Modeling and Simulation of an Assistive Exoskeleton Leg System with Knee Osteoarthritis and Quadriceps Weakness

The most common human locomotion problems such as quadriceps weakness, knee osteoarthritis can be healed up by using exoskeleton mechanisms with proper control systems. However, these kinds of abnormalities cannot be easily modeled in terms of engineering perspectives due to a lack of adequate data or unknown dynamics. Also, nature always seeks minimum energy as well as biology which means that the unknown dynamics can be built by using this phenomenon. In this study, a new system dynamic model had been involved in designing a simple single-legged exoskeleton robot mechanism and its control system to assist partially disabled individuals to improve their quality of locomotion. To determine the specific features of the human gait disorders to interpret their nature in the computer-aided simulation environment, knee osteoarthritis and quadriceps weakness, which are the common types of such problems, have been chosen as the main interests for this study. By using the lower limb model with anthropometric data, the simulations of disorders have been realized on MATLAB Simscape environment which enables us to model the entire exoskeleton system with the 3D parts of the human body. A model of a leg with the disorder was able to be obtained with the utilization of feedback linearization which is one of the examples of minimum principles in the control theory. A proper gait cycle is achieved with the exoskeleton application and separately for the leg, with approximately 10 deg deviation from the natural property in knee flexion. Finally, it can be seen that the system conversion into such problematic cases with or without an exoskeleton system is accomplished.

Inducing Vascular Grammars for Anomaly Classification in Brain Angiograms

As machine learning is used to make strides in med- ical diagnostics, few methods provide heuristics from which human doctors can learn directly. This work introduces a method for leveraging human observable structures, such as macro scale vascular formations, for producing assessments of medical conditions with rela- tively few training cases, and uncovering patterns that are potential diagnostic aids. The approach draws on shape grammars, a rule-based technique, pioneered in design and architecture, and accelerated through a re- cursive sub-graph mining algorithm. The distribution of rule instances in the data from which they are in- duced is then used as an intermediary representation en- abling common classification and anomaly detection ap- proaches to identify indicative rules with relatively small data sets. The method is applied to 7 Tesla time-of- flight (TOF) angiography MRI (n = 54) of human brain vasculature. The data were segmented and induced to generate representative grammar rules. Ensembles of rules were isolated to implicate vascular conditions reli- ably. This application demonstrates the power of auto- mated structured intermediary representations for as- sessing nuanced biological form relationships, and the strength of shape grammars, in particular for identify- ing indicative patterns in complex vascular networks.

Effect of Anatomical Sites On the Mechanical Properties of Spinal Dura Subjected to Biaxial Stretching

The spinal cord is encased by spinal meninges called the pia, arachnoid, and dura maters. Among these membranes, the dura mater is the thick and outermost layer and is the toughest and strongest. Thus, mechanical failure of the dura mater can lead to spontaneous cerebrospinal fluid leaks or hypovolemia, resulting in a complication or exacerbation of unfavorable symptoms involved in a mild traumatic brain injury. To develop protective equipment that can help prevent such injuries, accurate characterization of the spinal dura mater is required, especially regarding the mechanical properties at different anatomical sites. In this study, we used an equiload biaxial tensile tester to investigate the mechanical properties of porcine meningeal dura mater along the whole length of the spine. The resultant strain of the dorsal side was greater than that of the ventral side (P < 0.01), while the circumferential direction was significantly stiffer than the longitudinal direction (P < 0.01) at lower strains regardless of the spinal level. We also found that the material stiffness progressively increased from the cervical level to the thoracolumbar level at lower strains, which implies that the dura mater inherently possesses structurally preferred features or functions because the neck requires sufficient flexibility for daily activities. Further, Young's modulus was significantly less on the dorsal side than on the ventral side at higher strains (P < 0.05), suggesting that the dorsal side is readily elongated by spinal flexion even within the range of physiological motion.

A Novel QT Interval Analysis Method Based On Continuous Wavelet Transform and Philips Algorithm

QT surveillance is the most vital appliance to detect the possibility of sudden death sourced by using pro-arrhythmic drugs treating abnormal conditions in the heart. The repolarization of ventricles makes QT interval surveillance difficult since noisy conditions and individual cardiac situations. Besides, an automated QT algorithm is crucial due to a manual QT measurement with some disadvantages such as fatigue condition in reading long records. In this study, a fully novel automated method combining Continuous Wavelet Transform and Philips method was established to perform QT interval analysis. ECG recordings were obtained from PhyisoNet database marked by manual and standard automated methods. The proposed algorithm had scores of 15.46 and 11.87 millisecond mean error with 11.85 and 9.91 millisecond standard deviation in terms of gold and silver standards, respectively. Also, the entire QT database was utilized in order to test the algorithm performance with the score of 12.89 and 9.76 millisecond mean and standard deviation errors, respectively. The present algorithm performance had scores of -0.21±7.81 at golden standard, and -4.10±18.21 millisecond error for the whole QT database tests, respectively. The proposed algorithm is attained to more stable and robust results with a higher performance than the previous comparable studies.

Parameter Identification of Cardiovascular System Model Used for Left Ventricular Assist Device Algorithms

Advancement of implanted Left Ventricular Assist Device (LVAD) technology includes modern sensing and control methods to enable online diagnostics and monitoring of patients using on-board sensors. These methods often rely on a cardiovascular system (CVS) model, the parameters of which must be identified for the specific patient. Some of these, such as the Systemic Vascular Resistance (SVR), can be estimated online while others must be identified separately. This paper describes a three-staged approach for designing a parameter identification algorithm (PIA) for this problem. The approach is demonstrated using a two-element Windkessel model of the systemic circulation with a time-varying elastance for the left ventricle. A parameter identifiability stage is followed by identification using an unscented Kalman filter (UKF) which uses measurements of left ventricle pressure (Plv), aortic pressure (Pao), aortic flow (Qa), and known input measurement of LVAD flow rate (Qvad). Both simulation and experimental data from animal experiments were used to evaluate the presented methods. By bounding the initial guess for left ventricular volume, the identified CVS model is able to reproduce signals of Plv, Pao and Qa within a normalized root mean squared error (nRMSE) of 5.1 %, 19 %, and 11 %, respectively during simulations. Experimentally, the identified model is able to estimate SVR with an accuracy of 0.56 % compared with values from invasive measurements. Diagnostics and physiological control algorithms on-board modern LVADs could use CVS models other than those shown here, and the presented approach is easily adaptable to them. The methods also demonstrate how to test the robustness and accuracy of the identification algorithm.

Diagnostic of Injury Risk in the Anterior Cruciate Ligament Based On Shape Context Description of the Intercondylar Notch Curvature

An estimated number of 300,000 new anterior cruciate ligament (ACL) injuries occur each year in the United States. Although several magnetic resonance (MR) imaging-based ACL diagnostics methods have already been proposed in the literature, most of them are based on machine learning or deep learning strategies, which are computationally expensive. In this paper, we propose a diagnostics framework for the risk of injury in the anterior cruciate ligament (ACL) based on the application of the inner-distance shape context (IDSC) to describe the curvature of the intercondylar notch from MR images. First, the contours of the intercondylar notch curvature from 91 MR images of the distal end of the femur (70 healthy and 21 with confirmed ACL injury) were extracted manually using standard image processing tools. Next, the IDSC was applied to calculate the similarity factor between the extracted contours and reference standard curvatures. Finally, probability density functions of the similarity factor data were obtained through parametric statistical inference, and the accuracy of the ACL injury risk diagnostics framework was assessed using receiver operating characteristic analysis (ROC). The overall results for the area under the curve (AUC) showed that method reached a maximum accuracy of about 66%. Furthermore, the sensitivity and specificity results showed that an optimum discrimination threshold value for the similarity factor can be pursued that minimizes the incidence of false positives and false positives simultaneously.

Harvesting Brain Signal using Machine Learning Methods

Brain computer interface (BCI) systems are developed in biomedical fields to increase the quality of life. The development of a six class BCI controller to operate a semi-autonomous robotic arm is presented. The controller uses the following mental tasks: imagined left/right hand squeeze, imagined left/right foot tap, rest, one physical task, and jaw clench. To design a controller, the locations of active electrodes are verified and an appropriate machine learning algorithm is determined. Three subjects, ages ranging between 22-27, participated in five sessions of motor imagery experiments to record their brainwaves. These recordings were analyzed using event related potential plots and topographical maps to determine active electrodes. BCILAB was used to train two, three, five, and six class BCI controllers using linear discriminant analysis (LDA) and relevance vector machine (RVM) machine learning methods. The subjects' data was used to compare the two-method's performance in terms of error rate percentage. While a two class BCI controller showed the same accuracy for both methods, the three and five class BCI controllers showed the RVM approach having a higher accuracy than the LDA approach. For the five-class controller, error rate percentage was 33.3% for LDA and 29.2% for RVM. The six class BCI controller error rate percentage for both LDA and RVM was 34.5%. While the percentage values are the same, RVM was chosen as the desired machine learning algorithm based on the trend seen in the three and five class controller performances.

Reduction of Reflected Light On Fluorescence Emission for Indocyanine Green

Inside the brain tumor, the blood vessels are intricately composed, and the tumors and blood vessels are similar in color. Therefore, when observing tumors and blood vessels with the naked eye or a surgical microscope, it is difficult to distinguish between tumors and blood vessels. Fluorescence staining with indocyanine green (ICG) is performed to distinguish between brain tumors and blood vessels using a surgical microscope. However, when observing the blood circulation state of a tumor or blood vessel through a surgical microscope, light reflection occurs from the camera. In the process of observing the state of the blood vessel, due to the occurrence of light reflection, an obstruction phenomenon in which the observation field is blocked by the blood vessel of the object to be observed occurs. Therefore, it is difficult to diagnose the vascular condition. In this experiment, the 780nm light-emitting diode (LED) was irradiated to the ICG phantom, and then, when the fluorescence expression image was observed, the polarizing filter such as circular polarized light (CPL) filter and linear polarized light (LPL) filter were inserted into the camera and the reflected light was reduced. Therefore, it is possible to reduce the reflected light from the fluorescence expression image by using a polarizing filter, and it is expected to be applicable to surgery and diagnostic fields of cancer such as surgery.

Dual Fluorescence Emission by Irradiation of Single Optical Source for Fluorescein Sodium and 5-ALA Applications

Most brain surgeries aim to completely resection a tumor. However, the arrangement of blood vessels around brain tumors is often complex. Moreover, the tumors and blood vessels have similar colors, making it difficult to identify the boundaries between them with the naked eye. Fluorescent staining is a method used to distinguish the borders between brain tumors and blood vessels. The fluorescent contrast agents commonly used to observe tumors are 5-aminolevulinic acid (5-ALA) and fluorescein sodium (FS), which have different surgical sensitivities, depending on the type of tumor. In this article, a dual band band-pass filter (BPF) with dual-wavelength emission for 5-ALA and FS is designed, and the dual-band BPF capable of inducing simultaneous fluorescence emission of FS and 5-ALA was investigated experimentally to improve accuracy, speed, and energy efficiency in clinical settings. The possibility of dual fluorescence emission with a single irradiation is proposed. The proposed fluorescent dual-band filter has the advantage of saving energy, reducing auxiliary manpower and unit costs, and reducing operating room space requirements by producing two fluorescence diagnostic effects using a single equipment.

Manufacturing of a Gelatin Phantom with Lymphedema for Ultrasonic Imaging Measurement

For diagnosis of the secondary lymphedema, amplitude mode (A-mode) examination using a single ultrasound probe has been suggested as one of possible diagnostic modalities due to its relatively low cost, ease of usage, and mobility. However, A-mode ultrasound waves with respect to time have lots of noise and are complicated to analyze and achieve well correlated information related to change in volume of each layer of skin and subcutaneous tissues. Thus, development of adequate ultrasound calibration phantom is needed. For this, fundamental study on proper phantom materials which show acoustic characteristics of skin and subcutaneous tissues are needed. In this research, the fabrication method for ultrasonic phantom using gelatin material is presented in a wide range of acoustic impedance and their acoustic characteristics and usability were discussed.