# Advances in Human-Machine Systems, Human-Machine Interfaces and Human Wearable Device Performance
###### Abstract  
The human-machine system (HMS) and human-machine interface (HMI) are among the top factors that affect the development of advanced systems, equipment, and products. To achieve the optimal performance of systems and products and minimize system failure and disasters, human factors and ergonomics, including human-centered design [1, 2], design for human use [3], user-friendly design [4], and fitting tasks to humans [5, 6, 7], need to be widely adopted. These concepts require designing products based on the capabilities and characteristics of users [8, 9, 10]. The HMS treats both the system and the humans involved as a unit. The HMI, on the other hand, focuses on the interactions between humans and the machines they operate. How users communicate with the machines they are using and whether such communications are efficient and successful affect the performance of the HMS involved. To enhance our understanding of the HMS and HMI associated with advanced systems and products, we collected submissions in these areas with particular focus given to wearable devices such as augmented reality (AR) and virtual reality (VR) devices. Our aim is to promote research and fill information gaps in these areas. This Special Issue was launched on 7 November 2022 and lasted for almost three and a quarter years. During this period, we received 21 submissions. After rigorous review processes, we finally accepted and published 10 of these works. 9 July 202503  
The third paper published is the \"Impact of navigation aid and spatial ability skills on wayfinding performance and workload in indoor-outdoor campus navigation: challenges and design\". This paper highlights the challenges of wayfinding on a university campus using mobile navigation apps. A smartphone was used, and the performances of the participants, together with their spatial capability using the two apps, were compared. The map app and smartphone combinations used in this study comprised a system, and the performances of the HMS were presented. The fifth and sixth papers in this Special Issue focus on mental workload monitoring and mental fatigue assessment, respectively. Both studies in these papers focused on the development of monitoring and assessment methodologies. They tested their approaches or models using the dataset available online. In paper 5 (\"Brainwaves in the cloud: cognitive workload monitoring using deep gated neural network and industrial internet of things\"), a cognitive workload monitoring system was formulated by leveraging the deep gated neural network. A hybrid model integrating bidirectional long short-term memory and gated recurrent unit networks was included. Paper 6 (\"Human operator mental fatigue assessment based on video: ML-driven approach and its application to HFAVD dataset\") introduces an approach to assess human mental fatigue based on the postures recorded on videos. The aim of this study was to incorporate machine learning techniques in the mental fatigue assessment of human operators. Paper 8 (\"Emotional dynamics in human-machine interactive systems: effectively measuring Kuhn Poker approach with experimental validation\") explores how emotional states influence decision-making behaviors in HMS. The authors used Kuhn poker as an experimental context and developed an intelligent decision support system to provide real-time strategic advice to the participants. They found that positive emotional states lead individuals to be more risk-seeking and less inclined to collaborate with the AI-driven system. Negative emotional states, on the other hand, resulted in risk-averse tendencies and higher compliance with system recommendations. Expanding on this, the paper \"A wearable microphone array helmet for automotive applications\" (paper 9) is a unique article concerning a wearable device. In this paper, a wearable microphone array was installed over the surface of a helmet.