Automated Activity Identification for Construction Equipment Using Motion Data From Articulated Members

• Main Authors: Khandakar M. Rashid, Joseph Louis
• Format: Article
• Language: English
• Published: Frontiers Media S.A. 2020-01-01
• Series: Frontiers in Built Environment
• Subjects: activity recognition; construction equipment; machine learning; inertial measurement unit; feature extraction
• Online Access: https://www.frontiersin.org/article/10.3389/fbuil.2019.00144/full

Real-time monitoring and assessment of construction resources have always been a challenge due to the unique, dynamic, and complex nature of each construction site and operation. The ability to automatically classify activities performed by various equipment in real time can aid in making timely tactical operational decisions that can lead to increased fleet productivity, reduced time and cost of operations, and minimized idle times. Such endeavors have traditionally been performed manually through human observation, making it extremely labor and time-intensive. Meanwhile, the development of low-cost micro-electro-mechanical systems (MEMS) with rapidly evolving computing, networking, and storage capabilities, along with the advances in computational techniques such as machine learning present new opportunities in the real-time activity identification domain. Even though previous studies have shown promising results for equipment activity identification at limited levels of detail, they have a fundamental limitation in their reliance on the equipment vibration. Equipment vibration is highly dependent on factors that are extrinsic to the performance of an activity itself such as ground conditions, age and condition of equipment, and operator skill. This aspect of current methods necessitates the collection of training data from the specific equipment of interest and requires manual labeling of training data, thereby limiting its application across different types of equipment and operating conditions. This paper investigates the use of activity-specific equipment motions instead of vibration for activity identification. This approach is the first step toward the larger goal of generating training data automatically from virtual kinematic models of equipment in the future. This paper also provides an array of sensitivity analyses in order to determine the most appropriate parameters for implementing machine learning algorithms for equipment activity identification. A case study was performed using an excavator working on an earthmoving site that demonstrated a significant improvement in equipment activity identification results by utilizing inertial measurement unit (IMU) data of different articulated elements over previous efforts. The results of this paper indicate that more accurate results for activity identification can be obtained by using articulated equipment motion over vibration, which paves the way for the automatic generation of labeled training data in the future.