Skeleton-Based Activity Recognition for Process-Based Quality Control of Concealed Work via Spatial-Temporal Graph Convolutional Networks.

Computer vision (CV)-based recognition approaches have accelerated the automation of safety and progress monitoring on construction sites. However, limited studies have explored its application in process-based quality control of construction works, especially for concealed work. In this study, a framework is developed to facilitate process-based quality control utilizing Spatial-Temporal Graph Convolutional Networks (ST-GCNs). To test this model experimentally, we used an on-site collected plastering work video dataset to recognize construction activities. An ST-GCN model was constructed to identify the four primary activities in plastering works, which attained 99.48% accuracy on the validation set. Then, the ST-GCN model was employed to recognize the activities of three extra videos, which represented a process with four activities in the correct order, a process without the activity of fiberglass mesh covering, and a process with four activities but in the wrong order, respectively. The results indicated that activity order could be clearly withdrawn from the activity recognition result of the model. Hence, it was convenient to judge whether key activities were missing or in the wrong order. This study has identified a promising framework that has the potential to the development of active, real-time, process-based quality control at construction sites.

Measuring Labor Input: Construction Activity Counting Using IMU on Hand Tools.

Efficient measurement of labor input is a critical aspect of on-site control and management in construction projects, as labor input serves as the primary and direct determinant of project outcomes. However, conventional manual inspection methods are off-line, tedious, and fail to capture their effectiveness. To address this issue, this research presents a novel method that leverages Inertial Measurement Unit (IMU) sensors attached to hand tools during construction activities to measure labor input in a timely and precise manner. This approach encompasses three steps: temporal-spatial feature extraction, self-similarity matrix calculation, and local specific structure identification. The underlying principle is based on the hypothesis that repetitive use data from hand tools can be systematically collected, analyzed, and converted into quantitative measures of labor input by the automatic recognition of repetition patterns. To validate this concept and assess its feasibility for general construction activities, we developed a preliminary prototype and conducted a pilot study focusing on rotation counting for a screw-connection task. A comparative analysis between the ground truth and the predicted results obtained from the experiments demonstrates the effectiveness and efficiency of measuring labor input using IMU sensors on hand tools, with a relative error of less than 5%. To minimize the measurement error, further work is currently underway for accurate activity segmentation and fast feature extraction, enabling deeper insights into on-site construction behaviors.

Feasibility Analysis of Using Channel State Information (CSI) Acquired from Wi-Fi Routers for Construction Worker Fall Detection.

Accidental falls represent a major cause of fatal injuries for construction workers. Failure to seek medical attention after a fall can significantly increase the risk of death for construction workers. Wearable sensors, computer vision, and manual techniques are common modalities for detecting worker falls in the literature. However, they are severely constrained by issues such as cost, lighting, background, clutter, and privacy. To address the problems associated with the existing proposed methods, a new method has been conceived to identify construction worker falls by analyzing the CSI signals extracted from commercial Wi-Fi routers. In this research context, our study aimed to investigate the potential of using Channel State Information (CSI) to identify falls among construction workers. To achieve the aim of this study, CSI data corresponding to 360 sets of activities were collected from six construction workers on real construction sites. The results indicate that (1) the behavior of construction workers is highly correlated with the magnitude of CSI, even in real construction sites, and (2) the CSI-based method for identifying construction worker falls has an accuracy of 99% and can also accurately distinguish between falls and fall-like actions. The present study makes a significant contribution to the field by demonstrating the feasibility of utilizing low-cost Wi-Fi routers for the continuous monitoring of fall incidents among construction workers. To the best of our knowledge, this is the first investigation to address the issue of fall detection using commercial Wi-Fi devices in real-world construction environments. Considering the dynamic nature of construction sites, the new method developed in this study helps to detect falls at construction sites automatically and helps injured construction workers to seek medical attention on time.

Regulating intracellular ROS signal by a dual pH/reducing-responsive nanogels system promotes tumor cell apoptosis.

Purpose: The levels of reactive oxygen species (ROS) in tumor cells are much higher than that in normal cells, and rise rapidly under the influence of exogenous or endogenous inducing factors, eventually leading to the apoptosis of tumor cells. Therefore, this study prepared a dual pH/reducing-responsive poly (N-isopropylacrylamide-co-Cinnamaldehyde-co-D-α-tocopheryl polyethylene glycol 1000 succinate, PssNCT) nanogels, which employed two exogenous ROS inducers, cinnamaldehyde (CA) and D-α-tocopheryl polyethylene glycol 1000 succinate (TPGS), to selectively induce apoptosis by regulating ROS levels in tumor cells. Methods: The PssNCT nanogels were prepared by the free radical precipitation polymerization under the crosslink between pH-sensitive hydrazone and reducing-sensitive disulfide bonds, followed by the physicochemical and morphological characteristics investigations. Plasma stability, dual pH/reducing responsive degradation and in vitro release were also assessed. In cell experiments, cytotoxicity in different cells were first detected. The intracellular ROS levels and mitochondrial functions of tumor cells were then evaluated. Moreover, the apoptosis and western-blot assays were employed to verify the association between ROS levels elevation and apoptosis in tumor cells. Results: The nanogels exhibited a round-like hollow structure with the diameter smaller than 200nm. The nanogels were stable in plasma, while showed rapid degradation in acidic and reducing environments, thus achieving significant release of CA and TPGS in these media. Furthermore, the sufficient amplification of ROS signals was induced by the synergistically function of CA and TPGS on mitochondria, which resulted in the opening of the mitochondrial apoptotic pathway and enhanced cytotoxicity on MCF-7 cells. However, nanogels barely affected L929 cells owing to their lower intracellular ROS basal levels. Conclusion: The specific ROS regulation method achieved by these nanogels could be explored to selectively kill tumor cells according to the difference of ROS signals in different kinds of cells.