# Time of Flight Distance Sensor-Based Construction Equipment Activity Detection Method
## 4 Results and Discussion
### Comparison with Other Models  
In this study, the results are compared with the activity classification models in recent research (Table 4). The comparative analysis has consolidated the findings from studies based on image data and sensor-based research in comparison with the proposed model. The data used for verification in the vision-based model [7] consisted of 351 video clips with a resolution of \(1280\times 720\) pixels, and these were collected from 21 different construction sites, considering the site conditions, equipment viewpoints, and scales and colors of the excavators. The sensor-based model [12] utilized two three-axis accelerometers, one near the bucket and one on the excavator body, to collect six data points as a single \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**No.**} & \multirow{2}{*}{**Method**} & \multirow{2}{*}{**Category**} & \multirow{2}{*}{**Data Set**} & \multirow{2}{*}{**Activity**} & \multicolumn{2}{c}{**Performance Metrics**} \\ \cline{5-8}  & & & & **Precision** & **Recall** & **Accuracy** \\ \hline \multirow{4}{*}{1} & \multirow{4}{*}{[PERSON], et al. [7]} & \multirow{4}{*}{\begin{tabular}{c} Vision \\ (Image size: 1280 \(\times\) 720) \\ \end{tabular} } & \multirow{4}{*}{351} & Digging & 95 & 86 & \\  & & & Swinging & 86 & 93 & \\  & & & Loading & 84 & 80 & \\  & & & Average & 88 & 88 & 87.6 \\ \hline \multirow{4}{*}{2} & \multirow{4}{*}{\begin{tabular}{c} [PERSON], et al. [12] \\ (Acceleration sensor) \\ \end{tabular} } & \multirow{4}{*}{\
\begin{tabular}{c} Sensor \\ (Acceleration sensor) \\ \end{tabular} } & \multirow{4}{*}{242} & Idling & 100 & 81 & \\  & & & Traveling & 99 & 57 & \\  & & & Scooping & 73 & 96 & \\  & & & Cropping & 83 & 65 & \\  & & & Rotating (left) & 69 & 93 & \\  & & & Rotating (right) & 94 & 80 & \\  & & & Average & 85 & 83 & 83 \\ \hline \multirow{4}{*}{3} & \multirow{4}{*}{
\begin{tabular}{c} Proposed Model \\ (Distance sensor) \\ \end{tabular} } & \multirow{4}{*}{312} & Waiting & 100 & 100 & \\  & & & Scraping & 100 & 100 & \\  & & & Excavating (Under) & 82.7 & 89.6 & \\ \cline{1-1}  & & & Excavating (Front) & 96.2 & 86.2 & \\ \cline{1-1}  & & & Dumping & 96.2 & 98.0 & \\ \cline{1-1}  & & & Repositioning & 98.1 & 98.1 & \\ \cline{1-1}  & & & Average & 95.5 & 95.3 & 95.2 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Performance metrics comparison. Figure 8: Classification performance confusion matrix. vector. A total of 48,435 vectors were used for training. A vector recorded over two s at a rate of 100 Hz was treated as a single sample, and the number of such samples used in training was considered to be 242. In terms of accuracy, recall, and accuracy, the first model (vision model) and the second model (acceleration sensor model) showed higher measurements for the first model. On the other hand, the distance sensor model (proposed model) demonstrated superior performance, with all metrics exceeding 95%, compared to the other two models. In the comparison of individual activities, the vision model demonstrated a high precision rate at 95% in Digging and a high recall rate at 93% in Swinging. However, it exhibited lower performance in Loading, with a precision rate of 88% and a recall rate of 80%. The acceleration sensor model displayed high precision rates of 100%, 99%, and 94% in Idling, Traveling, and Rotating (right), respectively. However, it showed lower metrics in Traveling and Dropping, with precision rates of 57% and 65%, respectively. On the other hand, the distance sensor model shows a rate of precision and recall of above 90% in most metrics, excluding excavating. As can be seen in Figure 8, even the errors that indicate lower metrics are due to discrepancies between the 'Under' and 'Front' cases. It is expected that integrating these two metrics for experimentation as an activity would result in a lower error rate. In the comparison of average accuracy, the vision model exhibits a result of 87.6%, the acceleration sensor model shows 83%, and the distance sensor model demonstrates 95.2%.