Special Issue "Computer Methods in Bio-mechanics and Biomedical Engineering"

Deadline: 30 October 2019 (closed)
Guest Editors
Professor Lulu Wang, Hefei University of Technology
Professor Xiaoning Jiang, North Carolina State University
Professor Lindong Yu, Hefei University of Technology
Professor Linxia Gu, University of Nebraska-Lincoln

Summary

This special issue focuses on the implementation of various engineering principles in the conception, design, development, analysis and operation of biomedical and biotechnological systems and applications. The special issue aims to promote solutions of excellence for biomedical data and establishes links among engineers, researchers, and clinicians. 


This special issue offers a comprehensive forum for discussion of the current state-of-the-art in the scientific fields related to bio-mechanics and biomedical technologies, including, but not limited to:

1.Computational Modeling in Biomedical Applications
2.Computer Aided Diagnosis, Surgery, Therapy and Treatment
3.Data Processing and Analysis
4.Injury and Damage Bio-mechanics
5.Vibration and Acoustics in Biomedical Applications
6.Biomedical Imaging, Therapy and Tissue Characterization
7.Biomaterials and Tissue: Modelling, Synthesis, Fabrication and Characterization
8.Biomedical Devices
9.Dynamics and Control of Biomechanical Systems
10.Clinical Applications of Bioengineering
11.Musculoskeletal and Sports Bio-mechanics
12.Sensors and Actuators
13.Robotics, Rehabilitation
14.Data Processing and Analysis
15.Virtual Reality
16.Visual Data Mining and Knowledge Discovery
17.Software Development for Bio-mechanics and Biomedical Engineering


Keywords
Biomedical imaging; Biomedical Devices; CAD; Biosensors;

Published Papers
  • A Geometrical Approach to Compute Upper Limb Joint Stiffness
  • Abstract Exoskeletons are designed to control the forces exerted during the physical coupling between the human and the machine. Since the human is an active system, the control of an exoskeleton requires coordinated action between the machine and the load so to obtain a reciprocal adaptation. Humans in the control loop can be modeled as active mechanical loads whose stiffness is continuously changing. The direct measurement of human stiffness is difficult to obtain in real-time, thus posing a significant limitation to the design of wearable robotics controllers. Electromyographic (EMG) recordings can provide an indirect estimation of human muscle force and stiffness,… More
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  • Automatic Sleep Staging Algorithm Based on Random Forest and Hidden Markov Model
  • Abstract In the field of medical informatics, sleep staging is a challenging and timeconsuming task undertaken by sleep experts. According to the new standard of the American Academy of Sleep Medicine (AASM), the stages of sleep are divided into wakefulness (W), rapid eye movement (REM) and non-rapid eye movement (NREM) which includes three sleep stages (N1, N2 and N3) that describe the depth of sleep. This study aims to establish an automatic sleep staging algorithm based on the improved weighted random forest (WRF) and Hidden Markov Model (HMM) using only the features extracted from double-channel EEG signals. The WRF classification model… More
  •   Views:154       Downloads:134        Download PDF

  • A Joint Delay-and-Sum and Fourier Beamforming Method for High Frame Rate Ultrasound Imaging
  • Abstract Frame rate is an important metric for ultrasound imaging systems, and high frame rates (HFR) benefit moving-target imaging. One common way to obtain HFR imaging is to transmit a plane wave. Delay-and-sum (DAS) beamformer is a conventional beamforming algorithm, which is simple and has been widely implemented in clinical application. Fourier beamforming is an alternative method for HFR imaging and has high levels of imaging efficiency, imaging speed, and good temporal dynamic characteristics. Nevertheless, the resolution and contrast performance of HFR imaging based on DAS or Fourier beamforming are insufficient due to the single plane wave transmission. To address this… More
  •   Views:197       Downloads:165        Download PDF

  • Grading Method for Hypoxic-Ischemic Encephalopathy Based on Neonatal EEG
  • Abstract The grading of hypoxic-ischemic encephalopathy (HIE) contributes to the clinical decision making for neonates with HIE. In this paper, an automated grading method based on electroencephalogram (EEG) data is proposed to describe the severity of HIE infants, namely mild asphyxia, moderate asphyxia and severe asphyxia. The automated grading method is based on a multi-class support vector machine (SVM) classifier, and the input features of SVM classifier include long-term features which are extracted by decomposing the EEG data into different 64 s epoch data and short-term features which are extracted by segmenting the 64 s epoch data into 8 s epoch… More
  •   Views:268       Downloads:212        Download PDF