Integrating computer vision and machine learning with observation-based ergonomic posture risk assessment methods represents a significant advancement in occupational health and safety, which is essential in reducing work-related musculoskeletal disorders. This collaboration has enhanced the efficiency and scope of ergonomic posture risk assessments. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses guideline was utilized to select relevant papers in this review. A total of 583 research articles were retrieved from the Web of Science and IEEE Xplore databases, and 30 articles meeting the screening criteria were selected for detailed analysis. The results were organized into three distinct stages: Data Preparation, Pose Estimation, and Risk Assessment. During the Data Preparation Stage, the data acquisition devices, datasets, experimental conditions, and observation-based ergonomic posture risk assessment tools utilized in each article were discussed. Subsequently, in the Pose Estimation Stage, the human pose estimation techniques employed and their ability to detect wrist details were outlined. Finally, the risk assessment methods were presented, encompassing Directly Assess Risk Levels, Predict Risk Levels Using Traditional Machine Learning Algorithms, and Advanced Risk Prediction Using Deep Learning Algorithms. We propose several directions for future research. By synthesizing the current literature and identifying critical areas for future development, this review aims to provide a comprehensive overview of the intersection between computer vision, machine learning, and observational ergonomic posture risk assessment, with implications for improving occupational health and safety.

The development of biomaterials-based adhesives is one of the main research directions for the wood-based material industry. In previous research, tannin and sucrose were used as adhesive to manufacture particleboard. However, the reaction conditions need to be optimized. In this study, sulfuric acid was added to the tannin–sucrose adhesive as a catalyst to improve the curing process. Thermal analysis, insoluble mass proportion, FT-IR, and solid state ¹³C NMR were used to investigate the effects of sulfuric acid on the curing behavior of tannin and sucrose. Thermal analysis showed weight loss and endotherm temperature reduced from 205 and 215 to 136 and 138 °C, respectively, by adding sulfuric acid. In case of the adhesive with pH = 1.0, the insoluble mass proportion achieved 81% at 160 °C, which was higher than the reference at 220 °C. FT-IR analysis of the uncured adhesives showed that adding sulfuric acid leads to hydrolysis of sucrose; then, glucose and fructose converted to 5-hydroxymehthylfurfural (HMF) and levulinic acid. Dimethylene ether bridges were observed by FT-IR analysis of the cured adhesives. The results of solid state ¹³C NMR spectrum indicated that 5-HMF participated in the curing process and formed methylene bridges with the C8 position of the resorcinol A-rings of tannin, whereas dimethylene ether bridges were detected as a major chemical chain of the polymer. Lab particleboards were produced using 20 wt % resin content at 180 °C and 10 min press time; the tannin–sucrose adhesive modified with sulfuric acid to pH = 1.0 exhibited better performance than the unmodified tannin–sucrose adhesive; the properties of the boards fulfilled the requirement of Japanese Industrial Standard (JIS) A5908 type 15.

The growing incidence of ASD has made the development of early diagnostic techniques increasingly crucial. The paper provides a systematic review of video-based motion analysis and deep learning (DL) techniques for early diagnosis of ASD. It evaluates related articles published between 2018 and June 2024. We categorize the leading research according to the body parts involved: head pose, hand and arm pose, and trunk pose. And conduct an in-depth analysis of the procedures, feature extraction techniques, and classification models used in these studies. The paper delves into the general processes and methods proposed in the field, offering a detailed analysis. It presents a timeline of the evolution of ASD diagnostic methods and summarizes publicly available datasets, categorized into behavioral and multimodal datasets. The paper also reports the successes and challenges of applying AI in motion analysis for ASD detection. It provides a complete and systematic overview for researchers in the field of ASD.

With the continuous improvement of modern scientific construction, urban development is moving in the direction of digitization. Under the background of digital technology of Internet of things, many cities have new challenges and opportunities in the construction of a new generation. Urban furniture design plays an important role in public areas and activity spaces. It can bring many interactive and spatial elements to people’s daily life. At the same time, the functionality and reliability of urban furniture design are also the main contents we pay attention to. How to reflect the applicability of design and evaluate the performance of products is the research content of this paper under the background of Internet of things and digitization. Firstly, it analyzes the current situation of urban furniture design and the specific needs in the digital background. This paper explores the impact of the above factors on the safety of urban furniture from the aspects of urban development, economic situation and user groups. Using Internet of things data modeling and estimation methods to analyze the data changes contained in urban furniture design. Judge the performance changes of furniture design in actual use according to the data information, and analyze the feasibility of people in use. Finally, in the big data environment, with the support of Internet of things technology, this paper studies the sharing function in urban furniture design, and evaluates the feedback effect of shared urban furniture. The results show that urban furniture design under the digital background of Internet of things reflects good feasibility and applicability in modern urban construction. Digital urban furniture has gradually become a new symbol of a city. This paper uses data mining to obtain and analyze the data in the process of urban furniture design, and realizes the standardized processing of user behavior data. It not only meets people’s daily needs and the needs of intelligent equipment, but also strengthens the importance of urban furniture design and use in modern urban construction. Compared with the traditional Internet, the Internet of things can combine the wireless transmission network with the actual objects to achieve the purpose of accurately transmitting information.

If toddlers are not promptly checked and rescued after falling from relatively high locations at homes, they are at risk of severe health complications. We present a toddler target extraction method and real-time falling alarm. The procedure is executed in two stages: In stage I, a GELAN-integrated YOLOv8 model is used to extract the body features. Based on this, a head capture technique is developed to obtain the head features. In stage II, the “safe zone” is calculated through Generalized Hough Transform (GHT). The spatial location is compared to the preceding stage’s two centers of mass points, K for the toddler’s body and H for the head. Position status detection is performed on the extracted data. We gathered 230 RGB-captured daily videos of toddlers aged 13 to 30 months playing and experiencing upside-down falls. We split 500 video clips (×30 FPS) from 200 videos into 8:2 training and validation sets. A test set of 100 clips (×30 FPS) was cut from another 30 videos. The experimental results suggested that the framework has higher precision and recall in detection, as well as improved mean average precision and F1 scores compared to YOLOv3, v5, v6, and v8. It meets the standard FPS requirement for surveillance cameras and has an accuracy of 96.33 percent.

Toddlers face serious health hazards if they fall from relatively high places at home during everyday activities and are not swiftly rescued. Still, few effective, precise, and exhaustive solutions exist for such a task. This research aims to create a real-time assessment system for head injury from falls. Two phases are involved in processing the framework: In phase I, the data of joints is obtained by processing surveillance video with Open Pose. The long short-term memory (LSTM) network and 3D transform model are then used to integrate key spots’ frame space and time information. In phase II, the head acceleration is derived and inserted into the HIC value calculation, and a classification model is developed to assess the injury. We collected 200 RGB-captured daily films of 13- to 30-month-old toddlers playing near furniture edges, guardrails, and upside-down falls. Five hundred video clips extracted from these are divided in an 8:2 ratio into a training and validation set. We prepared an additional collection of 300 video clips (test set) of toddlers’ daily falling at home from their parents to evaluate the framework’s performance. The experimental findings revealed a classification accuracy of 96.67%. The feasibility of a real-time AI technique for assessing head injuries in falls through monitoring was proven.