# Time of Flight Distance Sensor-Based Construction Equipment Activity Detection Method
###### Abstract  
In this study, we delve into a novel approach by employing a sensor-based pattern recognition model to address the automation of construction equipment activity analysis. The model integrates time of flight (ToF) sensors with deep convolutional neural networks (DCNNs) to accurately classify the operational activities of construction equipment, focusing on piston movements. The research utilized a one-tweffth-scale excavator model, processing the displacement ratios of its pistons into a unified dataset for analysis. Methodologically, the study outlines the setup of the sensor modules and their integration with a controller, emphasizing the precision in capturing equipment dynamics. The DCNN model, characterized by its four-layered convolutional blocks, was meticulously tuned within the MATLAB environment, demonstrating the model's learning capabilities through hyperparameter optimization. An analysis of 2070 samples representing six distinct excavator activities yielded an impressive average precision of 95.51% and a recall of 95.31%, with an overall model accuracy of 95.19%. When compared against other vision-based and accelerometer-based methods, the proposed model showcases enhanced performance and reliability under controlled experimental conditions. This substantiates its potential for practical application in real-world construction scenarios, marking a significant advancement in the field of construction equipment monitoring. ToF distance sensor; equipment activity recognition; DCNN classification; piston movement; data transformation Article Time of Flight Distance Sensor-Based Construction Equipment Activity Detection Method  
2024 14  
sensors to monitor and interpret equipment activities. Notable methodologies encompass the deployment of acoustic sensors (e.g., microphones) [10; 11], inertial measurement units (IMUs), integrating gyroscopes, accelerometers, and magnetometers [12; 13], global positioning systems (GPSs) [14], ultra-wideband (UWB) technologies [15], and radio frequency identification (RFID) systems [15]. Among these technologies, acoustic sensors (e.g., microphones) demonstrate limitations in isolating equipment-specific signals within environments laden with obstacles and ambient noise [12]. A significant issue discussed in the context of the vision-based method is that the learning data are greatly influenced by weather, light, and shadows, and even the direction in which the object is observed and the color of the object can significantly affect the recognition results. On the other hand, in the sensor-based method, since the sensor's measurements are directly used for learning, there is a problem in that differences in activity recognition results can occur even when applying the same system if there are differences in the size or shape of the equipment. Accordingly, this study conducts research on the sensor-based method and has developed a data-handling approach and an artificial intelligence-based activity recognition model to solve the problems associated with this methodology. The composition of each section is as follows:  
Section 2: The discussion centers on reviewing existing studies within two distinct categories that are instrumental for the recognition of construction equipment activities. This review not only unpacks the most current trends but also delves into the limitations inherent in these methodologies. Additionally, this chapter sheds light on the fundamental concepts of optimization algorithms, which play a pivotal role in the realm of artificial intelligence training. Section 3: The focus shifts to elaborating the configuration of the data acquisition system. This system has been meticulously applied to both defined excavator activities and a one-twelfth scale model, specifically tailored for experimental purposes. Furthermore, this section provides a comprehensive overview of the strategies employed for the handling and analysis of the collected data.