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

Minimisation of energy consumption variance for multi-process manufacturing lines through genetic algorithm manipulation of production schedule

Typical manufacturing scheduling algorithms do not consider the energy consumption of each job, or its variance, when they generate a production schedule. This can become problematic for manufacturers when local infrastructure has limited energy distribution capabilities. In this paper, a genetic algorithm based schedule modification algorithm is presented. By referencing energy consumption models for each job, adjustments are made to the original schedule so that it produces a minimal variance in the total energy consumption in a multi-process manufacturing production line, all while operating within the constraints of the manufacturing line and individual processes. Empirical results show a significant reduction in energy consumption variance can be achieved on schedules containing multiple concurrent jobs

Discovering Associations between Acoustic Emission and Magnetic Resonance Imaging Biomarkers from 10 Osteoarthritic Knees

Objective: Acoustic emission (AE) sensed from knee joints during weight-bearing movements greatly increases with joint deterioration, but the relationship between AE patterns and specific anatomical damage, as seen for example in magnetic resonance imaging (MRI), is unknown. This knowledge is essential to validate AE biomarkers for the evaluation of knee joints, and forms the objective of this exploratory work to associate knee AE and MRI. Methods: A novel processing framework is proposed to enable direct correlation between static 3D MRI of knees and their dynamic 1D AE during sit-stand-sit movements. It comprises a method to estimate articular cartilage thickness according to joint angle from knee MRI, and a method to derive statistically representative waveform features according to joint angle from movement and load-dependent knee AE. Results: In 10 subjects diagnosed with knee osteoarthritis, age 55~79 years and body mass index 25~35 kg/m2, a strong inverse relationship between knee AE and cartilage thickness in the medial tibiofemoral compartment around the fully standing position was observed. Knees with thinner articular cartilage generated more AE with higher amplitude, greater energy, longer duration, and higher frequencies, in agreement with the assumption of more intense articulation friction under full body weight. Conclusion: AE provides promising quantitative biomarkers in knee joint disease. Significance: These findings provide impetus for the further development of AE as a low-cost non-invasive biomarker modality to improve the management of knee joint disease

Development of Bone-based Kinematics from Magnetic Resonance Imaging for Immersive Assessment of Knee Joints

By extracting the moving trajectory of an artificial anatomical knee model (AAKM), this paper presents a novel development of bone-based kinematics from magnetic resonance imaging (MRI) for immersive assessment of knee joints. Using this tool, it becomes possible to emulate patient-specific knee kinematics constructed from MRI, visualize surface contact areas between internal joint components during movement, and assess the damage of knee joints in a dynamic, immersive and interactive manner, with the help of the state-of-the-art virtual reality (VR) technology

Knee acoustic emission: a potential biomarker for quantitative assessment of joint ageing and degeneration

Based on a single time-point study of 34 healthy and 19 osteoarthritic knees in three different age groups (early, middle and late adulthood), this paper reports the potential of knee acoustic emission as a biomarker to monitor joint ageing and degeneration. Measurements were made of short transient high frequency acoustic emission signals generated by knee joints under stress during repeated sit–stand–sit movements along with joint angle. A statistically significant feature profile was established using a four-phase model of sit–stand–sit movements and two waveform features. The four-phase movement model is derived from joint angle measurement during repeated sit–stand–sit movements, 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. The two statistically significant waveform features are extracted from AE measurement during repeated sit–stand–sit movements, and they consist of the peak magnitude value and average signal level of each AE burst. In addition to the use of bilateral plots, statistical distributions and 2D colour histograms to visualise the differences and similarities among participants, use of principal component analysis showed not only distinct data clusters corresponding to participating groups, but also an age- and disease-related trajectory progressing from the early adulthood healthy group to the late adulthood healthy group followed by the middle adulthood osteoarthritic group to the late adulthood osteoarthritic group. Furthermore, this trajectory shows increasing areas for each data cluster, with a highly compact cluster for the early adulthood healthy group at one end and a widely spread cluster for the late adulthood osteoarthritic group at the other end. From these results, a strong basis is formed for further development of knee acoustic emission as a convenient and non-invasive biomarker for quantitative assessment of joint ageing and degeneration

A STATISITICAL SHAPE MODEL FOR DEFORMABLE SURFACE REGISTRATION

Abstract: This short paper presents a deformable surface registration scheme which is based on the statistical shape modelling technique. The method consists of two major processing stages, model building and model fitting. A statistical shape model is first built using a set of training data. Then the model is deformed and matched to the new data by a modified iterative closest point (ICP) registration process. The proposed method is tested on real 3-D facial data from BU-3DFE database. It is shown that proposed method can achieve a reasonable result on surface registration, and can be used for patient position monitoring in radiation therapy and potentially can be used for monitoring of the radiation therapy progress for head and neck patients by analysis of facial articulation

Dynamic hand gesture tracking and recognition for real-time immersive virtual object manipulation

Immersive visualisation is increasingly being used for comprehensive and rapid analysis of objects in 3D and object dynamic behaviour in 4D. Challenges are therefore presented to provide natural user interaction to enable effortless virtual object manipulation. Presented in this paper is the development and evaluation of a human-computer interaction system based on natural hand gestures. By employing a hybrid inertial and ultrasonic tracking system to provide the absolute positions and orientations of the userpsilas head and hands as well as a pair of high degrees-of-freedoms data gloves to provide the relative positions and orientations of finger joints and tips in both hands, the proposed system is shown to be able to automatically track and recognise a number of simple hand gestures. The effectiveness and potential of the proposed system is demonstrated through the five basic object manipulation tasks involving selection, release, translation, rotation and scaling of a 3D virtual cube

Hand Motion Recognition and Visualisation for Direct Sign Writing

Although SignWriting provides an intuitive notation system based on pictorial symbols to enable any sign based language in the world to be transcribed into a written form, it is a time consuming process for keyboard based input. To address the challenge of direct sign writing, the paper presents a human-computer-interaction system developed for recognition and visualisation of hand movements. The system is shown to be able to display the corresponding SignWriting symbols for various hand movements performed by two hands based on motion characteristics such as movement planes, movement directions, straight/curve movement paths, clockwise/anti-clockwise movements, and single/repeated movements

Overcoming the Information Overload Problem in a Multiform Feedback Based Virtual Reality System for Hand Motion Rehabilitation

The hand motion function of Cerebral Palsy (CP) and stroke patients could be improved by task-oriented rehabilitation. Nowadays, there is a move in the development of the rehabilitation environment from the real world to virtual worlds. Patients could benefit from virtual reality (VR) based rehabilitation by multimodal and multiform feedback. However, it is recognised that such feedback might cause information overload, leading to the patient feeling confused and distracted during training. The aim of this study is to investigate the effectiveness of separate function-specific feedback training exercises prior to the task-orientated multiple feedback training exercise in order to overcome the information overload problem. This paper presents a brief report of three training exercises, which include static reaching, rotating and muscle contraction feedback