Sensing Movement: Microsensors for Body Motion Measurement

Recognition of body posture and motion is an important physiological function that can keep the body in balance. Man-made motion sensors have also been widely applied for a broad array of biomedical applications including diagnosis of balance disorders and evaluation of energy expenditure. This paper reviews the state-of-the-art sensing components utilized for body motion measurement. The anatomy and working principles of a natural body motion sensor, the human vestibular system, are first described. Various man-made inertial sensors are then elaborated based on their distinctive sensing mechanisms. In particular, both the conventional solid-state motion sensors and the emerging non solid-state motion sensors are depicted. With their lower cost and increased intelligence, man-made motion sensors are expected to play an increasingly important role in biomedical systems for basic research as well as clinical diagnostics

IMECE2008-67855 STUDY OF WHOLE BLOOD VISCOSITY USING A MICROFLUIDIC DEVICE

ABSTRACT Cardiovascular diseases include a wide range of disorders that affect heart and blood vessels, and are the leading cause of death in the Unite

Coupled Confusion Correction: Learning from Crowds with Sparse Annotations

As the size of the datasets getting larger, accurately annotating such datasets is becoming more impractical due to the expensiveness on both time and economy. Therefore, crowd-sourcing has been widely adopted to alleviate the cost of collecting labels, which also inevitably introduces label noise and eventually degrades the performance of the model. To learn from crowd-sourcing annotations, modeling the expertise of each annotator is a common but challenging paradigm, because the annotations collected by crowd-sourcing are usually highly-sparse. To alleviate this problem, we propose Coupled Confusion Correction (CCC), where two models are simultaneously trained to correct the confusion matrices learned by each other. Via bi-level optimization, the confusion matrices learned by one model can be corrected by the distilled data from the other. Moreover, we cluster the ``annotator groups'' who share similar expertise so that their confusion matrices could be corrected together. In this way, the expertise of the annotators, especially of those who provide seldom labels, could be better captured. Remarkably, we point out that the annotation sparsity not only means the average number of labels is low, but also there are always some annotators who provide very few labels, which is neglected by previous works when constructing synthetic crowd-sourcing annotations. Based on that, we propose to use Beta distribution to control the generation of the crowd-sourcing labels so that the synthetic annotations could be more consistent with the real-world ones. Extensive experiments are conducted on two types of synthetic datasets and three real-world datasets, the results of which demonstrate that CCC significantly outperforms state-of-the-art approaches. Source codes are available at: https://github.com/Hansong-Zhang/CCC.Comment: This work has been accepted by AAAI-2

M3D: Dataset Condensation by Minimizing Maximum Mean Discrepancy

Training state-of-the-art (SOTA) deep models often requires extensive data, resulting in substantial training and storage costs. To address these challenges, dataset condensation has been developed to learn a small synthetic set that preserves essential information from the original large-scale dataset. Nowadays, optimization-oriented methods have been the primary method in the field of dataset condensation for achieving SOTA results. However, the bi-level optimization process hinders the practical application of such methods to realistic and larger datasets. To enhance condensation efficiency, previous works proposed Distribution-Matching (DM) as an alternative, which significantly reduces the condensation cost. Nonetheless, current DM-based methods still yield less comparable results to SOTA optimization-oriented methods. In this paper, we argue that existing DM-based methods overlook the higher-order alignment of the distributions, which may lead to sub-optimal matching results. Inspired by this, we present a novel DM-based method named M3D for dataset condensation by Minimizing the Maximum Mean Discrepancy between feature representations of the synthetic and real images. By embedding their distributions in a reproducing kernel Hilbert space, we align all orders of moments of the distributions of real and synthetic images, resulting in a more generalized condensed set. Notably, our method even surpasses the SOTA optimization-oriented method IDC on the high-resolution ImageNet dataset. Extensive analysis is conducted to verify the effectiveness of the proposed method. Source codes are available at https://github.com/Hansong-Zhang/M3D.Comment: This work has been accepted by AAAI-2

IMECE2008-67924 BIOMECHANICAL DEVICE TOWARDS QUANTITATIVE MASSAGE

ABSTRACT Massage therapies are widely employed for improving and recovering tissue functions and physical activities. It i

Novel Common Genetic Susceptibility Loci for Colorectal Cancer

BACKGROUND: Previous genome-wide association studies (GWAS) have identified 42 loci (P < 5 × 10-8) associated with risk of colorectal cancer (CRC). Expanded consortium efforts facilitating the discovery of additional susceptibility loci may capture unexplained familial risk. METHODS: We conducted a GWAS in European descent CRC cases and control subjects using a discovery-replication design, followed by examination of novel findings in a multiethnic sample (cumulative n = 163 315). In the discovery stage (36 948 case subjects/30 864 control subjects), we identified genetic variants with a minor allele frequency of 1% or greater associated with risk of CRC using logistic regression followed by a fixed-effects inverse variance weighted meta-analysis. All novel independent variants reaching genome-wide statistical significance (two-sided P < 5 × 10-8) were tested for replication in separate European ancestry samples (12 952 case subjects/48 383 control subjects). Next, we examined the generalizability of discovered variants in East Asians, African Americans, and Hispanics (12 085 case subjects/22 083 control subjects). Finally, we examined the contributions of novel risk variants to familial relative risk and examined the prediction capabilities of a polygenic risk score. All statistical tests were two-sided. RESULTS: The discovery GWAS identified 11 variants associated with CRC at P < 5 × 10-8, of which nine (at 4q22.2/5p15.33/5p13.1/6p21.31/6p12.1/10q11.23/12q24.21/16q24.1/20q13.13) independently replicated at a P value of less than .05. Multiethnic follow-up supported the generalizability of discovery findings. These results demonstrated a 14.7% increase in familial relative risk explained by common risk alleles from 10.3% (95% confidence interval [CI] = 7.9% to 13.7%; known variants) to 11.9% (95% CI = 9.2% to 15.5%; known and novel variants). A polygenic risk score identified 4.3% of the population at an odds ratio for developing CRC of at least 2.0. CONCLUSIONS: This study provides insight into the architecture of common genetic variation contributing to CRC etiology and improves risk prediction for individualized screenin

Electrochemical micromachining of micro-dimple arrays on cylindrical inner surfaces using a dry-film photoresist

AbstractThe application of surface textures has been employed to improve the tribological performance of various mechanical components. Various techniques have been used for the application of surface textures such as micro-dimple arrays, but the fabrication of such arrays on cylindrical inner surfaces remains a challenge. In this study, a dry-film photoresist is used as a mask during through-mask electrochemical micromachining to successfully prepare micro-dimple arrays with dimples 94μm in diameter and 22.7μm deep on cylindrical inner surfaces, with a machining time of 9s and an applied voltage of 8V. The versatility of this method is demonstrated, as are its potential low cost and high efficiency. It is also shown that for a fixed dimple depth, a smaller dimple diameter can be obtained using a combination of lower current density and longer machining time in a passivating sodium nitrate electrolyte

Distribution Manner of Compaction Circular Cylinders in Through-Active-Mask Electrochemical Machining

Electrochemical machining is widely used in the processing of difficult-to-machine metal materials. And through-mask electrochemical machining is a very important technology in the processing array structure of difficult-to-cut metal materials. Traditional through-mask electrochemical machining always uses a photoresist as the mask. The production process of a mask is complicated, and the mask cannot be reused. In this paper, through-active-mask electrochemical machining to process array structure in difficult-to-machine metal materials was investigated. Compared with traditional electrochemical machining masks, a copper-clad laminate is used to make the mask by mechanical machining in through-active-mask electrochemical machining. Also, the mask does not stick together with the workpiece but covers the workpiece by mechanical compaction, so the mask can be reused. In order to ensure the mask is in close contact with the workpiece, we need to arrange many compaction circular cylinders within the flow channel. The influences on electrolyte flow of compaction circular cylinders were investigated. The distribution of the compaction circular cylinders affects the electrolyte flow state, thereby affecting the processing. By analyzing the electrolyte flow state for the different distributions of compaction circular cylinders, one can find the best distribution of compaction circular cylinders for the required processing

Some Nutritional Characteristics of a Local Landrace of Tepary Bean Seed from the Republic of Benin

The study was led to investigate the proximate nutritional value of white tepary bean from Benin, Africa. The proximate nutritional composition of Tepary bean seed such as crude protein, fat, and carbohydrate were determined by the use of standard methods of analysis, and minerals such as iron, manganese, calcium and magnesium were also determined by atomic absorption spectrometry. The result shows that white tepary bean contains 25.69% protein, 0.96% fat and 73.35% total carbohydrates, Iron (Fe) 0.6mg/100g; Manganese (Mn) 2.3 mg/100g; Magnesium (Mg) 51.8 mg/100g and Calcium (Ca) 48 mg/100g. Keywords: Proximate, minerals, fatty acids, white tepary bea