Traditional rigid exoskeletons can be challenging to the comfort of wearers and can have large pressure, which can even alter natural hand motion patterns. In this paper, we propose a low-cost soft exoskeleton glove (SExoG) system driven by surface electromyography (sEMG) signals from non-paretic hand for bilateral training. A customization method of geometrical parameters of soft actuators was presented, and their structure was redesigned. Then, the corresponding pressure values of air-pump to generate different angles of actuators were determined to support four hand motions (extension, rest, spherical grip, and fist). A two-step hybrid model combining the neural network and the state exclusion algorithm was proposed to recognize four hand motions via sEMG signals from the healthy limb. Four subjects were recruited to participate in the experiments. The experimental results show that the pressure values for the four hand motions were about −2, 0, 40, and 70 KPa, and the hybrid model can yield a mean accuracy of 98.7% across four hand motions. It can be concluded that the novel SExoG system can mirror the hand motions of non-paretic hand with good performance.

Artificial intelligence generation technology has brought new opportunities to the field of fashion design. Attribute knowledge has a significant impact on the overall effect of fashion design. Contemporary generative methods of fashion design frequently yield results lacking semantic information or missing specific attributes. To address the problem, this study aims for an intelligent generative method of fashion design through constructing prompt templates and a specific attribute low-rank adaption (LoRA) to combine fashion attribute knowledge into the generative process of the Stable Diffusion Model. First, a fashion attribute knowledge graph is constructed to establish prompt templates, and natural language descriptions are transformed into professional and complete prompt through GPT-4. Second, the fashion dataset is annotated with templates to filter specific attributes for LoRA training, followed by controlling fashion attributes in generation. Furthermore, analyses of the generation of women’s jacket designs show that the proposed method consistently improves the accuracy and stability of attributes in fashion design generation.

Purpose: Choice overload in e-commerce induces choice difficulty, which is detrimental to shopping decision-making. The purpose of this paper is to provide relatively simple and effective methods and indicators to detect and assess the choice difficulty states of customers during clothing online shopping.Design/methodology/approach: In order to find out the behavioral performance of choice difficulty states during clothing online shopping, the authors performed the following steps: at first, the authors conducted an experiment to record the videos of the purchasing process during clothing online shopping. Then, the authors carried out the behavioral analysis of customers, correlating each behavioral index to choice difficulty states.Findings: The results of the behavioral analysis in this study have indicated that three types of behavior were significantly correlated to the choice difficulty states of customers.Practical implications: It is expected that it would be possible to use the threshold values of behavioral indicators to determine whether and when the customer is suffering from choice phobia disorder. Based on the findings, a recommender system with timely interventions to help customers with choice difficulty to make shopping decisions will be developed in the future.Originality/value: This is the first reported study that explores the possibility of using behavioral indicators to detect choice difficulty, which will remain helpful to the scientific community to start further customer-centered research work and development of clothing online shopping.

To investigate the legibility of Chinese characters' font size, text background opacity, and font stroke for the elderly in virtual reality, we recruited old and young participants to conduct experiments with VR and used eye-tracking technology to record the data of task completion time and error rate. After analysis, we concluded that the minimum recognition font size for the elderly is 30 dmm, and the best font size is 60 dmm, which is 20 and 40 dmm for young people. The font style has a significant effect on old people (p = 0.000*). Besides, for font sizes smaller than 20 dmm and bigger than 50 dmm, text with strokes and over 50% semi-transparent backgrounds can improve legibility for the elderly. With a suitable font size, the influence of font style on the elderly is not significant. These conclusions can provide a reference for the elderly-oriented Chinese font design in VR.