Discovery of acoustic emission based biomarker for quantitative assessment of knee joint ageing and degeneration

Based on the study of 34 healthy and 19 osteoarthritic knees in three different age groups (early, middle and late adulthood), this thesis reports the discovery of the potential of knee acoustic emission (AE) as a biomarker for quantitative assessment of joint ageing and degeneration. Signal processing and statistical analysis were conducted on the joint angle signals acquired using electronic goniometers attached to the lateral side of the legs during repeated sit- stand-sit movements. A four-phase movement model derived from joint angle measurement is proposed for statistical analysis, and it consists of the ascending- acceleration and ascending-deceleration phases in the sit-to- stand movement, followed by the descending-acceleration and descending-deceleration phases in the stand-to-sit movement. Through the quantitative assessment of joint angle signals based on the four-phase model established, statistical differences of different knee conditions related to age and degeneration were discovered based on cycle-by- cycle variations and movement symmetry. For AE burst signals acquired from piezo-electric sensors attached to the knee joints during repeated sit-stand-sit movements, the statistical analysis started from the quantity of AE events in the proposed four movement phases and extended to waveform features extracted from AE signals. While the quantity of AE events was found to follow certain statistical trends related to age and degeneration in each movement phase, detail statistical analysis of AE waveform features yielded the peak amplitude value and average signal level of each AE burst as two most significant features. An image based knee AE feature profile is presented based on 2D colour histograms formed by the peak amplitude value and average signal level in four movement phases. It provides not only a visual trend related to knee age and degeneration, but also enables visual assessment of th

Personalized Video Recommendation Using Rich Contents from Videos

Video recommendation has become an essential way of helping people explore the massive videos and discover the ones that may be of interest to them. In the existing video recommender systems, the models make the recommendations based on the user-video interactions and single specific content features. When the specific content features are unavailable, the performance of the existing models will seriously deteriorate. Inspired by the fact that rich contents (e.g., text, audio, motion, and so on) exist in videos, in this paper, we explore how to use these rich contents to overcome the limitations caused by the unavailability of the specific ones. Specifically, we propose a novel general framework that incorporates arbitrary single content feature with user-video interactions, named as collaborative embedding regression (CER) model, to make effective video recommendation in both in-matrix and out-of-matrix scenarios. Our extensive experiments on two real-world large-scale datasets show that CER beats the existing recommender models with any single content feature and is more time efficient. In addition, we propose a priority-based late fusion (PRI) method to gain the benefit brought by the integrating the multiple content features. The corresponding experiment shows that PRI brings real performance improvement to the baseline and outperforms the existing fusion methods

A novel method for detecting bearing defects based on EMD and fractal dimension

Bearings are widely used in rotating machines. Its health status is a significant index to indicate whether machines run continually or not. Detecting the bearing defects timely is very important for the maintenance decision making. In this paper, a novel bearing defects detection method based on EMD and Fractal Dimension is developed. The original data is decomposed into a set of intrinsic mode functions (IMFs) using EMD, and the fractal dimension of IMFs which contains bearing fault characteristic information are calculated, and these characteristic parameters are used to identify bearing fault types. The effectiveness of this methodology is demonstrated using experimental data

Polarization imaging apparatus

A polarization imaging apparatus measures the Stokes image of a sample. The apparatus consists of an optical lens set 11, a linear polarizer 14 with its optical axis 18, a first variable phase retarder 12 with its optical axis 16 aligned 22.5.degree. to axis 18, a second variable phase retarder 13 with its optical axis 17 aligned 45.degree. to axis 18, a imaging sensor 15 for sensing the intensity images of the sample, a controller 101 and a computer 102. Two variable phase retarders 12 and 13 were controlled independently by a computer 102 through a controller unit 101 which generates a sequential of voltages to control the phase retardations of VPRs 12 and 13. A set of four intensity images, I.sub.0, I.sub.1, I.sub.2 and I.sub.3 of the sample were captured by imaging sensor 15 when the phase retardations of VPRs 12 and 13 were set at (0,0), (.pi.,0), (.pi.,.pi.) and (.pi./2,.pi.), respectively Then four Stokes components of a Stokes image, S.sub.0, S.sub.1, S.sub.2 and S.sub.3 were calculated using the four intensity images

Discovering Differences in Acoustic Emission Between Healthy and Osteoarthritic Knees Using a Four-Phase Model of Sit-Stand-Sit Movements

By performing repeated sit-stand-sit movements to create stress on knee joints, short transient bursts of high frequency acoustic emission (AE) released by the knee joints were acquired from two age matched groups consisting of healthy and osteoarthritic (OA) knees, and significant differences between these two groups were discovered from the signal analysis performed. The analysis is based on a four-phase model of sit-stand-sit movements and a two-feature descriptor of AE bursts. The four phases are derived from joint angle measurement during movement, and they consist of the ascending-acceleration and ascending-deceleration phases in the sit-to-stand movement, followed by the descending-acceleration and descending-deceleration phases in the stand-to-sit movement. The two features are extracted from AE measurement during movement, and they consist of the peak magnitude value and average signal level of each AE burst. The proposed analysis method is shown to provide a high sensitivity for differentiation of the two age matched healthy and OA groups, with the most significant difference found to come from the peak magnitude value in the ascending-deceleration phase, clear quantity and strength differences in the image based visual display of their AE feature profiles due to substantially more AE bursts from OA knee joints with higher peak magnitude values and higher average signal levels, and two well separated clusters in the space formed by the principal components. These results provide ample support for further development of AE as a novel tool to facilitate dynamic integrity assessment of knee joints in clinic and home settings