and implementing HDTs.  
For both of these concerns, evaluating the algorithm's performance can be part of the mitigation actions. This will enable the improvement of the accuracy of suggested interventions, calculated predictions, or the provision of more accurate feedback. Several studies were found that discussed the methods and results of such evaluations. A good example can be found in [68], where the implementation of a personalized metabolic avatar is described. In this work, the performance and computational time of four different models--SARIMAX, GRU, LSTM, and Transformer--were evaluated. Based on the experiment's results, a candidate for implementation in the production environment is suggested. An interesting point that they made in their discussion of the results is that some of the models exhibited significant variability among users. A related issue that could not be addressed in their study, due to the limited number of participants, was whether it would be more effective to have different models for different groups within the population or to train one model for all data.  
In Table 8, an overview is given of the evaluation metric applied in the discussed articles. As can be seen from these results, only three articles discuss computational or execution time. In [48; 68], the authors look at the performance of the selected algorithms measured at execution time, but no significant conclusions are drawn. In [78], the difference in model generation time between a clustered and a personalized approach is evaluated, and a substantial reduction in computational time is obtained.  
Several studies apply clustering or grouping techniques, and in general, the results from this approach are positive. For example, in [72], the authors state _A cluster-based RL can learn a significantly better policy within 100 days compared to learning per user and learning across all users, provided that a suitable clustering is found_.  
These observations, along with the findings in the previous section, are consistent with the results of other studies identified in our survey. Little attention is paid to performance evaluation at the individual level, and the results obtained are generally based on calculations made on the entire dataset. In some cases, training or evaluation was based on data from a single participant; however, in all cases, it remains unclear whether different results could be obtained by examining individual subjects. No truly personalized approach to the evaluation of the ML algorithm can be found, but when clustering approaches are applied, promising results are obtained.