## 4 Discussion  
This study deals with the impact of sensor placement and multimodal sensor fusion on the performance of an LSTM-based model for activity classification. The investigated activities are related to a collaborative human-robot scenario in which a UGV follows workers during the harvesting process to transport full crates out of the field, as presented in [9,12]. Overall, by optimizing key hyper-parameters, the model achieved high accuracy in classifying activities. In terms of sensor placement, the results suggest the chest as the most effective anatomical location for capturing the necessary data for activity classification, achieving the highest F1-score of 0.939. Its stable position on the torso ensures consistent and accurate \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Accelerometer** & **Gyroscope** & **Magnetometer** & **F1-Score** \\ \hline \(\surd\) & & & 0.554 \\  & \(\surd\) & & 0.637 \\ \(\surd\) & \(\surd\) & & 0.774 \\ \(\surd\) & & \(\surd\) & 0.887 \\  & \(\surd\) & \(\surd\) & 0.915 \\ \(\surd\) & \(\surd\) & \(\surd\) & **0.939** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Multimodal sensor fusion, considering only the IMU at the chest, and corresponding F1-scores. Figure 5: Training and validation plot loss, considering only the IMU at the chest. data gathering, principally for movements involving the upper body. Moreover, the chest is less prone to interference compared to the wrists, for example, making it ideal for reliable HAR associated with the present experimental setup. Combinations of the chest with the lumbar region or cervix also performed well, although they did not surpass the single chest sensor's F1-score. Conversely, the inclusion of wrist-mounted sensors, particularly on the left wrist, generally resulted in lower F1-scores, suggesting that these placements are less effective for the tasks considered in this study. This can be attributed to dominance and usage patterns, since 90% of the participants had the right hand as dominant, leading to more intense and frequent movements on the right side [50; 51]. In contrast, the left wrist, being less dominant, may experience less consistent movement patterns, resulting in less reliable data. Multimodal sensor fusion demonstrated the value of combining data from different sensor types to enhance classification accuracy. Specifically, the magnetometer outperformed the accelerometer and gyroscope, emphasizing its ability to capture critical orientation and motion information that the other sensors might miss. However, the highest performance was achieved by fusing data from all three sensors. This all-inclusive approach exploited the unique strengths of each sensor; the accelerometer's measurement of linear acceleration, the gyroscope's detection of rotational movements, and the magnetometer's ability to sense changes in orientation or location. Hence, by integrating these diverse data sources, the LSTM model benefited from a more complete representation of the activities, leading to improved HAR. Given the promising results achieved in this study, future research could incorporate a more diverse dataset with a wider range of agricultural material handling activities and participant demographics that have the potential to improve generalizability and robustness of the present LSTM model. Also, investigating other sensor types, including electromyography (EMG) and electrocardiogram (ECG) for measuring muscle response and heart rate variability, respectively, could provide complementary information and potentially improve classification accuracy for specific tasks. Experimenting with different combinations of sensor placements, like legs, shoulders and elbows, might identify other most effective combinations for various use cases, leveraging both the upper and lower body data points. It is also essential to conduct real-world agricultural collaborative robotics experiments and collect feedback from workers to ensure that these systems meet practical needs and address any usability issues.