## 3 Results
### Sensor Placement Combinations  
Given the present set of five anatomical locations (cervix, chest, lumbar region, right wrist, and left wrist), the total number of combinations is:  
\[\mathrm{C}(5,1)+\mathrm{C}(5,2)+\mathrm{C}(5,3)+\mathrm{C}(5,4)+\mathrm{C}(5,5 )=31, \tag{2}\]  
where \(\mathrm{C}(n,k)=\frac{n!}{k!(n-k)! }\), with \(n\) being the total number of anatomical locations (5 in the present analysis), \(k\) is the number of anatomical locations to choose, and! denotes factorial, namely the product of all positive integers up to that number. Table 1 presents the F1-scores corresponding to different combinations of sensor placements on various anatomical locations, by considering the data fusion of all sensors that IMUs include; accelerometer, gyroscope, and magnetometer. Interestingly, the highest F1-score of 0.939 is achieved with a single IMU placed on the chest, indicating this location's superior effectiveness in capturing the necessary data for the present activities. However, the cervix and lumbar region are nearly as effective as the chest for sensor placement, potentially offering practical alternatives. In contrast, the wrist-mounted sensors, especially the left wrist, underperform compared to the chest-mounted sensor, suggesting that they are not as effective in capturing the relevant activities. The wrists' greater variability in movement patterns contributes to the difficulty of accurate activity recognition. Unlike the torso, which is directly involved in most activities, the wrists have a more indirect role, leading to inconsistent data and noise. This makes it challenging to identify clear patterns, as wrist movements can sometimes resemble those of other activities or involve irrelevant motions, such as arm swinging during standing or walking without a crate. Besides, activities like carrying a crate introduce further complexity through varying wrist orientations and different carrying techniques, adding task-specific variability that complicates classification. For the two anatomical location combinations, the F1-scores tend to be slightly lower than the highest score achieved with the single chest IMU. The combination of the chest with lumbar region and cervix yields the highest F1-scores of 0.932 and 0.931, respectively, among the dual placements, indicating that these sites are effective in capturing reliable data. Conversely, combinations involving the left wrist generally lead to lower F1-scores, such as right wrist and left wrist at 0.898. Concerning the three anatomical location combinations, the F1-scores demonstrate varied effectiveness, with some combinations achieving results close to those seen with two sensors. The combination of cervix, chest, and lumbar region yields the highest F1  
Figure 4: Effect of batch size on model performance. score of 0.931. However, not all three-IMU setups enhance data accuracy, and some may even introduce redundancy or noise, reducing overall performance. For the four anatomical location combinations, the F1-scores reflect a slight decline compared to some of the three-location setups. The combination of chest, cervix, lumbar region, and right wrist achieves the highest F1-score of 0.922 among the four-location configurations, suggesting that this setup captures the examined activities sufficiently. These results also indicate diminishing returns with the inclusion of the left wrist, as expected. Finally, the combination of all five anatomical locations provides an F1-score equal to 0.908, which is 3.4% lower than the corresponding metric of the single sensor positioned on the chest. Consequently, the inclusion of all five sensors seems to introduce unnecessary complexity, which can lead to a decrease in the effectiveness of the LSTM model. This reinforces the idea that strategic selection of fewer, more impactful sensor locations can be more beneficial than using many locations.