Mechanical Design, Control and Evaluation of A Portable Rehabilitation Device for Upper Arm

There is a need for functional and effective rehabilitation devices for humans with upper arm injuries. Existing devices are either too heavy, not portable, or do not have 4 degrees-of-freedom (DOF) on the forearm. In this research, a new mechanical mechanism and structure were proposed to cover the full range of wrist and forearm motions as much as possible without sacrificing portability. In addition, the proposed device would have 4 DOF including wrist flexion/extension and radial/ulnar deviation, forearm pronation/supination, and elbow flexion/extension motions. A prototype was developed using 3D printed parts weighing about 840 grams; by comparison, the lightest existing device weighs 2 kg. The portability of the proposed design can increase the flexibility of therapy programs. Experiments were carried out to evaluate the prototype based on workspace, backlash, accuracy, and repeatability. Compared to other devices, the prototype covers all 4 DOF and the motion range coverage ranges from 88% to 100%. These improvements allow the prototype to cover more complicated rehab motions and thereby facilitate performance of difficult daily activities such as rise from a chair and tie a scarf. Experiments results also suggest that the performance of the prototype is very accurate and repeatable. For example, the average backlash is about 1 mm, the accuracy of the device is about ±0.8 mm, and the repeatability is about 0.5 mm. Future directions include (1) evaluate the effectiveness of the prototype with human subjects, (2) add a human centered sensory and computing device to monitor and provide customized rehabilitation motions

Improving garment thermal insulation property by combining two non-contact measuring tools

To investigate the effect of air gaps on the heat transfer performance of clothing, the method using the combination of two non-contact measuring tools (infrared thermal camera and 3D body scanner) has been developed considering the quantification of the air gap thickness and clothing surface temperature of different body parts without contacting clothing surface directly. The results show that the air gaps over middle and lower back of upper body have the largest thickness in all body parts, while the front and back shoulders have the smallest air gap thickness. The one-way analysis of variance shows that air gap thickness under shoulder segments has no significant difference in terms of size. Furthermore, clothing surface temperatures of shoulder and chest decrease gradually along with air gap thickness; clothing surface temperatures of front abdomen, front waist, pelvis and hip segments decrease initially but begin to increase when the air gap is above 1.5cm; clothing surface temperatures of middle back and back waist continually increase with the air gap thickness. Based on the comprehensive analyzation of the distributed features of air gap thickness and clothing surface temperature of different body parts, a revised clothing pattern with lower regional temperature and higher thermal insulation is put forward

Q2ATransformer: Improving Medical VQA via an Answer Querying Decoder

Medical Visual Question Answering (VQA) systems play a supporting role to understand clinic-relevant information carried by medical images. The questions to a medical image include two categories: close-end (such as Yes/No question) and open-end. To obtain answers, the majority of the existing medical VQA methods relies on classification approaches, while a few works attempt to use generation approaches or a mixture of the two. The classification approaches are relatively simple but perform poorly on long open-end questions. To bridge this gap, in this paper, we propose a new Transformer based framework for medical VQA (named as Q2ATransformer), which integrates the advantages of both the classification and the generation approaches and provides a unified treatment for the close-end and open-end questions. Specifically, we introduce an additional Transformer decoder with a set of learnable candidate answer embeddings to query the existence of each answer class to a given image-question pair. Through the Transformer attention, the candidate answer embeddings interact with the fused features of the image-question pair to make the decision. In this way, despite being a classification-based approach, our method provides a mechanism to interact with the answer information for prediction like the generation-based approaches. On the other hand, by classification, we mitigate the task difficulty by reducing the search space of answers. Our method achieves new state-of-the-art performance on two medical VQA benchmarks. Especially, for the open-end questions, we achieve 79.19% on VQA-RAD and 54.85% on PathVQA, with 16.09% and 41.45% absolute improvements, respectively

Assessment of English Teaching From Social - Anthropological Perspective: A Case Study of Microteaching in Warwick SJTU ETD Programme

Microteaching has gained considerable attention for its effectiveness in training teachers. Based on social-anthropological theory, a microteaching workshop in Warwick SJTU ETD Programme for 22 English teachers was investigated. Observation and interview, as the main basic methods, were applied to collect data. The results showed that microteaching offered participants an opportunity to practice teaching and receive useful feedback from peers and professional supervisors. Moreover, it was indicated that the improvement of teaching largely depends on self-reflection. The participants who were aware of teaching objectives and teaching aids, and opened to alternative teaching materials could easily manage the classroom teaching, and activate students’ learning