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.

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.

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.