Ali Kamali Mohammadzadeh

In the past decade, extended reality (XR) has been introduced into healthcare due to several potential benefits, such as scalability and cost savings. As there is no comprehensive study covering all the factors influencing the selection of an XR system in the healthcare and medical domain, a Decision Support System is proposed in this paper to identify and rank factors impacting the performance of XR in this domain from an engineering design perspective. The proposed system is built upon the Supply Chain Operations Reference (SCOR) model supported by a literature survey and experts’ knowledge to extract and identify important factors. Subsequently, the factors are categorized into distinct categories, and their relative importance is specified by Analytic Network Process (ANP) models under a fuzzy environment. Two fuzzy approaches for the ANP models are compared, and the results are analyzed using statistical testing. The computational results show that the ranking agreement between the two fuzzy approaches is strong and corresponds to the fact that both approaches yield the same ranking of primary factors, highlighting the significance of reliability as the topmost factor, followed by responsiveness, cost, and agility. It is shown that while the top three important sub-factors are identical between the two approaches, their relative order is slightly varied. Safety is considered to be the most critical aspect within the reliability category in both approaches, but there are discrepancies in the rankings of accuracy and user control and freedom. Both approaches also consider warranty and depreciation costs as the least significant criteria.

Intention recognition in Human–Robot Interaction (HRI) is critical for enabling robots to anticipate and respond to human actions effectively. This study explores the application of deep learning techniques for the classification of human intentions in HRI, utilizing data collected from Virtual Reality (VR) environments. By leveraging VR, a controlled and immersive space is created, where human behaviors can be closely monitored and recorded. Ensemble deep learning models, particularly Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM), and Transformers, are trained on this rich dataset to recognize and predict human intentions with high accuracy. While CNN and CNN-LSTM models yielded high accuracy rates, they encountered difficulties in accurately identifying certain intentions (e.g., standing and walking). In contrast, the CNN-Transformer model outshone its counterparts, achieving near-perfect precision, recall, and F1-scores. The proposed approach demonstrates the potential for enhancing HRI by providing robots with the ability to anticipate and act on human intentions in real time, leading to more intuitive and effective collaboration between humans and robots. Experimental results highlight the effectiveness of VR as a data collection tool and the promise of deep learning in advancing intention recognition in HRI.