Modeling Spatial Relations of Human Body Parts for Indexing and Retrieving Close Character Interactions

Retrieving pre-captured human motion for analyzing and synthesizing virtual character movement have been widely used in Virtual Reality (VR) and interactive computer graphics applications. In this paper, we propose a new human pose representation, called Spatial Relations of Human Body Parts (SRBP), to represent spatial relations between body parts of the subject(s), which intuitively describes how much the body parts are interacting with each other. Since SRBP is computed from the local structure (i.e. multiple body parts in proximity) of the pose instead of the information from individual or pairwise joints as in previous approaches, the new representation is robust to minor variations of individual joint location. Experimental results show that SRBP outperforms the existing skeleton-based motion retrieval and classification approaches on benchmark databases

Pose-based Tremor Classification for Parkinson's Disease Diagnosis from Video

Parkinson's disease (PD) is a progressive neurodegenerative disorder that results in a variety of motor dysfunction symptoms, including tremors, bradykinesia, rigidity and postural instability. The diagnosis of PD mainly relies on clinical experience rather than a definite medical test, and the diagnostic accuracy is only about 73-84% since it is challenged by the subjective opinions or experiences of different medical experts. Therefore, an efficient and interpretable automatic PD diagnosis system is valuable for supporting clinicians with more robust diagnostic decision-making. To this end, we propose to classify Parkinson's tremor since it is one of the most predominant symptoms of PD with strong generalizability. Different from other computer-aided time and resource-consuming Parkinson's Tremor (PT) classification systems that rely on wearable sensors, we propose SPAPNet, which only requires consumer-grade non-intrusive video recording of camera-facing human movements as input to provide undiagnosed patients with low-cost PT classification results as a PD warning sign. For the first time, we propose to use a novel attention module with a lightweight pyramidal channel-squeezing-fusion architecture to extract relevant PT information and filter the noise efficiently. This design aids in improving both classification performance and system interpretability. Experimental results show that our system outperforms state-of-the-arts by achieving a balanced accuracy of 90.9% and an F1-score of 90.6% in classifying PT with the non-PT class.Comment: MICCAI 202

Social Interaction-Aware Dynamical Models and Decision Making for Autonomous Vehicles

Interaction-aware Autonomous Driving (IAAD) is a rapidly growing field of research that focuses on the development of autonomous vehicles (AVs) that are capable of interacting safely and efficiently with human road users. This is a challenging task, as it requires the autonomous vehicle to be able to understand and predict the behaviour of human road users. In this literature review, the current state of IAAD research is surveyed in this work. Commencing with an examination of terminology, attention is drawn to challenges and existing models employed for modelling the behaviour of drivers and pedestrians. Next, a comprehensive review is conducted on various techniques proposed for interaction modelling, encompassing cognitive methods, machine learning approaches, and game-theoretic methods. The conclusion is reached through a discussion of potential advantages and risks associated with IAAD, along with the illumination of pivotal research inquiries necessitating future exploration

A Feasibility Study on Image Inpainting for Non-cleft Lip Generation from Patients with Cleft Lip

A Cleft lip is a congenital abnormality requiring surgical repair by a specialist. The surgeon must have extensive experience and theoretical knowledge to perform surgery, and Artificial Intelligence (AI) method has been proposed to guide surgeons in improving surgical outcomes. If AI can be used to predict what a repaired cleft lip would look like, surgeons could use it as an adjunct to adjust their surgical technique and improve results. To explore the feasibility of this idea while protecting patient privacy, we propose a deep learning-based image inpainting method that is capable of covering a cleft lip and generating a lip and nose without a cleft. Our experiments are conducted on two real-world cleft lip datasets and are assessed by expert cleft lip surgeons to demonstrate the feasibility of the proposed method.Comment: 4 pages, 2 figures, BHI 202

Classification of different types of plastics using Deep Transfer Learning

Plastic pollution has affected millions globally. Research shows tiny plastics in the food we eat, the water we drink, and even in the air, we breathe. An average human intakes 74,000 micro-plastic every year, which sig- nificantly affects the health of living beings. This pollution must be administered before it severely impacts the world. We have substantially compared three state-of-the-art models on the WaDaBa dataset, which contains different types of plastics. These models are capable of classifying different types of plastic wastes which can be reused or recycled, thus limiting their wastage

A Multitier Deep Learning Model for Arrhythmia Detection

An electrocardiograph (ECG) is employed as a primary tool for diagnosing cardiovascular diseases (CVDs). ECG signals provide a framework to probe the underlying properties and enhance the initial diagnosis obtained via traditional tools and patient-doctor dialogs. Notwithstanding its proven utility, deciphering large data sets to determine appropriate information remains a challenge in ECG-based CVD diagnosis and treatment. Our study presents a deep neural network (DNN) strategy to ameliorate the aforementioned difficulties. Our strategy consists of a learning stage where classification accuracy is improved via a robust feature extraction protocol. This is followed by using a genetic algorithm (GA) process to aggregate the best combination of feature extraction and classification. Comparison of the performance recorded for the proposed technique alongside state-of-the-art methods reported the area shows an increase of 0.94 and 0.953 in terms of average accuracy and F1 score, respectively. The outcomes suggest that the proposed model could serve as an analytic module to alert users and/or medical experts when anomalies are detected

Mitochondrial Uncoupling Protein-2 (UCP2) Mediates Leptin Protection Against MPP+ Toxicity in Neuronal Cells

Mitochondrial dysfunction is involved in the pathogenesis of neurodegenerative diseases, including Parkinson’s disease (PD). Uncoupling proteins (UCPs) delink ATP production from biofuel oxidation in mitochondria to reduce oxidative stress. UCP2 is expressed in brain, and has neuroprotective effects under various toxic insults. We observed induction of UCP2 expression by leptin in neuronal cultures, and hypothesize that leptin may preserve neuronal survival via UCP2. We showed that leptin preserved cell survival in neuronal SH-SY5Y cells against MPP+ toxicity (widely used in experimental Parkinsonian models) by maintaining ATP levels and mitochondrial membrane potential (MMP); these effects were accompanied by increased UCP2 expression. Leptin had no effect in modulating reactive oxygen species levels. Stable knockdown of UCP2 expression reduced ATP levels, and abolished leptin protection against MPP+-induced mitochondrial depolarization, ATP deficiency, and cell death, indicating that UCP2 is critical in mediating these neuroprotective effects of leptin against MPP+ toxicity. Interestingly, UCP2 knockdown increased UCP4 expression, but not of UCP5. Our findings show that leptin preserves cell survival by maintaining MMP and ATP levels mediated through UCP2 in MPP+-induced toxicity

Vertebral rotation measurement: a summary and comparison of common radiographic and CT methods

Current research has provided a more comprehensive understanding of Adolescent Idiopathic Scoliosis (AIS) as a three-dimensional spinal deformity, encompassing both lateral and rotational components. Apart from quantifying curve severity using the Cobb angle, vertebral rotation has become increasingly prominent in the study of scoliosis. It demonstrates significance in both preoperative and postoperative assessment, providing better appreciation of the impact of bracing or surgical interventions. In the past, the need for computer resources, digitizers and custom software limited studies of rotation to research performed after a patient left the scoliosis clinic. With advanced technology, however, rotation measurements are now more feasible. While numerous vertebral rotation measurement methods have been developed and tested, thorough comparisons of these are still relatively unexplored. This review discusses the advantages and disadvantages of six common measurement techniques based on technology most pertinent in clinical settings: radiography (Cobb, Nash-Moe, Perdriolle and Stokes' method) and computer tomography (CT) imaging (Aaro-Dahlborn and Ho's method). Better insight into the clinical suitability of rotation measurement methods currently available is presented, along with a discussion of critical concerns that should be addressed in future studies and development of new methods

Measurement of αΩ in Ω− → ΛΚ− Decays

The HyperCP experiment (E871) at Fermilab has collected the largest sample of hyperon decays in the world. With a data set of over a million Ω− → ΛΚ− decays we have measured the product of αΩαΛ from which we have extracted αΩ. This preliminary result indicates that αΩ is small, but non‐zero. Prospects for a test of CP symmetry by comparing the α parameters in Ω− and Ω̄+ decays will be discussed. © 2003 American Institute of PhysicsPeer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/87682/2/251_1.pd