Human activity recognition (HAR) has been actively researched in fields such as healthcare to understand and analyze human behavior in human-robot interaction. However, most studies have struggled to recognize activities like turning and motion transitions, which are often associated with dynamic balance. Therefore, we propose a novel HAR approach using a single sensor to collect and early fuse motion and position data. The aim is to enhance the accuracy of motion classification for daily activities and those that cause imbalance, which have traditionally been difficult to recognize. We constructed a quarantine room environment for data collection and to evaluate the impact of the suggested features on behavior. Five deep learning models were trained and evaluated to identify the optimal model. The collected data was classified and analyzed by the selected model, which demonstrated an average accuracy of 98.96%.
Water spraying work to prevent the dust from scattering during building dismantling operation has usually been done manually. Since it is very risky and often causes fatal accidents due to unexpected collapse, a few countries have adopted mechanical water spaying machines. However, these machines are still operated by human laborer, specifically in orienting the spraying direction, which induces low dust suppression efficiency. In this research, an automated fine dust tracking system was suggested to identify and track the dust generating position accurately. A GPS is installed on the secured body of the excavator which contains a crusher as an end-effector for building dismantlement. Assuming the position of the crusher is the dust generating spot, a forward kinematics analysis is performed to identify the crusher position from the body origin on which the GPS sensor is placed. With another GPS on the water-spraying robot, its relative position to the dust generating spot and its heading angle for tracking can be calculated consequently. Tracking experiments were conducted with a miniature excavator and a reduced size water spraying robot. The results showed a sufficient tracking performance enough to be applied to the water spaying machines.
Citations
Citations to this article as recorded by
Autonomous Fine Dust Source Tracking System of the Water Spray Robot for High-rise Building Demolition Hyeongyeong Jeong, Hyunbin Park, Jaemin Shin, Hyeonjae Jeong, Baeksuk Chu Journal of the Korean Society for Precision Engineering.2023; 40(9): 695. CrossRef
Motion Trajectory Planning and Design of Material Spraying Service Robot Gang Wang, Hongyuan Wen, Jun Feng, Jun Zhou, Haichang Zhang Advances in Materials Science and Engineering.2022; 2022: 1. CrossRef
Excavator Posture Estimation and Position Tracking System Based on Kinematics and Sensor Network to Control Mist-Spraying Robot Sangwoong Lee, Hyunbin Park, Baeksuk Chu IEEE Access.2022; 10: 107949. CrossRef
Optimal Design and Verification of a Water Spraying Robot for Dust Suppression Seolha Kim, Baeksuk Chu Journal of the Korean Society for Precision Engineering.2020; 37(10): 729. CrossRef
This paper proposes a walking position tracking method using inertial measurement unit (IMU) based on kinematic model of human body and walking cycle analysis. A kinematic model of lower body consisting of 9 coordinate frames and 7 links is used to estimate walking trajectory of the body based on rotation angles of the lower body measured by IMU. In this method, the position of left or right end frame of the lower body which is in contact with the ground is first identified and set as the reference position. The position of the base frame attached on the center of pelvis is then computed using the kinematic model and the reference position. One can switch the reference position with the position of the other end frame at the moment of heel strike. The proposed position tracking method was experimentally validated. Experimental result showed that position tracking errors were within 1.4% of walking distance for straight walking and 2.2% for circular walking.
Citations
Citations to this article as recorded by
Evaluation of Ergonomic Performance of Medical Smart Insoles Jae-Hoon Yi, Jin-Wook Lee, Dong-Kwon Seo Physical Therapy Rehabilitation Science.2022; 11(2): 215. CrossRef
Detection of Human Gait Phases Using Textile Pressure Sensors: A Low Cost and Pervasive Approach Matko Milovic, Gonzalo Farías, Sebastián Fingerhuth, Francisco Pizarro, Gabriel Hermosilla, Daniel Yunge Sensors.2022; 22(8): 2825. CrossRef
Inertial Sensor-Based Relative Position Estimation between Upper Body Segments Considering Non-Rigidity of Human Bodies Chang June Lee, Jung Keun Lee Journal of the Korean Society for Precision Engineering.2021; 38(3): 215. CrossRef
Gait Analysis Accuracy Difference with Different Dimensions of Flexible Capacitance Sensors DongWoo Nam, Bummo Ahn Sensors.2021; 21(16): 5299. CrossRef
Development of Wearable Sensing Suit for Monitoring Wrist Joint Motions and Deep Neural Network-based Calibration Method Junhwi Cho, Hyunkyu Park, Jung Kim Journal of the Korean Society for Precision Engineering.2020; 37(10): 765. CrossRef
Kinematic Constraint-Projected Kalman Filter to Minimize Yaw Estimation Errors Induced by Magnetic Distortions Tae Hyeong Jeon, Jung Keun Lee Journal of the Korean Society for Precision Engineering.2019; 36(7): 659. CrossRef
First
Prev
