Foot Recognition Using Deep Learning for Knee Rehabilitation

The use of foot recognition can be applied in many medical fields such as the gait pattern analysis and the knee exercises of patients in rehabilitation. Generally, a camera-based foot recognition system is intended to capture a patient image in a controlled room and background to recognize the foot in the limited views. However, this system can be inconvenient to monitor the knee exercises at home. In order to overcome these problems, this paper proposes to use the deep learning method using Convolutional Neural Networks (CNNs) for foot recognition. The results are compared with the traditional classification method using LBP and HOG features with kNN and SVM classifiers. According to the results, deep learning method provides better accuracy but with higher complexity to recognize the foot images from online databases than the traditional classification method

Feature Selection and Casual Discovery For Ensemble Classifiers.

With rapid development of computer and information technology that can improve a large number of applications such as web text mining, intrusion detection, biomedical informatics, gene selection in micro array data, medical data mining, and clinical decision support systems, many information databases have been created. However, in some applications especially in the medical area, clinical data may contain hundreds to thousands of features with relatively few samples. A consequence of this problem is increased complexity that leads to degradation in efficiency and accuracy. Moreover, in this high dimensional feature space, many features are possibly irrelevant or redundant and should be removed in order to ensure good generalisation performance. Otherwise, the classifier may over-fit the data, that is the classifier may specialise on features which are not relevant for discrimination. To overcome this problem, feature selection and ensemble classification are applied. In this thesis, an empirical analysis on using bootstrap and random subspace feature selection for multiple classifier system is investigated and bootstrap feature selection and embedded feature ranking for ensemble MLP classifiers along with a stopping criterion based on the out-of-bootstrap estimate are proposed. Moreover, basically, feature selection does not usually take causal discovery into account. However, in some cases such as when the testing distribution is shifted from manipulation by external agent, causal discovery can provide some benefits for feature selection under these uncertainty conditions. It also can learn the underlying data structure, provide better understanding of the data generation process and better accuracy and robustness under uncertainty. Similarly, feature selection mutually enables global causal discovery algorithms to deal with high dimensional data by eliminating irrelevant and redundant features before exploring the causal relationship between features. A redundancy-based ensemble causal feature selection approach using bootstrap and random subspace and a comparison between correlation-based and causal feature selection for ensemble classifiers are analysed. Finally, hybrid correlation-causal feature selection for multiple classifier system is proposed in order to scale up causal discovery and deal with high dimensional features

Empirical Analysis of Feature Reduction in Deep Learning and Conventional Methods for Foot Image Classification

Deep learning algorithms are employed in many applications, especially in medical fields such as gait analysis and human pose detection for rehabilitation. However, creating the desired model with deep learning algorithms requires high memory and computing costs, which is problematic because deep learning technologies must be run on low-power devices such as edge computing equipment. To deal with these problems, feature reduction methods reduce the memory and energy costs. This paper presents an empirical analysis of deep learning with feature reduction. The method classifies foot images for knee rehabilitation using convolutional and dense autoencoders. The obtained results are compared with those of conventional methods (histograms of oriented gradients and local binary pattern algorithms). The features were classified and compared using support vector machine, k-nearest neighbor, and multilayer perceptron methods. The experimental results demonstrate that the conventional method uses fewer features than the deep learning method with higher accuracy because its algorithm projects pixels onto the histogram. In addition, using fewer features in deep learning layers maintains high accuracy, which is beneficial for edge computing implementations

Inter-rater and intra-rater reliability of isotonic exercise monitoring device for measuring active knee extension

Background The goal of this study was to assess the reliability of electromyography and range of motion measurements obtained using a knee exercise monitoring system. This device was developed to collect data on knee exercise activities. Methods Twenty healthy individuals performed isotonic quadriceps exercises in this study. The vastus medialis surface electromyography (sEMG) and range of motion (ROM) of the knee were recorded during the exercise using the isotonic knee exercise monitoring device, the Mobi6-6b, and a video camera system. Each subject underwent a second measuring session at least 24 h after the first session. To determine reliability, the intraclass correlation coefficients (ICCs) and standard error of measurement (SEM) at the 95% confidence interval were calculated, and a Bland–Altman analysis was performed. Results For inter-rater reliability, the ICCs of the mean absolute value (MAV) and root mean square (RMS) of sEMG were 0.73 (0.49, 0.86) and 0.79 (0.61, 0.89), respectively. ROM had an ICC of 0.93 (0.02, 0.98). The intra-rater reliability of the MAV of the sEMG was 0.89 (0.71, 0.96) and the intra-rater reliability of RMS of the sEMG was 0.88 (0.70, 0.95). The ROM between days had an intra-rater reliability of 0.82 (0.54, 0.93). The Bland–Altman analysis demonstrated no systematic bias in the MAV and RMS of sEMG, but revealed a small, systematic bias in ROM (−0.8311 degrees). Conclusion For sEMG and range of motion measures, the isotonic knee exercise monitoring equipment revealed moderate to excellent inter- and intra-rater agreement. However, the confidence interval of ROM inter-rater reliability was quite large, indicating a small agreement bias; hence, the isotonic knee exercise monitor may not be suitable for measuring ROM. This isotonic knee exercise monitor could detect and collect information on a patient’s exercise activity for the benefit of healthcare providers