# Training Performance Indications for Amateur Athletes Based on Nutrition and Activity Lifelogs
## 3 Experimental Results
### Evaluations
Together with the periodic-frequent patterns mined from this person's lifelogs, we can see the interesting correlation between nutrition, physical activities, and training performance. Based on that, the person could be recommended to adjust their food intake and activity level to achieve possible weight loss. This detailed example shows that our method can be used to provide recommendations based on the data and predictions, which could lead to possible health and performance benefits. \begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline \multirow{2}{*}{**Name of Group**} & \multicolumn{2}{c}{**General Model**} & \multicolumn{2}{c}{**Intersection Model**} & \multicolumn{2}{c}{**Personal Model**} & \multicolumn{2}{c}{**Stacking Model**} \\ \cline{2-7}  & **MAE** & **MSE** & **MAE** & **MSE** & **MAE** & **MSE** & **MAE** & **MSE** \\ \hline Weight\_group\_image\_food & 0.416 & 0.256 & 0.408 & 0.235 & 0.458 & 0.367 & 0.006 & 0.002 \\ \hline Weight\_all\_Female & 0.378 & 0.198 & 0.366 & 0.181 & 0.221 & 0.072 & 0.009 & 0.005 \\ \hline Weight\_all\_Male & 0.322 & 0.138 & 0.376 & 0.167 & 0.391 & 0.237 & 0.003 & 0.001 \\ \hline Weight\_Group\_A & 0.459 & 0.248 & 0.480 & 0.236 & 0.280 & 0.150 & 0.002 & 0.001 \\ \hline Weight\_Group\_B & 0.238 & 0.090 & 0.509 & 0.290 & 0.237 & 0.088 & 0.007 & 0.003 \\ \hline Weight\_Age\_20\_40\_Male & 0.371 & 0.169 & 0.443 & 0.205 & 0.225 & 0.075 & 0.005 & 0.001 \\ \hline Weight\_Age\_20\_40\_Male\_Female & 0.372 & 0.188 & 0.428 & 0.190 & 0.265 & 0.130 & 0.006 & 0.002 \\ \hline Weight\_Age\_41\_60\_Male\_Female & 0.270 & 0.127 & 0.293 & 0.122 & 0.203 & 0.061 & 0.000 & 0.000 \\ \hline Weight\_full\_version & 0.231 & 0.082 & 0.371 & 0.158 & 0.263 & 0.122 & 0.005 & 0.002 \\ \hline Speed\_group\_image\_food & 0.169 & 0.039 & 0.126 & 0.024 & 0.225 & 0.086 & 0.107 & 0.020 \\ \hline Speed\_all\_Female & 0.182 & 0.051 & 0.102 & 0.021 & 0.331 & 0.152 & 0.086 & 0.018 \\ \hline Speed\_all\_Male & 0.134 & 0.031 & 0.078 & 0.014 & 0.349 & 0.181 & 0.067 & 0.012 \\ \hline Speed\_Group\_A & 0.112 & 0.023 & 0.057 & 0.011 & 0.387 & 0.182 & 0.049 & 0.009 \\ \hline Speed\_Group\_B & 0.175 & 0.043 & 0.171 & 0.034 & 0.463 & 0.286 & 0.145 & 0.029 \\ \hline Speed\_Age\_20\_40\_Male & 0.150 & 0.037 & 0.073 & 0.012 & 0.454 & 0.247 & 0.062 & 0.010 \\ \hline Speed\_Age\_20\_40\_Male\_Female & 0.111 & 0.021 & 0.069 & 0.009 & 0.421 & 0.217 & 0.058 & 0.007 \\ \hline Speed\_Age\_41\_60\_Male\_Female & 0.151 & 0.038 & 0.253 & 0.077 & 0.322 & 0.153 & 0.128 & 0.033 \\ \hline Speed\_full\_version & 0.119 & 0.024 & 0.095 & 0.016 & 0.345 & 0.170 & 0.081 & 0.013 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Evaluation results of the data-driven stacking-based model. \begin{table}
\begin{tabular}{l c c} \hline \hline
**Patterns** & **Support (\%)** & **Periodicity (\%)** \\ \hline low\_moderately\_active\_minutes, & & \\ high\_sedentary\_minutes, & 0.99 & 2 \\ low\_very\_active\_minutes & & \\ \hline low\_glasses\_of\_fluid & 0.96 & 6 \\ \hline Yes\_Breakfast, Yes\_Dinner & 0.37 & 16 \\ \hline high\_Fast\_food & 0.14 & 24 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Periodic-frequent pattern mining results for participant P03.