An interactive motion analysis framework for diagnosing and rectifying potential injuries caused through resistance training

With the rapid increase in individuals participating in resistance training activities, the number of injuries pertaining to these activities has also grown just as aggressively. Diagnosing the causes of injuries and discomfort requires a large amount of resources from highly experienced physiotherapists. In this paper, we propose a new framework to analyse and visualize movement patterns during performance of four major compound lifts. The analysis generated will be used to efficiently determine whether the exercises are being performed correctly, ensuring anatomy remains within its functional range of motion, in order to prevent strain or discomfort that may lead to injury

Emotion Transfer for 3D Hand and Full Body Motion using StarGAN

In this paper, we propose a new data-driven framework for 3D hand and full-body motion emotion transfer. Specifically, we formulate the motion synthesis task as an image-to-image translation problem. By presenting a motion sequence as an image representation, the emotion can be transferred by our framework using StarGAN. To evaluate our proposed method's effectiveness, we first conducted a user study to validate the perceived emotion from the captured and synthesized hand motions. We further evaluate the synthesized hand and full body motions qualitatively and quantitatively. Experimental results show that our synthesized motions are comparable to the captured motions and those created by an existing method in terms of naturalness and visual quality

Emotion Transfer for Hand Animation

We propose a new data-driven framework for synthesizing hand motion at different emotion levels. Specifically, we first capture high-quality hand motion using VR gloves. The hand motion data is then annotated with the emotion type and a latent space is constructed from the motions to facilitate the motion synthesis process. By interpolating the latent representation of the hand motion, new hand animation with different levels of emotion strength can be generated. Experimental results show that our framework can produce smooth and consistent hand motions at an interactive rate

Emotion Transfer for 3D Hand Motion using StarGAN

In this paper, we propose a new data-driven framework for 3D hand motion emotion transfer. Specifically, we first capture high-quality hand motion using VR gloves. The hand motion data is then annotated with the emotion type and converted to images to facilitate the motion synthesis process and the new dataset will be available to the public. To the best of our knowledge, this is the first public dataset with annotated hand motions. We further formulate the emotion transfer for 3D hand motion as an Image-to-Image translation problem, and it is done by adapting the StarGAN framework. Our new framework is able to synthesize new motions, given target emotion type and an unseen input motion. Experimental results show that our framework can produce high quality and consistent hand motions

A clinically relevant sheep model of orthotopic heart transplantation 24 h after donor brainstem death

BACKGROUND: Heart transplantation (HTx) from brainstem dead (BSD) donors is the gold-standard therapy for severe/end-stage cardiac disease, but is limited by a global donor heart shortage. Consequently, innovative solutions to increase donor heart availability and utilisation are rapidly expanding. Clinically relevant preclinical models are essential for evaluating interventions for human translation, yet few exist that accurately mimic all key HTx components, incorporating injuries beginning in the donor, through to the recipient. To enable future assessment of novel perfusion technologies in our research program, we thus aimed to develop a clinically relevant sheep model of HTx following 24 h of donor BSD. METHODS: BSD donors (vs. sham neurological injury, 4/group) were hemodynamically supported and monitored for 24 h, followed by heart preservation with cold static storage. Bicaval orthotopic HTx was performed in matched recipients, who were weaned from cardiopulmonary bypass (CPB), and monitored for 6 h. Donor and recipient blood were assayed for inflammatory and cardiac injury markers, and cardiac function was assessed using echocardiography. Repeated measurements between the two different groups during the study observation period were assessed by mixed ANOVA for repeated measures. RESULTS: Brainstem death caused an immediate catecholaminergic hemodynamic response (mean arterial pressure, p = 0.09), systemic inflammation (IL-6 - p = 0.025, IL-8 - p = 0.002) and cardiac injury (cardiac troponin I, p = 0.048), requiring vasopressor support (vasopressor dependency index, VDI, p = 0.023), with normalisation of biomarkers and physiology over 24 h. All hearts were weaned from CPB and monitored for 6 h post-HTx, except one (sham) recipient that died 2 h post-HTx. Hemodynamic (VDI - p = 0.592, heart rate - p = 0.747) and metabolic (blood lactate, p = 0.546) parameters post-HTx were comparable between groups, despite the observed physiological perturbations that occurred during donor BSD. All p values denote interaction among groups and time in the ANOVA for repeated measures. CONCLUSIONS: We have successfully developed an ovine HTx model following 24 h of donor BSD. After 6 h of critical care management post-HTx, there were no differences between groups, despite evident hemodynamic perturbations, systemic inflammation, and cardiac injury observed during donor BSD. This preclinical model provides a platform for critical assessment of injury development pre- and post-HTx, and novel therapeutic evaluation. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s40635-021-00425-4

A922 Sequential measurement of 1 hour creatinine clearance (1-CRCL) in critically ill patients at risk of acute kidney injury (AKI)

Meeting abstrac

International genome-wide meta-analysis identifies new primary biliary cirrhosis risk loci and targetable pathogenic pathways.

Primary biliary cirrhosis (PBC) is a classical autoimmune liver disease for which effective immunomodulatory therapy is lacking. Here we perform meta-analyses of discovery data sets from genome-wide association studies of European subjects (n=2,764 cases and 10,475 controls) followed by validation genotyping in an independent cohort (n=3,716 cases and 4,261 controls). We discover and validate six previously unknown risk loci for PBC (Pcombined<5 × 10(-8)) and used pathway analysis to identify JAK-STAT/IL12/IL27 signalling and cytokine-cytokine pathways, for which relevant therapies exist

Improving posture classification accuracy for depth sensor-based human activity monitoring in smart environments

Smart environments and monitoring systems are popular research areas nowadays due to its potential to enhance the quality of life. Applications such as human behaviour analysis and workspace ergonomics monitoring are automated, thereby improving well-being of individuals with minimal running cost. The central problem of smart environments is to understand what the user is doing in order to provide the appropriate support. While it is difficult to obtain information of full body movement in the past, depth camera based motion sensing technology such as Kinect has made it possible to obtain 3D posture without complex setup. This has fused a large number of research projects to apply Kinect in smart environments. The common bottleneck of these researches is the high amount of errors in the detected joint positions, which would result in inaccurate analysis and false alarms. In this paper, we propose a framework that accurately classifies the nature of the 3D postures obtained by Kinect using a max-margin classifier. Different from previous work in the area, we integrate the information about the reliability of the tracked joints in order to enhance the accuracy and robustness of our framework. As a result, apart from general classifying activity of different movement context, our proposed method can classify the subtle differences between correctly performed and incorrectly performed movement in the same context. We demonstrate how our framework can be applied to evaluate the user’s posture and identify the postures that may result in musculoskeletal disorders. Such a system can be used in workplace such as offices and factories to reduce risk of injury. Experimental results have shown that our method consistently outperforms existing algorithms in both activity classification and posture healthiness classification. Due to the low-cost and the easy deployment process of depth camera based motion sensors, our framework can be applied widely in home and office to facilitate smart environments

Interactive partner control in close interactions for real-time applications

This article presents a new framework for synthesizing motion of a virtual character in response to the actions performed by a user-controlled character in real time. In particular, the proposed method can handle scenes in which the characters are closely interacting with each other such as those in partner dancing and fighting. In such interactions, coordinating the virtual characters with the human player automatically is extremely difficult because the system has to predict the intention of the player character. In addition, the style variations from different users affect the accuracy in recognizing the movements of the player character when determining the responses of the virtual character. To solve these problems, our framework makes use of the spatial relationship-based representation of the body parts called interaction mesh, which has been proven effective for motion adaptation. The method is computationally efficient, enabling real-time character control for interactive applications. We demonstrate its effectiveness and versatility in synthesizing a wide variety of motions with close interactions