## 4 Discussion
### Model Accuracy  
The most accurate models for both feelings of energy and fatigue were Gradient Boosting Classifier models. While both reported similar mean accuracy (74.34% for energy vs. 74.23% for fatigue), the lower confidence interval for feelings of energy suggests that the identification of individuals who were low energy versus individuals who were high energy may be more accurate. Additionally, the best classifiers for energy had 100% accuracy, suggesting that if the model gets \"lucky\", and the \"right\" training and test dataset is used, feelings of energy may be identified very accurately using walking gait. While identification of feelings of fatigue had a similar mean accuracy, the higher confidence intervals, and the lower accuracy of the best and worst models suggests that other features may need to be captured to create more accurate models to identify individuals who are low fatigue versus those who are high fatigue. The mean AUC ROC was >0.80 for both models, suggesting that the models have an 80% + chance of distinguishing between the two classes created for both moods. These findings are not comparable to other literature; however, literature that has tried to identify other subjective moods, such as feelings of anxiety and depression, using a similar protocol has reported similar accuracy rates [39, 40].  
While the accuracy of measuring the two moods separately was in line with literature from studies examining other subjective mood states using walking gait [39, 40], when classifying individuals using both moods simultaneously, the accuracy rates decline significantly (mean = 43.14%), and the AUC ROC shrinks (0.658) as well. The primary takeaways from these models are that: (1) there are individuals who may report being high energy and high fatigue (\(n\) = 33), and those who can be both low energy and low fatigue (\(n\) = 23), further supporting previously published findings [6, 7, 8, 9, 10]; (2) the number of individuals in each category may have been too small to create accurate machine learning models; (3) the AUC ROC for the best model (0.796) suggests that larger sample sizes may have yielded different results.  
When examining the accuracy of the regressor models, both Energy and Fatigue models had very low mean MAE (mean MAE Energy = 0.005, mean MAE Fatigue = 0.007), both using K-fold cross-validation and bootstrapping techniques. When examining the coefficient of determination of the models (R\({}^{2}\)), the mean R\({}^{2}\) for the models that used the bootstrap was high (mean R\({}^{2}\) Energy = 0.884, mean R\({}^{2}\) Fatigue = 0.886); however, when using K-fold cross-validation, the mean R\({}^{2}\) was reduced to 0.310 and 0.349 for Energy and Fatigue, respectively. A stratified K-fold with four strata was also used, but the R\({}^{2}\) was not very different. Taken together, the low MAE suggests that gait may be a good predictor of fatigue and energy [54]; however, the low R\({}^{2}\) in the K-fold models may be explained by the fact that there was a large variation in the data (scores ranging from 0 to 20), and a small sample size (\(n\) = 126), and thus the K-fold cross-validation reduced the R\({}^{2}\). However, when examining bootstrapped models, the higher R\({}^{2}\) suggest that gait might help us identify feelings of energy and fatigue in larger samples, where K-fold cross-validation would result in higher values of R\({}^{2}\). The bootstrapped findings may have overfit the models; however, with small sample sizes, these models are the most appropriate analyses [51]. The results from both sets of models provide useful evidence for future researchers interested in identifying feelings of energy and fatigue using walking gait.  
Together, the results of the classifier and regressor models support the use of walking gait to identify feelings of energy and fatigue using machine learning models.