Design and analysis of polycentric prosthetic knee with enhanced kinematics and stability.

This paper describes a continuation of earlier work using the finite element method to conduct an engineering failure analysis of an existing polycentric prosthetic knee. The primary purpose of this work is to enhance the quality of the existing knee which has been reported with multiple cases of failure during its clinical practice in India. A modified design of the polycentric knee has been proposed based on the findings of failure analysis.

Application of Machine Learning Techniques for Characterization of Ischemic Stroke with MRI Images: A Review.

Magnetic resonance imaging (MRI) is a standard tool for the diagnosis of stroke, but its manual interpretation by experts is arduous and time-consuming. Thus, there is a need for computer-aided-diagnosis (CAD) models for the automatic segmentation and classification of stroke on brain MRI. The heterogeneity of stroke pathogenesis, morphology, image acquisition modalities, sequences, and intralesional tissue signal intensity, as well as lesion-to-normal tissue contrast, pose significant challenges to the development of such systems.

Conformity assessment with structural strength requirements of mechanical polycentric prosthetic knee used for amputee rehabilitation.

Prosthetic restoration is an important component of amputee rehabilitation which may be subjected to a static load of nearly five times of amputees' body weight and is continuously administered to cyclic or fatigue loads during its function. This study presents a structural strength analysis of polycentric mechanical prosthetic knee commonly used in National Institutes in India by finite element simulation and its experimental validation. Static and fatigue analyses have been performed to ensure its structural integrity as per the ISO 10328:2006 standard.

Deep Transfer Learning for Automatic Prediction of Hemorrhagic Stroke on CT Images.

Intracerebral hemorrhage (ICH) is the most common type of hemorrhagic stroke which occurs due to ruptures of weakened blood vessel in brain tissue. It is a serious medical emergency issues that needs immediate treatment. Large numbers of noncontrast-computed tomography (NCCT) brain images are analyzed manually by radiologists to diagnose the hemorrhagic stroke, which is a difficult and time-consuming process.

Detection of ventricular arrhythmia using hybrid time-frequency-based features and deep neural network.

Sudden cardiac death (SCD) is a major cause of death among patients with heart diseases. It occurs mainly due to ventricular tachyarrhythmia (VTA) which includes ventricular tachycardia (VT) and ventricular fibrillation (VF) conditions. The main challenging task is to predict the VTA condition at a faster rate and timely application of automatic external defibrillator (AED) for saving lives.

A systematic review on design technology and application of polycentric prosthetic knee in amputee rehabilitation.

The objective of this paper is to conduct a systematic review on design technology and clinical application of polycentric prosthetic knee joint in the rehabilitation of trans-femoral amputees. Relevant studies were identified using electronic database such as PubMed, EMBASE, SCOPUS and the Cochrane Controlled Trials Register (Rehabilitation and Related Therapies) up to February 2020. Screening of abstracts and application of inclusion and exclusion criteria were made.

Automated approach for detection of ischemic stroke using Delaunay Triangulation in brain MRI images.

It is difficult to develop an accurate algorithm to detect the stroke lesions using magnetic resonance imaging (MRI) images due to variation in different lesion sizes, variation in morphological structure, and similarity in intensity of lesion with normal brain in three types of stroke, namely partial anterior circulation syndrome (PACS), lacunar syndrome (LACS) and total anterior circulation stroke (TACS). In this paper, we have integrated the advantages of Delaunay triangulation (DT) and fractional order Darwinian particle swarm optimization (FODPSO), called DT-FODPSO technique for automatic segmentation of the structure of the stroke lesion. The approach was validated on 192 MRI images obtained from different stroke subjects.

ECG beat classification using empirical mode decomposition and mixture of features.

Computer-aided analysis is useful in predicting arrhythmia conditions of the heart by analysing the recorded ECG signals. In this work, we proposed a method to detect, extract informative features to classify six types of heartbeat of ECG signals obtained from the MIT-BIH Arrhythmia database. The powerful discrete wavelet transform (DWT) is used to eliminate different sources of noises.

Delineation of the ischemic stroke lesion based on watershed and relative fuzzy connectedness in brain MRI.

Precise segmentation of stroke lesions from brain magnetic resonance (MR) images poses a challenging task in automated diagnosis. In this paper, we proposed a new method called watershed-based lesion segmentation algorithm (WLSA), which is a novel intensity-based segmentation technique used to delineate infarct lesion in diffusion-weighted imaging (DWI) MR images of the brain. The algorithm was tested on a series of 142 real-time images collected from different stroke patients reported at IMS and SUM Hospital.

Blood vessel extraction of diabetic retinopathy using optimized enhanced images and matched filter.

Accurate extraction of structural changes in the blood vessels of the retina is an essential task in diagnosis of retinopathy. Matched filter (MF) technique is the effective way to extract blood vessels, but the effectiveness is reduced due to noisy images. The concept of MF and MF with first-order derivative of Gaussian (MF-FDOG) has been implemented for retina images of the DRIVE database.

Functional electrical stimulation on improving foot drop gait in poststroke rehabilitation: a review of its technology and clinical efficacy.

This article presents technical developments in and clinical applications of functional electrical stimulation (FES) in the recovery of gait and motor function in poststroke rehabilitation. We review stroke incidence, stimulator design, brain-computer interface-based FES systems, and clinical applications of FES. Developments in different types of foot drop stimulators are reviewed, including hard-wired and microprocessor-based surface stimulator systems.

Functional electrical stimulation of dorsiflexor muscle: effects on dorsiflexor strength, plantarflexor spasticity, and motor recovery in stroke patients.

Objective: To evaluate the therapeutic effects of Functional Electrical Stimulation (FES) of the tibialis anterior muscle on plantarflexor spasticity, dorsiflexor strength, voluntary ankle dorsiflexion, and lower extremity motor recovery with stroke survivors.

Design: We conducted a prospective interventional study.

Setting: Rehabilitation ward, physiotherapy unit and gait analysis laboratory.

Effect of functional electrical stimulation on the effort and walking speed, surface electromyography activity, and metabolic responses in stroke subjects.

Objective: To investigate the effects of functional electrical stimulation (FES) combined with conventional rehabilitation program on the effort and speed of walking, the surface electromyographic (sEMG) activity and metabolic responses in the management of drop foot in stroke subjects.

Methods: Fifteen patients with a drop foot resulting from stroke at least 3 months prior to the start of the trial took part in this study. All subjects were treated 1h a day, 5 days a week, for 12 weeks, including conventional stroke rehabilitation program and received 30 min of FES to the tibialis anterior (TA) muscle of the paretic leg in clinical settings.

Restoration of gait and motor recovery by functional electrical stimulation therapy in persons with stroke.

Purpose: To evaluate the clinical efficacy of functional electrical stimulation (FES) therapy of the tibialis anterior (TA) muscle on gait restoration and enhancing motor recovery with stroke patients.

Method: Thirty hemiparetic participants with spastic foot-drop impairments who were at least 3 months post-stroke were recruited from a rehabilitation institute and were assigned either to a control group or a FES group. Both the groups participated in a conventional stroke rehabilitation program for 60 min per day, 5 days a week, for 12-weeks.