Ergonomics is important for smooth and sustainable industrial operation. In the manufacturing industry, due to poor workstation design, workers frequently and repeatedly experience uncomfortable postures and actions (reaching above their shoulders, bending at awkward angles, bending backwards, flexing their elbows/wrists, etc.). Incorrect working postures often lead to specialized injuries, which reduce productivity and increase development costs. Therefore, examining workers’ ergonomic postures becomes the basis for recognizing, correcting, and preventing bad postures in the workplace. This paper proposes a new framework to carry out risk analysis of workers’ ergonomic postures through 3D human pose estimation from video/image sequences of their actions. The top-down network calculates human body joints when bending, and those angles are compared with the ground truth body bending data collected manually by expert observation. Here, we introduce the body angle reliability decision (BARD) method to calculate the most reliable body-bending angles to ensure safe working angles for workers that conform to ergonomic requirements in the manufacturing industry. We found a significant result with high accuracy in the score for ergonomics we used for this experiment. For good postures with high reliability, we have OWAS score 94%, REBA score 93%, and RULA score 93% accuracy. Similarly, for occluded postures we have OWAS score 83%, REBA score 82%, and RULA score 82%, compared with expert’s occluded scores. For future study, our research can be a reference for ergonomics score analysis with 3D pose estimation of workers’ postures.

With the demand for clean energy increasing, novel research is presented in this paper on providing sustainable, clean energy for a university campus. The Internet of Things (IoT) is now a leading factor in saving energy. With added deep learning for action recognition, IoT sensors implemented in real-time appliances monitor and control the extra usage of energy in buildings. This gives an extra edge on digitizing energy usage and, ultimately, reducing the power load in the electric grid. Here, we present a novel proposal through context-aware architecture for energy saving in classrooms, combining Internet of Things (IoT) sensors and video action recognition. Using this method, we can save a significant amount of energy usage in buildings.

Empathy has emerged as a valuable tool for communication in today’s digital world, with empathic computing serving as an integral component in facilitating human-like changes such as humanoid robots and fostering comfortable interactions. This article thoroughly explores the realms of empathy, empathic computing, emotion, and their intricate interconnections. Our primary focus is to investigate methods for measuring empathy between two remote collaborators. We review empathy usage and its measurement in remote collaboration and computing process. We analyze recent empathic computing methods in virtual reality and through gaze behavior. Our analysis explores empathy, and the application of empathic computing, and analyze their corresponding responses. We conclude by providing a comprehensive understanding of empathy’s multifaceted nature and its crucial role in establishing mutual understanding and connection between remote agents.

In this paper, we present a novel context-aware architecture for a classroom environment, recognizing student activities and saving energy. In contrast to previous models which are mainly focused on either video or image, various sensor data such as temperature, humidity, and luminance are combined with a video sequence in the proposed context-aware architecture. More specifically, student activities are classified using the convolutional 3D network (C3D) model and classroom temperature and humidity are predicted using a long short term memory (LSTM) network. Then, the outputs of the C3D model and LSTM network are combined and fed into fully connected layers to produce control parameters such as turn on/off air conditioners/heaters/dehumidifiers. Experimental results show that the proposed context-aware framework can be used for energy saving in classroom environments.

Direction of arrival (DOA) estimation has been a vital area of research since few decades due to its importance in applications like wireless communication, navigation radar, sonar etc. The resolution being one of the main challenges in the direction of arrival estimation can be enhanced by array antenna system with reliable signal processing. In this paper we simulate the high resolution subspace based DOA algorithm called Multiple Signal Classification (MUSIC) on a uniform linear array in the presence of ideal white Gaussian noise. This paper provides the relation between DOA estimation and number of array elements using MUSIC algorithm. MUSIA methods give more accurate and smooth resolution of the signals.