Knee Joint Vibration Signal Analysis with Matching Pursuit Decomposition and Dynamic Weighted Classifier Fusion

Analysis of knee joint vibration (VAG) signals can provide quantitative indices for detection of knee joint pathology at an early stage. In addition to the statistical features developed in the related previous studies, we extracted two separable features, that is, the number of atoms derived from the wavelet matching pursuit decomposition and the number of significant signal turns detected with the fixed threshold in the time domain. To perform a better classification over the data set of 89 VAG signals, we applied a novel classifier fusion system based on the dynamic weighted fusion (DWF) method to ameliorate the classification performance. For comparison, a single leastsquares support vector machine (LS-SVM) and the Bagging ensemble were used for the classification task as well. The results in terms of overall accuracy in percentage and area under the receiver operating characteristic curve obtained with the DWF-based classifier fusion method reached 88.76% and 0.9515, respectively, which demonstrated the effectiveness and superiority of the DWF method with two distinct features for the VAG signal analysis

Removal of artifacts in knee joint vibroarthrographic signals using ensemble empirical mode decomposition and detrended fluctuation analysis

National Natural Science Foundation of China [81101115, 31200769]; Natural Science Foundation of Fujian Province of China [2011J01371]; Fundamental Research Funds for the Central Universities of China [2010121061]; Program for New Century Excellent Talents in Fujian Province UniversityHigh-resolution knee joint vibroarthrographic (VAG) signals can help physicians accurately evaluate the pathological condition of a degenerative knee joint, in order to prevent unnecessary exploratory surgery. Artifact cancellation is vital to preserve the quality of VAG signals prior to further computer-aided analysis. This paper describes a novel method that effectively utilizes ensemble empirical mode decomposition (EEMD) and detrended fluctuation analysis (DFA) algorithms for the removal of baseline wander and white noise in VAG signal processing. The EEMD method first successively decomposes the raw VAG signal into a set of intrinsic mode functions (IMFs) with fast and low oscillations, until the monotonic baseline wander remains in the last residue. Then, the DFA algorithm is applied to compute the fractal scaling index parameter for each IMF, in order to identify the anti-correlation and the long-range correlation components. Next, the DFA algorithm can be used to identify the anti-correlated and the long-range correlated IMFs, which assists in reconstructing the artifact-reduced VAG signals. Our experimental results showed that the combination of EEMD and DFA algorithms was able to provide averaged signal-to-noise ratio (SNR) values of 20.52 dB (standard deviation: 1.14 dB) and 20.87 dB (standard deviation: 1.89 dB) for 45 normal signals in healthy subjects and 20 pathological signals in symptomatic patients, respectively. The combination of EEMD and DFA algorithms can ameliorate the quality of VAG signals with great SNR improvements over the raw signal, and the results were also superior to those achieved by wavelet matching pursuit decomposition and time-delay neural filter

APPLICATIONS IN VIBROARTHROGRAPHY: ASSESSMENTS OF INSTABILITY IN TOTAL HIP ARTHROPLASTY, CAM-POST ENGAGEMENT IN TOTAL KNEE ARTHROPLASTY, AND VISCOSUPPLEMENTATION IN OSTEOARTHRITIC KNEES

Measurement of joint sounds and vibrations for non-invasive orthopaedic diagnostic purposes has slowly advanced since the 1960s. Most work has been focused in the development of methods for screening of abnormal knees. To date the technique has not gained clinical traction as is it fraught with various obstacles and skepticism. This doctoral thesis is neither an argument in favor of nor against the clinical use of vibroarthrography for musculoskeletal diagnostics in humans, but rather an exploration of its potential in cases of orthopaedic interest. These areas include 1) instability in total hip arthroplasty, 2) cam-post engagement in posterior stabilized total knee arthroplasty, and 3) viscosupplementation in osteoarthritic knees. It was expected that each of these unique cases would be characterized by dynamic phenomena that could be measured in the form of surface vibrations at the skin.Methods previously presented in various vibroarthrography research were adopted, modified, and expounded upon to best suit the needs of each experiment. In a mechanical hip simulator, it was found that vibroarthrography could be effectively used to distinguish the difference between 1 mm and 2 mm of hip separation. In posterior stabilized total knee arthroplasty subjects, it was found that multiple vibroarthrographic features may be used to approximate the occurrence of cam-post engagement, and that vibrations measured at the joint surface may be correlated to cam-post engagement velocity. In osteoarthritic knees, the relationship between clinical evidence, viscosupplementation, and vibroarthrography varied on a case by case basis.To the knowledge of the author, all three of these experiments are the first of their kind. Ultimately, the methods and results presented within provide new foundations for vibroarthrography that may be used to further explore the clinical potential of this noninvasive diagnostic

Development of new intelligent autonomous robotic assistant for hospitals

Continuous technological development in modern societies has increased the quality of life and average life-span of people. This imposes an extra burden on the current healthcare infrastructure, which also creates the opportunity for developing new, autonomous, assistive robots to help alleviate this extra workload. The research question explored the extent to which a prototypical robotic platform can be created and how it may be implemented in a hospital environment with the aim to assist the hospital staff with daily tasks, such as guiding patients and visitors, following patients to ensure safety, and making deliveries to and from rooms and workstations. In terms of major contributions, this thesis outlines five domains of the development of an actual robotic assistant prototype. Firstly, a comprehensive schematic design is presented in which mechanical, electrical, motor control and kinematics solutions have been examined in detail. Next, a new method has been proposed for assessing the intrinsic properties of different flooring-types using machine learning to classify mechanical vibrations. Thirdly, the technical challenge of enabling the robot to simultaneously map and localise itself in a dynamic environment has been addressed, whereby leg detection is introduced to ensure that, whilst mapping, the robot is able to distinguish between people and the background. The fourth contribution is geometric collision prediction into stabilised dynamic navigation methods, thus optimising the navigation ability to update real-time path planning in a dynamic environment. Lastly, the problem of detecting gaze at long distances has been addressed by means of a new eye-tracking hardware solution which combines infra-red eye tracking and depth sensing. The research serves both to provide a template for the development of comprehensive mobile assistive-robot solutions, and to address some of the inherent challenges currently present in introducing autonomous assistive robots in hospital environments.Open Acces

Design and Validation of a Handheld Probe to Measure Knee Joint Sounds

The goal of this research is to design and validate a system used to record joint sounds measurements that can be collected at home by patients. Wearable and handheld technology allows patients to monitor conditions at home, eliminating the need for time-consuming and costly medical appointments. In the United States, around 33% of adults have arthritis or other chronic joint pain conditions (CDC 2001). Due to the load placed on the knee and the reliance of the knee joint on soft tissues, knee joint conditions are the most common articular condition. The most common approach for diagnosing and monitoring knee joint disorders is physical examination, which yields poor diagnostic validity with the exception of the Lachman test for anterior cruciate ligament (ACL) injuries (Tanaka 2017). The clinical gold standard for diagnosing joint disorders is thus medical imaging, such as X-Ray or CT scanning, which poses financial challenges for patients and hospitals alike. Patients are interested in at-home joint monitoring devices. In a focus group study of osteoarthritis patients, patients described the need for objective measures of treatment success that could be taken at home (Papi 2015).Existing joint monitoring devices focus on patient-reported outcomes and physical task performances, leaving quantitative measurements of joint conditions to clinicians. This work aims to validate a knee joint health tracking device for home use that can reduce the costliness and frequency of medical appointments.Undergraduat

Quantifying the Effects of Knee Joint Biomechanics on Acoustical Emissions

The knee is one of the most injured body parts, causing 18 million patients to be seen in clinics every year. Because the knee is a weight-bearing joint, it is prone to pathologies such as osteoarthritis and ligamentous injuries. Existing technologies for monitoring knee health can provide accurate assessment and diagnosis for acute injuries. However, they are mainly confined to clinical or laboratory settings only, time-consuming, expensive, and not well-suited for longitudinal monitoring. Developing a novel technology for joint health assessment beyond the clinic can further provide insights on the rehabilitation process and quantitative usage of the knee joint. To better understand the underlying properties and fundamentals of joint sounds, this research will investigate the relationship between the changes in the knee joint structure (i.e. structural damage and joint contact force) and the JAEs while developing novel techniques for analyzing these sounds. We envision that the possibility of quantifying joint structure and joint load usage from these acoustic sensors would advance the potential of JAE as the next biomarker of joint health that can be captured with wearable technology. First, we developed a novel processing technique for JAEs that quantify on the structural change of the knee from injured athletes and human lower-limb cadaver models. Second, we quantified whether JAEs can detect the increase in the mechanical stress on the knee joint using an unsupervised graph mining algorithm. Lastly, we quantified the directional bias of the load distribution between medial and lateral compartment using JAEs. Understanding and monitoring the quantitative usage of knee loads in daily activities can broaden the implications for longitudinal joint health monitoring.Ph.D

Acoustic analysis of the knee joint in the study of osteoarthritis detection during walking

This thesis investigates the potential of non-invasive detection of knee Osteoarthritis (OA) using the sounds emitted by the knee joint during walking and captured by a single microphone. This is a novel application since, until now, there are no other methods that considered this type of signals. Clinical detection of knee OA relies on imaging techniques such as X-radiology and Magnetic Resonance Imaging. Some of these methods are expensive and impractical while others pose health risks due to radiation. Knee sounds on the other hand may offer a quick, practical and cost-effective alternative for the detection of the disease. In this thesis, the knee sound signal structure is investigated using signal processing methods for information extraction from the time, frequency, cepstral and modulation domains. Feature representations are obtained and their discriminant properties are studied using statistical methods such as the Bhattacharyya distance and supervised learning techniques such as Support Vector Machine. From this work, a statistical feature parameterisation is proposed and its efficacy for the task of healthy vs OA knee condition classification is investigated using a comprehensive experimental framework proposed in this thesis. Feature-based representations that incorporate spatiotemporal information using gait pattern variables, were also investigated for classification. Using the waveform characteristics of the acoustic pulse events detected in the signal, such representations are proposed and evaluated. This approach utilised a novel stride detection and segmentation algorithm that is based on dynamic programming and is also proposed in the thesis. This algorithm opens up potential applications in other research fields such as gait analysis.Open Acces