## 3 Results
### Multimodal Sensor Fusion  
As proved in the previous section, the IMU placed on the chest was the most effective sensor location for the LSTM model. Its superior performance is attributed to its strategic location on the torso, which provides stable, consistent data relevant to many upper-body activities. Hence, its central placement helps in accurately capturing core movements and reducing interference from other body parts, resulting in better performance in activity classification. Focusing on this case consideration, a characteristic plot of the training and validation loss decreasing over the epochs is depicted in Figure 5. The very low generalization gap, evidenced by the close alignment of training and validation loss curves, indicates that the model is performing well both on the training data and on unseen validation data. Furthermore, the smooth and decreasing curves demonstrate that the model is learning effectively and making consistent progress in minimizing loss over time. As detailed in Section 2.1, this study considers signals acquired from accelerometer, gyroscope, and magnetometer sensors. These sensors are all integral components of the IMUs utilized in this research. Sensor fusion was applied towards combining results for multimodal data similarly to studies like [49]. For the evaluation, F1-score was used in \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multicolumn{2}{c}{} & \multicolumn{1}{c}{\multirow{2}{*}{**Confusion Matrix**}} & \multicolumn{6}{c}{**Predicted Label**} \\ \cline{3-8} \multicolumn{2}{c}{} & **Standing** & **Walking (without Crate)** & **Bending** & **Lifting Crate** & **Walking (with Crate)** & **Placing Crate** \\ \hline \multirow{4}{*}{**LSTM**} & Standing & **4456** & 497 & 3 & 3 & 0 & 0 \\ \cline{2-8}  & Walking (without crate) & 548 & **14,685** & 303 & 59 & 42 & 2 \\ \cline{2-8}  & Bending & 5 & 524 & **4385** & 350 & 31 & 0 \\ \cline{2-8}  & Lifting crate & 3 & 111 & 366 & **5950** & 425 & 0 \\ \cline{2-8}  & Walking (with crate) & 0 & 37 & 20 & 260 & **15,280** & 394 \\ \cline{2-8}  & Placing crate & 2 & 18 & 1 & 2 & 767 & **2979** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Confusion matrix of the proposed LSTM network by considering all the anatomical locations. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multicolumn{2}{c}{} & \multicolumn{1}{c}{\multirow{2}{*}{**Confusion Matrix**}} & \multicolumn{6}{c}{**Predicted Label**} \\ \cline{3-8} \multicolumn{2}{c}{} & **Standing** & **Walking (without Crate)** & **Bending** & **Lifting Crate** & **Walking (with Crate)** & **Placing Crate** \\ \hline \multirow{4}{*}{**LSTM**} & Standing & **934** & 60 & 0 & 0 & 0 & 0 \\ \cline{2-8}  & Walking (without crate) & 127 & **2947** & 39 & 4 & 0 & 0 \\ \cline{2-8}  & Bending & 0 & 33 & **1043** & 43 & 0 & 0 \\ \cline{2-8}  & Lifting crate & 0 & 4 & 46 & **1238** & 48 & 0 \\ \cline{2-8}  & Walking (with crate) & 0 & 0 & 0 & 23 & **3064** & 83 \\ \cline{2-8}  & Placing crate & 0 & 0 & 0 & 0 & 126 & **640** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Confusion matrix of the proposed LSTM network by considering only the IMU at the chest. order to guarantee the fairness and consistency of the subsequent comparisons, as it is a useful performance indicator for imbalanced classes like the present one [24,48]. In addition, only the IMU at the chest was considered in this section, as it proved to yield the best model performance compared with the other body positions. First, the overall performance of single sensor modality was assessed for predicting the investigated activities. As depicted in Table 4, the magnetometer consistently outperforms the accelerometer and gyroscope in terms of F1-score, by showing an F1-score equal to 0.783. The lowest performance was achieved by taking into account only the accelerometers. Combining data from multiple sensors proved to lead to improved performance compared to using a single sensor, stressing the value of multimodal sensor fusion in enhancing classification accuracy.