Intelligent technology has had a profound impact on working patterns and the needs of office workers. The corresponding office environments have also changed to some extent. As more and more intelligent functions are integrated, a question as to whether the intelligent office environment really meets needs of office workers needs to be considered. In this study, a methodology that is applicable to improve biophilic and intelligent designs simultaneously in office environments was developed according to the needs of office workers. The observations of office environments, interviews, and questionnaires of an office group were performed. The physical and psychological health needs of office workers, as well as the correlations between six office environmental factors and six intelligent functions were discussed using the data of 211 office workers. Then, 12 patterns of biophilic design were addressed from existing biophilic theory, and the results of a user study, as well as the corresponding design framework of intelligent office products, which are the carriers of intelligent functions, were described. The feasibility and the positive influence in the office have been illustrated through a design case and its evaluation in real office environments. This study mainly explores the design of office environments from the perspective of humans with the hope to provide helpful guidance for intelligent offices and related workspace design in the future.

The emotional expression of body movement, which is an aspect of emotional communication between humans, has not been considered enough in the field of human–robot interactions (HRIs). This paper explores human perceptions of the emotional expressions of humanoid robot body movements to study the emotional design of the bodily expressions of robots and the characteristics of the human perception of these emotional body movements. Six categories of emotional behaviors, including happiness, anger, sadness, surprise, fear, and disgust, were designed by imitating human emotional body movements, and they were implemented on a Yanshee robot. A total of 135 participants were recruited for questionnaires and eye-tracking measurements. Statistical methods, including K-means clustering, repeated analysis of variance (ANOVA), Friedman’s ANOVA, and Spearman’s correlation test, were used to analyze the data. According to the statistical results of emotional categories, intensities, and arousals perceived by humans, a guide to grading the designed robot’s bodily expressions of emotion is created. By combining this guide with certain objective analyses, such as fixation and trajectory of eye movements, the characteristics of human perception, including the perceived differences between happiness and negative emotions and the trends of eye movements for different emotional categories, are described. This study not only illustrates subjective and objective evidence that humans can perceive robot bodily expressions of emotions through only vision but also provides helpful guidance for designing appropriate emotional bodily expressions in HRIs.

This paper mainly explores the computational model that connects a robot’s emotional body movements with human emotion to propose an emotion recognition method for humanoid robot body movements. There is sparse research directly carried out from this perspective to recognize robot bodily expression. A robot’s body movements are designed by imitating human emotional body movements. Subjective questionnaires and statistical methods are used to analyze the characteristics of a user’s perceptions and select appropriate designs. An emotional body movement recognition model using a BP neural network (EBMR-BP model) is proposed, in which the selected robot’s body movements and corresponding emotions are used as inputs and outputs. The corresponding topological architecture, encoding rules, and training process are illustrated in detail. Then, the PSO method and the RMSProp algorithm are introduced to optimize the EBMR-BP method, and the PSO-BP-RMSProp model is developed. Through experiments and comparisons for emotion recognition of a robot’s body movements, the feasibility and effectiveness of the EBMR-BP model, with a recognition rate of 66.67%, and the PSO-BP-RMSProp model, with a recognition rate of 88.89%, are verified. This indicates that the proposed method can be used for emotion recognition of a robot’s body movements, and optimization can improve emotion recognition. The contributions are beneficial for emotional interaction design in HRI.

The interaction patterns are constantly changing as the rapid development in technology. The non-contact interaction system using gestures is becoming important in human-computer interaction nowadays. This paper illustrates a design framework of a non-contact interaction system using gestures, which fully considers the approach to fit non-contact gestures into the system. The corresponding user requirements were investigated by surveys, and the universal design guide of non-contact gestures was summarized by statistical analysis. A case study was illustrated for key points of the developed design framework. The scheme, including interaction architecture, gesture definition, and user interface design, was described. The rapid prototyping was built. Evaluations, including eye-tracking measurements and tests of system usability, were implemented and analyzed by statistical methods to present suggestions of iterative design. The feasibility of the developed framework was verified. By statistical methods, the relationship between the interaction usability and the user’s perception for information was discussed, and the user’s perception for the display of dynamic and static gestures was analyzed. The results provide a clear approach to satisfy user’s preferences and improve the interaction quality for non-contact interaction systems using gestures, and furnish empirical grounding for sustainably devising similar interaction systems.

In the post-pandemic era, public space safety has emerged as a central public concern. As global public health awareness rose, traditional manual cleaning methods were increasingly inadequate for large-scale and high-standard cleaning requirements. Therefore, the design of a commercial cleaning robot that combined cleaning efficiency, environmental performance, and sustainability was essential to meet evolving societal demands. This paper systematically gathered and analyzed commercial cleaning robot functional requirements through a comprehensive investigation. According to the Kano model, functional requirements were categorized by attributes and their importance was quantified. Subsequently, using the QFD model, the design requirements for commercial cleaning robots were determined, and their significance weights were established. This identified the key design requirements. The results of this study indicated that the integration of the Kano and QFD models provided a novel perspective for the design of functional integration in commercial cleaning robots. Furthermore, it provided a practical methodological framework for addressing similar issues in the future.

Since service robots serving as salespersons are expected to be deployed efficiently and sustainably in retail environments, this paper explores the impacts of their interaction cues on customer experiences within small-scale self-service shops. The corresponding customer experiences are discussed in terms of fluency, comfort and likability. We analyzed customers’ shopping behaviors and designed fourteen body gestures for the robots, giving them the ability to select appropriate movements for different stages in shopping. Two experimental scenarios with and without robots were designed. For the scenario involving robots, eight cases with distinct interaction cues were implemented. Participants were recruited to measure their experiences, and statistical methods including repeated-measures ANOVA, regression analysis, etc., were used to analyze the data. The results indicate that robots solely reliant on voice interaction are unable to significantly enhance the fluency, comfort and likability effects experienced by customers. Combining a robot’s voice with the ability to imitate a human salesperson’s body movements is a feasible way to truly improve these customer experiences, and a robot’s body movements can positively influence these customer experiences in human–robot interactions (HRIs) while the use of colored light cannot. We also compiled design strategies for robot interaction cues from the perspectives of cost and controllable design. Furthermore, the relationships between fluency, comfort and likability were discussed, thereby providing meaningful insights for HRIs aimed at enhancing customer experiences.