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Assistive product (AP) aims to provide supplementary treatment for patients' physical function and daily activities to improve their functional status. One challenge in the AP design is prioritizing design requirements (DRs) based on patient requirements (PRs) involving diverse and variable interaction behaviors. This work developed and applied a quality function deployment method based on the spherical fuzzy bipartite graph (SFBG-QFD) to comprehensively consider the dependency relationship between PRs and DRs during AP design. Specifically, a spherical fuzzy bipartite graph is constructed in the PR-DR relationship matrix. The projection algorithm generates the one-mode spherical fuzzy graphs of PRs and DRs, reflecting the internal relationship of PR-PR and DR-DR pairs. Subsequently, the interaction influence of PRs and DRs is defined as an additional criterion for the QFD method. Through the SFBG-QFD method, designers can better consider the uncertainty of interaction between different types of requirements and make more reliable decisions. The effectiveness of the proposed method is verified through the AP design for pneumoconiosis patients, and the stability and potential of the proposed method are emphasized through comparative studies. [ABSTRACT FROM AUTHOR]

Serious games have emerged as promising tools for physiotherapy, offering an engaging way to motivate patients and improve physical functions. Stress, a common physiological response, can impede patient performance and hinder recovery in physiotherapy. However, there is limited research on stress recognition for children during physiotherapeutic serious games. This study proposes a deep and machine learning based method for stress detection from facial images of children. The contributions include the utilization of a novel dataset comprising videos of 25 children, including children with specific conditions, and the exploration of using adults' facial images for data augmentation. The proposed method employs a modified deep network architecture, VGG-Face, for facial emotion recognition and utilizes three machine learning models for stress recognition. This study represents the first attempt to recognize stress in children's facial images during physiotherapy with serious games. The findings hold the potential to optimize patient outcomes and contribute to monitoring patients during home therapy. • Emotion/Stress recognition in children during physiotherapeutic games. • First attempt to recognize stress from children's facial images. • Usage of a novel dataset of 25 children including children with specific conditions. [ABSTRACT FROM AUTHOR]