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. Simulation-based comparative analysis of polycentric knees has been performed as per the ISO 10328:2016 standard in terms of stress distribution, total contour deformation, safety factor, and fatigue life. The upper extension lever is subjected to static and cyclic loads of 4130 and 1230 N, whereas the lower plate has a translational constraint. The modified polycentric knee prosthesis outperforms static and fatigue strength tests. The standard of the existing knee prosthesis has significantly improved as a result of design variations and integration of high-strength and lightweight aluminium 7075-T6 alloy. The modified polycentric knee prosthesis has a predicted maximum deformation of less than 0.7 mm and a minimum safety factor between 1.7 and 2 compared to 2.66 mm and 1.0 for the existing knee prosthesis. Based on the fatigue simulation results, it is predicted that the modified polycentric knee will have a lifespan of at least ten years indicating a safe design. It has improved alignment stability and kinematics, with a significant weight reduction of 33 g, and a high cost-benefit ratio to reach the maximum amputee population in low-income countries like India.

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. Accurate dimensioning of knee components have been obtained using coordinate measuring machine and the 3 D CAD model has been generated by CATIA V5 from the 2 D geometry. The model is imported to the ANSYS 20.1 workbench to study stress distribution in the knee for ensuring its safety performance. The selection of reference planes, application of calculated loads, and position of load line have been done as per the ISO test procedure. Static and cyclic loadings of 4130 N and 1230 N are applied at the top and the bottom plate is given with translational constraints to limit its movement in any direction. Results indicate that the prosthetic knee model is moderately strong enough to outstrip the static strength test. However, the calculated strain and predicted fatigue life during the cyclic test suggest that this knee unit has poor fatigue strength. Validation results with an average error percentage of 3.44 and 10 show higher reliability based on previous study results and experimental tests, respectively.

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. Machine learning (ML) is increasingly being used in predictive neuroimaging diagnosis and prognostication. This paper reviews image processing and machine learning techniques that have been applied to detect ischemic stroke on brain MRI, including details on image acquisition, pre-processing, techniques to segment, extraction of features, and classification into stroke types. The main objective of this work is to find the state-of-art machine learning techniques used to predict the ischemic stroke and their application in clinical set-up. The article selection is performed according to PRISMA guideline. The state-of-the-art on automated MRI stroke diagnosis, with a focus on machine learning, is discussed, along with its advantages and limitations. We found that the various machine learning models discussed in this article are able to detect the infarcts with an acceptable accuracy of 70-90%. However, no one has highlighted the time complexity to predict the stroke in the model developed, which is an important factor. The work concludes with proposals for future recommendations for building efficient and robust deep learning (DL) models for quantitative brain MRI analysis. In recent work, with the application of DL approaches, using large datasets to train the models has improved the detection accuracy and reduced computational complexity. We suggest that the design of a decision support system based on artificial intelligence (AI) and clinical data presenting symptoms is essential to support clinicians to accelerate diagnosis and timeous therapy in the emergency management of stroke.

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. In this study, we propose an automated transfer deep learning method that combines ResNet-50 and dense layer for accurate prediction of intracranial hemorrhage on NCCT brain images. A total of 1164 NCCT brain images were collected from 62 patients with hemorrhagic stroke from Kalinga Institute of Medical Science, Bhubaneswar and used for evaluating the model. The proposed model takes individual CT images as input and classifies them as hemorrhagic or normal. This deep transfer learning approach reached 99.6% accuracy, 99.7% specificity, and 99.4% sensitivity which are better results than that of ResNet-50 only. It is evident that the deep transfer learning model has advantages for automatic diagnosis of hemorrhagic stroke and has the potential to be used as a clinical decision support tool to assist radiologists in stroke diagnosis.

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. In this study, a VF/VT classification scheme has been proposed using a deep neural network (DNN) approach using hybrid time-frequency-based features. Two annotated public domain ECG databases (CUDB and VFDB) were used as training, test, and validation of datasets. The main motivation of this study was to implement a deep learning model for the classification of the VF/VT conditions and compared the results with other standard machine learning algorithms. The signal is decomposed with the wavelet transform, empirical mode decomposition (EMD) and variable mode decomposition (VMD) approaches and twenty-four are extracted to form a hybrid model from a window of length 5 s length. The DNN classifier achieved an accuracy (Acc) of 99.2%, sensitivity (Se) of 98.8%, and specificity (Sp) of 99.3% which is comparatively better than the results of the standard classifier. The proposed algorithm can detect VTA conditions accurately, hence could reduce the rate of misinterpretations by human experts and improves the efficiency of cardiac diagnosis by ECG signal analysis.

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. Design, modeling, material use, kinematic study, simulation technique and clinical application of polycentric knee models used in many developed and developing countries have been reviewed. Out of 516 potentially relevant studies, 43 articles were included. Specific variables on technical and clinical aspects were extracted and added to summary tables. The results reveal that polycentric knees have a variety of geometries but the methods for comparing their performances are rare. The data of structural analysis using different simulation techniques are validated with experimental results for determining model accuracy. Gait analysis using the polycentric knee components provides a valid tool to correlate with experimental results. There are well-designed studies on the technological development of polycentric knees, however, high-quality clinical researches are scarce. Conventional clinical knowledge had considerable gaps concerning the effects of polycentric knee and their mechanical characteristics on human functioning with a lower-limb prosthesis. Still, further research is needed to develop and implement standardized measures on prosthetic knee joints for their effective use, function, durability, and cost-effectiveness.

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. Statistical and morphological features were extracted and classified according to the Oxfordshire community stroke project (OCSP) using support vector machine (SVM) and random forest (RF) classifiers. The method effectively detected the stroke lesions and achieved promising results with an average sensitivity of 0.93, accuracy of 0.95, JI of 0.89 and Dice similarity index of 0.93 using RF classifier. These promising results indicates the DT based optimized approach is efficient in detecting ischemic stroke and it can aid the neuro-radiologists to validate their routine screening.

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. One MRI slice having largest area of infract lesion is selected from each patient from multiple slices. The main objective is to combine the strength of guided filter and watershed transform through relative fuzzy connectedness (RFC) to detect lesion boundaries appropriately. The extracted informative statistical and geometrical features are used to classify the types of stroke lesions according to the Oxfordshire Community Stroke Project (OCSP) classification. The experimental results demonstrated the effectiveness of the proposed process with high accuracy in delineating lesions. A classification with a dice similarity index (DSI) of 96% with computational time of 0.06 s in random forest (RF) and an accuracy of 85% with computational time of 0.84 s has been obtained by multilayer perceptron (MLP) neural network classifier in tenfold cross-validation process. Better detection accuracy is achieved in RF classifier in classifying stroke lesions.

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. Empirical mode decomposition (EMD) with adaptive thresholding has been used to detect precise R-peaks and QRS complex. The significant features consists of temporal, morphological and statistical were extracted from the processed ECG signals and combined to form a set of features. This feature set is classified with probabilistic neural network (PNN) and radial basis function neural network (RBF-NN) to recognise the arrhythmia beats. The process achieved better result with sensitivity of 99.96%, and positive predictivity of 99.81 with error rate of 0.23% in detecting the QRS complex. In class-oriented scheme, the arrhythmia conditions are classified with accuracy of 99.54%, 99.89% using PNN and RBF-NN classifier respectively. The obtained result confirms the superiority of the proposed scheme compared to other published results cited in literature.

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. The optimized particle swarm optimization (PSO) algorithm is used for enhancing the images by edgels to improve the performance of filters. The vessels were detected by the response of thresholding to the MF, whereas the threshold is adjusted in response to the FDOG. The PSO-based enhanced MF response significantly improved the performances of filters to extract fine blood vessels structures. Experimental results show that the proposed method based on enhanced images improved the accuracy to 91.1%, which is higher than that of MF and MF-FDOG, respectively. The peak signal-to-noise ratio was also found to be higher with low mean square error values in enhanced MF response. The accuracy, sensitivity, and specificity values are significantly improved among MF, MF-FDOG, and PSO-enhanced images ([Formula: see text]).

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. The replacement of the foot switch by using artificial and "natural" sensors as the primary control in foot drop stimulators is reviewed. In addition, this review evaluates the clinical effects of FES applications in gait, motor control, and functional ability compared to conventional therapy alone during poststroke rehabilitation. The literature suggests the combination of FES and a conventional rehabilitation program has a positive therapeutic effect on the recovery of gait, motor function, energy expenditure, and functional ability in stroke patients. On the basis of our review, we recommend using FES therapy along with a conventional rehabilitation program in the poststroke rehabilitation process. In summary, this article describes the need for rigorous technological development, clinical studies, and collaboration between clinicians and engineers for FES systems. Future research would facilitate the design of costeffective FES systems as well as analysis of FES applications in stroke patients to optimize the rehabilitation process.

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. PARTICIPANTS: Fifty-one patients with foot drop resulting from stroke. INTERVENTION: The functional electrical stimulation (FES) group (n=27) received 20-30 minutes of electrical stimulation to the peroneal nerve and anterior tibial muscle of the paretic limb along with conventional rehabilitation program (CRP). The control group (n=24) treated with CRP only. The subjects were treated 1 hr per day, 5 days a week, for 12 weeks. MAIN OUTCOME MEASURES: Plantarflexor spasticity measured by modified ashworth scale (MAS), dorsiflexion strength measured by manual muscle test (MMT), active/passive ankle joint dorsiflexion range of motion, and lower-extremity motor recovery by Fugl-Meyer assessment (FMA) scale. RESULTS: After 12 weeks of treatment, there was a significant reduction in a plantarflexor spasticity by 38.3% in the FES group and 21.2% in control group (P< 0.05), between the beginning and end of the trial. Dorsiflexor muscle strength was increased significantly by 56.6% and 27.7% in the FES group and control group, respectively. Similarly, voluntary ankle dorsiflexion and lower-extremity motor function improved significantly in both the groups. No significant differences were found in the baseline measurements among groups. When compared with control group, a significant improvement (p< 0.05) was measured in all assessed parameters in the FES group at post-treatment assessment, thus FES therapy has better effect on recovery process in post-stroke rehabilitation. CONCLUSIONS: Therapy combining FES and conventional rehabilitation program was superior to a conventional rehabilitation program alone, in terms of reducing spasticity, improving dorsiflexor strength and lower extremity motor recovery in stroke patients.

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. Baseline and post-treatment measurements were made for temporal and spectral EMG parameters of TA muscle, walking speed, the effort of walking as measured by physiological cost index (PCI) and metabolic responses. RESULTS: The experimental results showed a significant improvement in mean-absolute-value (21.7%), root-mean-square (66.3%) and median frequency (10.6%) of TA muscle EMG signal, which reflects increased muscle strength. Mean increase in walking speed was 38.7%, and a reduction in PCI of 34.6% between the beginning and at end of the trial. Improvements were also found in cardiorespiratory responses with reduction in oxygen consumption (24.3%), carbon dioxide production (19.9%), heart rate (7.8%) and energy cost (22.5%) while walking with FES device. CONCLUSIONS: The results indicate that the FES may be a useful therapeutic tool combined with conventional rehabilitation program to improve the muscle strength, walking ability and metabolic responses in the management of drop foot with stroke patients.

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. The FES group received the electrical stimulation to the TA muscle for correction of foot-drop. RESULTS: Functional electric stimulation (FES) resulted in a 26.3% (p < 0.001) improvement of walking speed measured with 10-m walkway, whereas the improvement in the control group was only 11.5% (p < 0.01). The FES group also showed significantly greater improvements compared to control group in other gait parameters (e.g. cadence, step length), physiological cost index (PCI), ankle range of motion, spasticity of calf muscle, Fugl-Meyer scores, and the maximum value of the root mean square (RMS(max)), which reflects the capacity of the muscle output. CONCLUSIONS: These findings suggest that, the FES therapy combined with conventional therapy treatment more effectively improves the walking ability and enhances the motor recovery when compared with conventional therapy alone in stroke survivors.