## 4 Experiments and Results
### Experimental Results of Position-Dependent HAR  
We evaluated the proposed methodology for various on-body positions in this particular instance. Evaluating position-dependent HAR was paramount. The relevant tables, specifically Tables 2-4, present the F1-scores and accuracies of the Att-ResBiGRU model as suggested. The on-body positions from the three HAR datasets--Opportunity, PAMAP2, and REALWORLD16--are presented in these tables. The findings presented in Table 2 enable the following significant observations to be drawn:  
* The model demonstrated commendable effectiveness when identifying position-dependent human activities on the Opportunity dataset. Accuracy scores spanned from 84.76% to 89.06% across the five sensor positions. * The F1-score of 85.69% and accuracy of 89.06% for the back position indicate that it produced the most valuable signals for activity recognition on the Opportunity dataset. * The effectiveness of the left lower arm (LLA) position was the lowest, with an accuracy of 84.76% and an F1-score of 79.84%. This finding implies that the left lower arm motion may not be as indicative of the actions designated in the Opportunity dataset. * The sensor's efficacy generally declined as the position transitioned from the upper body (e.g., back, right upper arm) to the lower body (e.g., left upper arm, left lower arm). This is consistent with the hypothesis that signals from the upper body are more discriminatory for numerous prevalent activities. * The LLA position exhibited the smallest standard deviation in performance variation between trials, with approximately 0.7% for accuracy and 1.2% for the F1-score. Other positions showed a more significant variance, indicating greater inconsistency between studies. The outcomes illustrate the model's capacity to attain commendable performance in the HAR domain, with the performance varying according to the sensor positions. Significantly, variations in the efficacy of each sensor position for activity recognition can be observed. The most informative signals were obtained from the back and upper-arm positions in the provided dataset. The following are the principal findings presented in Table 3:  
* The model attained exceptionally high F1-scores and accuracies across all three sensor positions on the PAMAP2 dataset, with the accuracy varying from 97.11% to 97.59%. * The torso position exhibited superior performance, achieving an accuracy of 97.59% and an F1-score of 97.46%. This finding implies that the signals produced by upper-body motion were the most valuable for activity recognition in this dataset. * The effectiveness of the hand (97.12% accuracy) and ankle (97.25% accuracy) positions was comparable, indicating similar information provided by wrist and ankle movements for action recognition on PAMAP2. * The mean values of accuracy and F1-score standard deviations for each trial were below 0.3% and 0.26%, respectively, indicating that the model's effectiveness remained consistent across numerous iterations. * All loss values were below 0.13, indicating that the model accurately represented the training data. The Att-ResBiGRU model consistently exhibited outstanding HAR performance, particularly across different positions, when evaluated on the PAMAP2 dataset. The thoracic position yielded the most critical signals. The model's robust generalization, observed across many test trials, was substantiated by the minimal deviations. The key conclusions from the results presented in Table 4 are as follows:  
* The model achieved high F1-score ratings and accuracies ranging from 94.74% to 98.59% across all seven sensor positions on the REALWORLD16 dataset. Notably, the F1-score was 98.52%, and the quadriceps position achieved the highest accuracy of 98.59%, suggesting that thigh motion provided the most valuable signals for activity recognition in this dataset.