Automated design of robust discriminant analysis classifier for foot pressure lesions using kinematic data

In the recent years, the use of motion tracking systems for acquisition of functional biomechanical gait data, has received increasing interest due to the richness and accuracy of the measured kinematic information. However, costs frequently restrict the number of subjects employed, and this makes the dimensionality of the collected data far higher than the available samples. This paper applies discriminant analysis algorithms to the classification of patients with different types of foot lesions, in order to establish an association between foot motion and lesion formation. With primary attention to small sample size situations, we compare different types of Bayesian classifiers and evaluate their performance with various dimensionality reduction techniques for feature extraction, as well as search methods for selection of raw kinematic variables. Finally, we propose a novel integrated method which fine-tunes the classifier parameters and selects the most relevant kinematic variables simultaneously. Performance comparisons are using robust resampling techniques such as Bootstrap$632+$and k-fold cross-validation. Results from experimentations with lesion subjects suffering from pathological plantar hyperkeratosis, show that the proposed method can lead to$sim 96$correct classification rates with less than 10% of the original features

Footwear-integrated force sensing resistor sensors: A machine learning approach for categorizing lower limb disorders

Lower limb disorders are a substantial contributor to both disability and lower standards of life. The prevalent disorders affecting the lower limbs include osteoarthritis of the knee, hip, and ankle. The present study focuses on the use of footwear that incorporates force-sensing resistor sensors to classify lower limb disorders affecting the knee, hip, and ankle joints. The research collected data from a sample of 117 participants who wore footwear integrated with force-sensing resistor sensors while walking on a predetermined walkway of 9 meters. Extensive preprocessing and feature extraction techniques were applied to form a structured dataset. Several machine learning classifiers were trained and evaluated. According to the findings, the Random Forest model exhibited the highest level of performance on the balanced dataset with an accuracy rate of 96%, while the Decision Tree model achieved an accuracy rate of 91%. The accuracy scores of the Logistic Regression, Gaussian Naive Bayes, and Long Short-Term Memory models were comparatively lower. K-fold cross-validation was also performed to evaluate the models’ performance. The results indicate that the integration of force-sensing resistor sensors into footwear, along with the use of machine learning techniques, can accurately categorize lower limb disorders. This offers valuable information for developing customized interventions and treatment plans

Machine learning for brain stroke: a review

Machine Learning (ML) delivers an accurate and quick prediction outcome and it has become a powerful tool in health settings, offering personalized clinical care for stroke patients. An application of ML and Deep Learning in health care is growing however, some research areas do not catch enough attention for scientific investigation though there is real need of research. Therefore, the aim of this work is to classify state-of-arts on ML techniques for brain stroke into 4 categories based on their functionalities or similarity, and then review studies of each category systematically. A total of 39 studies were identified from the results of ScienceDirect web scientific database on ML for brain stroke from the year 2007 to 2019. Support Vector Machine (SVM) is obtained as optimal models in 10 studies for stroke problems. Besides, maximum studies are found in stroke diagnosis although number for stroke treatment is least thus, it identifies a research gap for further investigation. Similarly, CT images are a frequently used dataset in stroke. Finally SVM and Random Forests are efficient techniques used under each category. The present study showcases the contribution of various ML approaches applied to brain stroke.info:eu-repo/semantics/publishedVersio

In Vivo Mechanics of Cam-Post Engagement in Fixed and Mobile Bearing TKA and Vibroarthrography of the Knee Joint

The objective of this dissertation was to determine the mechanics of the cam-post mechanism for subjects implanted with a Rotating Platform (RP) PS TKA, Fixed Bearing (FB) PS TKA or FB Bi-Cruciate Stabilized (BCS) TKA. Additionally, a secondary goal of this dissertation was to investigate the feasibility of vibroarthrography in correlating in-vivo vibrations with features exhibited in native, arthritic and implanted knees. In-vivo, 3D kinematics were determined for subjects implanted with nine knees with a RP-PS TKA, five knees with a FB-PS TKA, and 10 knees with a FB-BCS TKA, while performing a deep knee bend. Distance between the cam-post surfaces was monitored throughout flexion and the predicted contact map was calculated. A forward dynamic model was constructed for 3 test cases to determine the variation in the nature of contact forces at the cam-post interaction. Lastly, a different set of patients was monitored using vibroarthrography to determine differences in vibration between native, arthritic and implanted knees. Posterior cam-post engagement occurred at 34° for FB-BCS, 93o for FB-PS and at 97° for RP-PS TKA. In FB-BCS and FB-PS knees, the contact initially occurred on the medial aspect of the tibial post and then moved centrally and superiorly with increasing flexion. For RP-PS TKA, it was located centrally on the post at all times. Force analysis determined that the forces at the cam-post interaction were 1.6*body-weight, 2.0*body-weight, and 1.3*body-weight for the RP-PS, FB-BCS and FB-PS TKA. Sound analysis revealed that there were distinct differences between native and arthritic knees which could be differentiated using a pattern classifier with 97.5% accuracy. Additionally, vibrations from implanted knees were successfully correlated to occurrences such as lift-off and cam-post engagement. This study suggests that mobility of the polyethylene plays a significant role in ensuring proper cam-post interaction in RP-PS TKA. The polyethylene insert rotates axially in accord with the rotating femur, maintaining central cam-post contact. This phenomenon was not observed in the FB-BCS and FB-PS TKAs

Examining the Trend of Literature on Classification Modelling: A Bibliometric Approach

This paper analyses and reports various types of published works related to classification or discriminant modelling. This paper adopted a bibliometric analysis based on the data obtained from the Scopus online database on 27th July 2019. Based on the ‘keywords’ search results, it yielded 2775 valid documents for further analysis. For data visualisation purposes, we employed VOSviewer. This paper reports the results using standard bibliometric indicators, particularly on the growth rate of publications, research productivity, analysis of the authors and citations. The outcomes revealed that there is an increased growth rate of classification literature over the years since 1968. A total of 2473 (89.12%) documents were from journals (n=1439; 51.86%) and conference proceedings (n=1034; 37.26%) contributed as the top publications in this classification topic. Meanwhile, 2578 (92.9%) documents are multi-authored with an average collaboration index of 3.34 authors per article. However, this classification research field found that the famous numbers of authors’ collaboration in a document are two (with n=758; 27.32%), three (n=752; 27.10%) and four (n=560; 20.18%) respectively. An analysis by country, China with 1146 (41.30%) published documents thus is ranked first in productivity. With respect to the frequency of citations, Bauer and Kohavi (1999)’s article emerged as the most cited article through 1414 total citations with an average of 70.7 citations per year. Overall, the increasing number of works on classification topics indicates a growing awareness of its importance and specific requirements in this research field

3D video based detection of early lameness in dairy cattle

Lameness is a major issue in dairy cattle and its early and automated detection offers animal welfare benefits together with potentially high commercial savings for farmers. Current advancements in automated detection have not achieved a sensitive measure for classifying early lameness; it remains to be a key challenge to be solved. The state-of-the-art also lacks behind on other aspects e.g. robust feature detection from a cow's body and the identification of the lame leg/side. This multidisciplinary research addresses the above issues by proposing an overhead, non-intrusive and covert 3-Dimensional (3D) video setup. This facilitates an automated process in order to record freely walking Holstein dairy cows at a commercial farm scale, in an unconstrained environment.The 3D data of the cow's body have been used to automatically track key regions such as the hook bones and the spine using a curvedness feature descriptor which operates at a high detection accuracy (100% for the spine, >97% for the hooks). From these tracked regions, two locomotion traits have been developed. First, motivated by a novel biomechanical approach, a proxy for the animal's gait asymmetry is introduced. This dynamic proxy is derived from the height variations in the hip joint (hooks) during walking, and extrapolated into right/left vertical leg motion signals. This proxy is evidently affected by minor lameness and directly contributes in identifying the lame leg. Second, back posture, which is analysed using two cubic-fit curvatures (X-Z plane and X-Y plane) from the spine region. The X-Z plane curvature is used to assess the spine's arch as an early lameness trait, while the X-Y plane curvature provides a novel definition for localising the lame side. Objective variables were extracted from both traits to be trained using a linear Support Vector Machine (SVM) classifier. Validation is made against ground truth data manually scored using a 1–5 locomotion scoring (LS) system, which consist of two datasets, 23 sessions and 60 sessions of walking cows. A threshold has been identified between LS 1 and 2 (and above). This boundary is important as it represents the earliest point in time at which a cow is considered lame, and its early detection could improve intervention outcome, thereby minimising losses and reducing animal suffering. The threshold achieved an accuracy of 95.7% with a 100% sensitivity (detecting lame cows), and 75% specificity (detecting non-lame cows) on dataset 1 and an accuracy of 88.3% with an 88% sensitivity and 92% specificity on dataset 2. Thereby outperforming the state-of-the-art at a stricter lameness boundary. The 3D video based multi-trait detection strives towards providing a comprehensive locomotion assessment on dairy farms. This contributes to the detection of developing lameness trends using regular monitoring which will improve the lack of robustness of existing methods and reduce reliance on expensive equipment and/or expertise in the dairy industry

Wearable Sensors Applied in Movement Analysis

Recent advances in electronics have led to sensors whose sizes and weights are such that they can be placed on living systems without impairing their natural motion and habits. They may be worn on the body as accessories or as part of the clothing and enable personalized mobile information processing. Wearable sensors open the way for a nonintrusive and continuous monitoring of body orientation, movements, and various physiological parameters during motor activities in real-life settings. Thus, they may become crucial tools not only for researchers, but also for clinicians, as they have the potential to improve diagnosis, better monitor disease development and thereby individualize treatment. Wearable sensors should obviously go unnoticed for the people wearing them and be intuitive in their installation. They should come with wireless connectivity and low-power consumption. Moreover, the electronics system should be self-calibrating and deliver correct information that is easy to interpret. Cross-platform interfaces that provide secure data storage and easy data analysis and visualization are needed.This book contains a selection of research papers presenting new results addressing the above challenges

Computational Intelligence in Healthcare

This book is a printed edition of the Special Issue Computational Intelligence in Healthcare that was published in Electronic

Computational Intelligence in Healthcare

The number of patient health data has been estimated to have reached 2314 exabytes by 2020. Traditional data analysis techniques are unsuitable to extract useful information from such a vast quantity of data. Thus, intelligent data analysis methods combining human expertise and computational models for accurate and in-depth data analysis are necessary. The technological revolution and medical advances made by combining vast quantities of available data, cloud computing services, and AI-based solutions can provide expert insight and analysis on a mass scale and at a relatively low cost. Computational intelligence (CI) methods, such as fuzzy models, artificial neural networks, evolutionary algorithms, and probabilistic methods, have recently emerged as promising tools for the development and application of intelligent systems in healthcare practice. CI-based systems can learn from data and evolve according to changes in the environments by taking into account the uncertainty characterizing health data, including omics data, clinical data, sensor, and imaging data. The use of CI in healthcare can improve the processing of such data to develop intelligent solutions for prevention, diagnosis, treatment, and follow-up, as well as for the analysis of administrative processes. The present Special Issue on computational intelligence for healthcare is intended to show the potential and the practical impacts of CI techniques in challenging healthcare applications

Development of a novel method for the classification of osteoarthritic and normal knee function

Advances in our understanding of human locomotion can be futile if no practical use is made of them. For the long-term benefit of patients in a clinical setting, scientists and engineers need to forge stronger links with orthopaedic surgeons to make the most use of the recent developments in motion analysis technology. With this requirement as a driving-force, an objective classification tool was developed that uses motion analysis for an application to clinical diagnostics and monitoring, namely knee osteoarthritis (OA) progression and total knee replacement (TKR) recovery. The classification tool is based around the Dempster-Shafer (DS) theory, and as such is built upon the sound foundations of Bayesian statistics. The tool expands on the work of Safranek et al. (1990) and Gerig et al. (2000) who developed and used parts of the classification method in the areas of vision and medical image analysis respectively. Using the data collected during a clinical knee trial, this novel approach enables the objective classification of subjects into an OA or normal group. Each piece of data is transformed into a set of belief values: a level of belief that a subject has OA knee function, a level of belief that a subject has NL knee function and an associated level of uncertainty. The belief values are then represented on a simplex plot, which enables the final classification of a subject, and the level of benefit achieved by TKR surgery to be visualised. The DS method can be used as a fully or partially automated tool. The input variables and control parameters, which are an intrinsic part of the tool, can be chosen by an expert or an optimisation approach. Using a leave-one-out (LOO) approach, the tool was able to classify new subjects with an accuracy of 97.62%. This compares with the 63.89% and 95.24% LOO accuracies of two well-established methods---the Artificial Neural Network and the Linear Discriminant Analysis classifiers respectively. The tool also provides an objective indication of the variables that are the most influential in distinguishing OA and NL knee function. In this case, the variables identified by the tool as important are often cited as clinically relevant variables, which enhances the appeal of the tool to the clinical community and allows for more effective comparison with clinical approaches to diagnosis. Using Simulated Annealing to select the control parameters reduced the LOO accuracy to 95.24%. Automated feature selection using a Genetic Algorithm and Sequential Forward Selection increased the LOO accuracy to 100%. However, further work is required to improve the effect of this process on the overall level of uncertainty in the classification. Initial studies have demonstrated a practical and visual approach that can discriminate between the characteristics of NL and OA knee function with a high level of accuracy. Further development will enable the tool to assist orthopaedic surgeons and therapists in making clinical decisions, and thus promote increased confidence in a patient's medical care.EThOS - Electronic Theses Online ServiceGBUnited Kingdo