# Time of Flight Distance Sensor-Based Construction Equipment Activity Detection Method
## 4 Results and Discussion
### Contributions and Limitations  
In this study, a new methodology based on a DCNN for detecting construction equipment activity using sensor data, as well as a data collection approach, is presented.  
We reveal a DCNN (deep convolutional neural network) methodology capable of detecting construction equipment activity information, including time series data, by utilizing distance sensors to store the displacement of three pistons as a single vector and treating the 200 accumulated vectors as one activity sample. In the experiments conducted on the one-twelfth scale model, this methodology demonstrated a higher level of accuracy compared to image-based and accelerometer-based methodologies. In the vision-based study [7], it was explained that detection errors in the sample cases occurred due to variations in the vision-based model's training data (video), such as shadows, the position of the observation camera, the posture of the subject under observation, and the lighting conditions at the time of recording. In the accelerometer-based study [35], it is mentioned that numerous experiments are required to obtain additional training data, making data expansion challenging and necessitating a large volume of data. This issue may arise due to significant variances in the values measured by the accelerometer, which can vary according to the excavator's size and shape, work speed, and the placement of the accelerometer. Therefore, it is expected that securing a diverse range of additional data is necessary.  
On the other hand, in the proposed method, data acquisition is facilitated by using a time of flight (ToF) distance sensor module. This module measures the distance by calculating the time it takes for light emitted at a specific frequency to bounce back from the target. Furthermore, the patterns learned from a specific excavator, based on the piston length ratio, can be applied to excavators with different specifications. If the current piston length ratio can be determined, the developed data collection method and learning model can be directly applied without modification. This advantage can overcome the problem that vision-based models face, which is the need to separately train excavators of different forms (such as color and size). Since the collected data represent solely the activity of the excavator, this fundamentally solves the issue of accuracy degradation caused by noise (all pixels, excluding the observed object) that is included in a single vector along with the observation subject in vision-based research.  
Accelerometer data, which estimate excavator movements through changes in speed, makes reproducing the original actions difficult. It is speculated that this data loss during the pattern estimation process contributes to discrepancies in precision and recall among the classes (refer to Table 4). Conversely, in the proposed methodology, by accurately preserving the core movement data of the equipment, including the piston displacement of each part over time, a perfect reproduction of the collected data is achieved. This approach is analyzed to result in higher performance compared to other methods.  
The limitations of this study are as follows. This research relies on limited data obtained from three pistons, excluding information such as the rotation of the excavator body, the movement of the undercarriage, and the amount of soil in the bucket. However, even with just the movement of three pistons, the performance in a constrained experimental environment has been shown to be commendable. This issue can be addressed by installing additional sensors to increase the data volume for each vector and by expanding the model's input data format. Moreover, the experiments were conducted using a one-twelfth scale model without the participation of professional excavator operators. Therefore, future research should involve real excavators and experts to compare the differences between the scale and actual experiments and to verify the practical applicability of the data.