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Sitting in a chair is a typical act of modern people. Prolonged sitting and sitting with improper postures can lead to musculoskeletal disorders. Thus, there is a need for a sitting posture classification monitoring system that can predict a sitting posture. The purpose of this paper is to develop a system for classifying children’s sitting postures for the formation of correct postural habits.

For the data analysis, a pressure sensor of film type was installed on the seat of the chair, and image data of the postu.re were collected. A total of 26 children participated in the experiment and collected image data for a total of seven postures. The authors used convolutional neural networks (CNN) algorithm consisting of seven layers. In addition, to compare the accuracy of classification, artificial neural networks (ANN) technique, one of the machine learning techniques, was used.

The CNN algorithm was used for the sitting position classification and the average accuracy obtained by tenfold cross validation was 97.5 percent. The authors confirmed that classification accuracy through CNN algorithm is superior to conventional machine learning algorithms such as ANN and DNN. Through this study, we confirmed the applicability of the CNN-based algorithm that can be applied to the smart chair to support the correct posture in children.

This study successfully performed the posture classification of children using CNN technique, which has not been used in related studies. In addition, by focusing on children, we have expanded the scope of the related research area and expected to contribute to the early postural habits of children.

Plantar force is the interface pressure existing between the foot plantar surface and the shoe sole during static or dynamic gait. Plantar force derived from gait and posture plays a critical role for rehabilitation, footwear design, clinical diagnostics and sports activities, and so on. This paper aims to review plantar force measurement technologies based on piezoelectric materials, which can make the reader understand preliminary works systematically and provide convenience for researchers to further study.

The review introduces working principle of piezoelectric sensor, structures and hardware design of plantar force measurement systems based on piezoelectric materials. The structures of sensors in plantar force measurement systems can be divided into four kinds, including monolayered sensor, multilayered sensor, tri-axial sensor and other sensor. The previous studies about plantar force measurement system based on piezoelectric technology are reviewed in detail, and their characteristics and performances are compared.

A good deal of measurement technologies have been studied by researchers to detect and analyze the plantar force. Among these measurement technologies, taking advantage of easy fabrication and high sensitivity, piezoelectric sensor is an ideal candidate sensing element. However, the number and arrangement of the sensors will influence the characteristics and performances of plantar force measurement systems. Therefore, it is necessary to further study plantar force measurement system for better performances.

So far, many plantar force measurement systems have been proposed, and several reviews already introduced plantar force measurement systems in the aspect of types of pressure sensors, experimental setups for foot pressure measurement analysis and the technologies used in plantar shear stress measurements. However, this paper reviews plantar force measurement systems based on piezoelectric materials. The structures of piezoelectric sensors in the measurement systems are discussed. Hardware design applied to measurement system is summarized. Moreover, the main point of further study is presented in this paper.

Embroidery as a textile embellishment technique plays an important role in people's daily life. Esthetic embroidery artworks possess cultural values. With the development of robotics and artificial intelligence (AI), these technologies have been studied and applied in the embroidery process. This study aims to survey how these technologies facilitate embroidery from different aspects.

This paper surveys how the technologies of robotics and AI are applied in the embroidery field. The applications are mainly reviewed from three aspects: computerized robotic embroidery systems has been widely used for the mass production of embroidered textiles, the advanced technological systems and techniques have greatly facilitated the development of smart textiles and the artificial intelligence plays an important role in the inheritance, innovation and protection of traditional handicraft artwork of embroidery.

The programmable robotic embroidery machines have greatly improved the production efficiency of embroidered textiles and promoted the development of electronic textiles. The AI, mainly the deep learning technology, brings significant benefits to esthetic embroidery creation. Technology-based embroidery has become a hot research topic in the field of textiles.

This paper summarizes the application of robotics and AI technologies in the field of embroidery, which provides readers a comprehensive and systematic understanding about the research progress of modern technology-oriented embroidery. This helps readers gain inspiration from the technology perspectives.

The swivel construction method is a specially designed process used to build bridges that cross rivers, valleys, railroads and other obstacles. To carry out this construction method safely, real-time monitoring of the bridge rotation process is required to ensure a smooth swivel operation without collisions. However, the traditional means of monitoring using Electronic Total Station tools cannot realize real-time monitoring, and monitoring using motion sensors or GPS is cumbersome to use.

This study proposes a monitoring method based on a series of computer vision (CV) technologies, which can monitor the rotation angle, velocity and inclination angle of the swivel construction in real-time. First, three proposed CV algorithms was developed in a laboratory environment. The experimental tests were carried out on a bridge scale model to select the outperformed algorithms for rotation, velocity and inclination monitor, respectively, as the final monitoring method in proposed method. Then, the selected method was implemented to monitor an actual bridge during its swivel construction to verify the applicability.

In the laboratory study, the monitoring data measured with the selected monitoring algorithms was compared with those measured by an Electronic Total Station and the errors in terms of rotation angle, velocity and inclination angle, were 0.040%, 0.040%, and −0.454%, respectively, thus validating the accuracy of the proposed method. In the pilot actual application, the method was shown to be feasible in a real construction application.

In a well-controlled laboratory the optimal algorithms for bridge swivel construction are identified and in an actual project the proposed method is verified. The proposed CV method is complementary to the use of Electronic Total Station tools, motion sensors, and GPS for safety monitoring of swivel construction of bridges. It also contributes to being a possible approach without data-driven model training. Its principal advantages are that it both provides real-time monitoring and is easy to deploy in real construction applications.

In the information commons (IC) space of library, it is very important to recognize the emotional state of users for better playing the role of IC. In view of this point, this paper aims to discuss the human expression of user emotion.

An emotional state recognition method based on body posture change under video monitoring is proposed. In this method, two parameters are proposed to represent the emotional state of users. Finally, the distribution of users’ overall emotional state is recognized.

It is found that the change of human posture reflects the emotional state of users to a certain extent. The spatial frequency of the user’s average body position change and per capita body position change can reflect the spatial distribution of individual and body position change, respectively.

The method in this paper can effectively overcome the inaccuracy of manual identification of video monitoring images, especially in the case of a large number of users and effectively help the construction of university library IC space and provide a basis for the setting of environmental parameters.

In order-picking activities, the performance of the system can be influenced by different variables such as the order to be fulfilled, the distance to be covered or the experience of operators. Usually, this kind of activity is performed by operators rather than machines to assure flexibility. Consequently, their fatigue accumulation can decrease the performance of the overall system. The purpose of this paper is to define the kind of device to be used in an order-picking context, to obtain data which can be utilized for the evaluation of the level of fatigue and to improve the performance of the picking system.

The paper presents a comparison between existing fatigue methods which can be applied in a picking context. In addition, an analysis of the physiological literature for the evaluation of a new device for the monitoring of fatigue level is carried on and its practical use is shown.

The proposed research identifies in the heart rate monitor the device that, thanks to its advantages, can be the best one to be used in an industrial context for monitoring the physical fatigue of operators.

This study considers the importance of human factors in picking activities such as physical fatigue of operators and the need to have validated tools to monitor and to define the level of fatigue accumulation in each activity of different rate and duration.

Soft robotics is a burgeoning field owing to its high adaptability and safety in human–machine interaction and unstructured environments. However, the feedback control of soft actuators with flexible sensors is still a challenge.

To address this issue, this study proposes an optical fibre-based sensing membrane for the posture measurement of soft pneumatic bending actuators. The major contribution is the development of a flexible sensing membrane with a high sensitivity and repeatability for the feedback control of soft actuators. The characteristics of sensing membrane were analysed. The relationship between wavelength shift and bending curvature was derived. The curvatures of soft actuator were measured at four bending status, and the postures were reconstructed.

The results indicate that the measurement error is less than 2.1% of the actual bending curvature. The sensitivity is up to 212.8 pm/m⁻¹, and the signal fluctuation in repeated measurements is negligible. This approach has broad application prospects in soft robotics, because it makes the optical fibre achieve more strength and compatible with soft actuators, thus improving the sensing accuracy, sensitivity and reliability of fibre sensors.

Different from previous approaches, an optical fibre with FBGs is embedded into a multilayered polyimide film to form a flexible sensing membrane, and the membrane is embedded into a soft pneumatic bending actuator as the smart strain limited layer which is able to measure the posture in real time. This approach makes the optical fibre stronger and compatible with the soft pneumatic bending actuator, and the sensing accuracy, sensitivity and reliability are improved. The proposed sensing configuration is effective for the feedback control of the soft pneumatic bending actuators.

Current classroom observation strategies include questionnaires, interviews, tests, self-report metrics and live or video review observation. However, these traditional methods are subject to biases from observers in determining behavior nuances, as well as being difficult to analyze for rapid and practical feedback. In addition, the invasiveness of extra body equipment in the classroom may alter the dynamic between students and teachers. The emergence of portable devices into mainstream usage has opened a pathway for a relatively novel source of quantitative data, free from observer bias and often with accompanying analytical tools for convenience. The purpose of this study is to summarize current uses of portable technology and explore how such devices could be used as monitoring tools by both schools and education researchers.

Functions based on utility outside the education field are proposed for monitoring student activity, posture and movement, as well as proximity and relationships to others and their environment. Deterrents to device implementation in regular classroom use, such as the importance of considering ethical issues and hardware limitations, are also covered.

While current portable technology is unlikely to replace more commonly used techniques of observing classes, they have the capacity to augment qualitative strategies, particularly in the area of real-time data output.

Ideally, the lack of unbiased observational tools available and increased adoption of portable devices in classrooms could prompt future advances in technology for teaching and learning environments.

This review summarizes potential uses for portable technology in classroom observation strategies undertaken by researchers and teachers to improve learning and teaching practices.