# Training Performance Indications for Amateur Athletes Based on Nutrition and Activity Lifelogs
## 3 Experimental Results
### Evaluations  
Table 4 shows that our model produces accuracy with a low MAE (<0.5) and MSE (<0.4) in all groups (also compared to other often-used methods later in the article). Among models, the stacking model gives the best accuracy. Interestingly, we can see that the food images significantly strengthened the accuracy level, even though only three people took food pictures. Figure 4: The data-driven stacking-based Model: an overview. There is an exciting observation of group \"AAA_group_image_food\" where the stacking model generates a better accuracy for the \"weight\" group than the \"speed\" group. This observation strengthens the role of nutrition in training performance: if you have a good nutrition regimen (both in healthy qualifications and regular meals eaten), you can improve your training performance. Another important and interesting discovery is that though people record the calories they took during the day, the food images they took are more informative. From the observations mentioned, we discovered the following interesting facts: (1) to predict running speed change, we should focus on a group's common features rather than individual features; (2) to predict weight change, we must consider both common and individual features; and (3) to achieve high accuracy for each person, we have to use both common and individual features. The final interesting observation is that our model can work well with different groups (i.e., using the entire data cohort or particular group data, the model still predicts well). Hence, our model can work independently with subjective clustering defined by individual people. This discovery is beneficial when applying our model to a new cohort because it means one does not need to worry about how to divide the data into suitable groups. This validates the idea that our feature selection approach can alleviate the problem of data grouping. Moreover, it points out that exercise outcomes are associated with daily habits, not people's demographic characteristics. \begin{table}
\begin{tabular}{l l} \hline \hline
**Name of Group** & **Person in Group** \\ \hline Weight\_Group\_Image\_food & p01, p03, p05 \\ \hline Weight\_all\_Female & p04, p10, p11 \\ \hline Weight\_all\_Male & p01, p02, p03, p05, p06, p07, p08, p09, p12, p13, \\ \hline Weight\_Group\_A & p01, p02, p03, p04, p05, p11, p12, p13, p14 \\ \hline Weight\_Group\_B & p06, p07, p08, p09, p10, p16 \\ \hline Weight\_Group\_Age\_20\_40\_Male & p03, p05, p06, p07, p08, p09, p12, p13, p16 \\ \hline Weight\_Group\_Age\_20\_40\_Male\_and\_Female & p03, p04, p05, p07, p08, p09, p10, p11, p12, p13, \\ \hline Weight\_Group\_Age\_41\_60 Male\_and\_Female & p01, p02, p06, p14 \\ \hline Weight\_all\_people & p01, p02, p03, p04, p05, p06, p07, p08, p09, p10, \\ \hline Speed\_Group\_Image\_food & p01, p03, p05 \\ \hline Speed\_all\_Female & p04, p10, p11 \\ \hline Speed\_all\_Male & p01, p02, p03, p05, p06, p07, p08, p09, p12, p13, \\ \hline Speed\_Group\_A & p01, p02, p03, p04, p05, p11, p12, p13, p14 \\ \hline Speed\_Group\_B & p06, p07, p08, p09, p10 \\ \hline Speed\_Group\_Age\_20\_40\_Male & p03, p05, p06, p07, p08, p09, p12, p13 \\ \hline Speed\_Group\_Age\_20\_40\_Male\_and\_Female & p03, p04, p05, p07, p08, p09, p10, p11, p12, p13 \\ \hline Speed\_Group\_Age\_41\_60 Male\_and\_Female & p01, p02, p06, p14 \\ \hline Speed\_all\_people & p01, p02, p03, p04, p05, p06, p07, p08, p09, p10, \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Data grouping. Table 5 denotes some interesting patterns mined from the data of participant P03. We can consider this person as not active looking at the frequency of \"not exercise\" activities, which occupy a large portion of the data. Further, this person has a habit of drinking a lot of water regularly. In addition, this person has the habit of eating fast food (not in large portions, but regularly). The prediction results of a change in running speed and weight for this person are accurate, forecasting a weight increase and slower running speed.