# Time of Flight Distance Sensor-Based Construction Equipment Activity Detection Method
## 2 Literature Review and Background
### Sensor-Based Method for Recognizing Equipment Activity  
Sensor-based methods enable the monitoring of construction equipment by analyzing the location and movement data collected from sensors attached to the machinery. These methods can be defined as real-time location system (RTLS) methods and kinematic methods. In RTLS research, sensors such as GPS, UWB, and RFID have been used to analyze the information (trajectories and regions) of construction equipment by combining it with the site's geographical information, thereby estimating the equipment's productivity [24; 25; 26; 27; 28; 29]. However, RTLS methods rely solely on location information, which presents limitations in estimating work productivity based on the detailed actions of the equipment. In order to overcome this, kinematic method-based activity estimation approaches have been researched, applying sensors such as accelerometers and IMUs to directly measure the equipment's movement.  
Studies have been conducted on attaching accelerometers (smartphones) to the cabin's control panel to use acceleration information for classifying the specific activities of equipment and extracting cycle times [5; 29]. A method using 3D orientation sensors to automatically classify the activities of trucks and loaders has been developed [30]. Additionally, success was achieved in recognizing three types of excavator activities based on accelerometer sensors [31]. These features have been utilized as foundational research for applying networks and activating the field of construction equipment activity detection by using sensors and artificial intelligence with the use of SVM, random forest (RF), and dynamic time warping (DTW) [32]. Later studies have verified a high accuracy performance of over 96% in both types of neural networks, employing CNNs with accelerometer data, and recurrent neural networks (RNNs) with IMU data for classifying excavator and loader activity [12; 33].  
However, when using data from accelerometers or IMUs in the kinematic method, there is a problem in that obtaining similar results becomes uncertain if the equipment is changed. For example, in a study that applied IMU and GPS signals data to the fractional random forest (FRF) technique, the same learning method was applied to actual excavators and a one-twelfth scale model, but the experiment showed a difference in accuracy, with 84.1% for the actual excavator and 72.9% for the model excavator [14]. Thus, the accuracy of subsequent activity recognition in the kinematic method is significantly affected by the sensor's location, tag number, and positioning details of the construction equipment structure [20].  
For these reasons, it is necessary to overcome the limitations of both the vision-based method and the kinematic method. This paper presents a method that utilizes time of flight (ToF) distance sensors to directly measure the piston displacement of equipment, allowing for the collection of samples in terms of piston displacement ratios. The data collected from sensors is assumed to be a single image with specific patterns within a certain time series. For this reason, our study adopts the CNN classifier method, which shows specialized performance in analyzing image patterns when compared to RNN and RF classifiers. As a result, this study develops a construction equipment activity detection model based on a deep convolutional neural network (DCNN) with layered convolutional layers.