Vibroarthrograpyhy (VAG) is an innovative, objective and non-invasive technique to obtain diagnostic information concerning the articular cartilage of knee joints. Knee VAG signals can be detected by putting a contact sensor on the surface of the knee joints during the movement such as flexion and extension. Before this research, there are many VAG group studies that contribute in signal processing and database building. The adaptive segmentation method and autoregressive modeling are developed to segment the nonstationary VAG signals. This thesis tries to investigate the accuracy of some database containing root mean square (RMS) value and intraclass distance (ID) feature parameters of physiological patellofemoral crepitus (PPC) signals. This research is first setting up two diagnosis standards for RMS and ID. According to the two standards, all signals are divided into three types: normal, unknown and injured, and those appear both in normal type of RMS and ID parameters are picked out. The same does the injured type. In conclusion, by checking the anamneses of these signals, we can be aware of the numbers of real normal and real injured in normal type and injured type; therefore the accuracy of the database can be derived. Consequently the accuracy of database in this thesis is quite certifiable.

abstract: There is a growing interest for improved high-accuracy camera calibration methods due to the increasing demand for 3D visual media in commercial markets. Camera calibration is used widely in the fields of computer vision, robotics and 3D reconstruction. Camera calibration is the first step for extracting 3D data from a 2D image. It plays a crucial role in computer vision and 3D reconstruction due to the fact that the accuracy of the reconstruction and 3D coordinate determination relies on the accuracy of the camera calibration to a great extent. This thesis presents a novel camera calibration method using a circular calibration pattern. The disadvantages and issues with existing state-of-the-art methods are discussed and are overcome in this work. The implemented system consists of techniques of local adaptive segmentation, ellipse fitting, projection and optimization. Simulation results are presented to illustrate the performance of the proposed scheme. These results show that the proposed method reduces the error as compared to the state-of-the-art for high-resolution images, and that the proposed scheme is more robust to blur in the imaged calibration pattern. / Dissertation/Thesis / M.S. Electrical Engineering 2012

Hospital Acquired Infections (HAI) are a global patient safety challenge, costly to treat, and affect hundreds of millions of patients annually worldwide. It has been shown that the majority of HAI are transferred to patients by caregivers' hands and therefore, can be prevented by proper hand hygiene (HH). However, many factors including cognitive load, cause caregivers to forget to cleanse their hands. Hand hygiene compliance among caregivers remains low around the world. In this thesis I showed that it is possible to build a wearable accelerometer-based HH reminder system to identify ongoing nursing activities with the patient, indicate the high-risk activities, and prompt the caregivers to clean their hands. Eight subjects participated in this study, each wearing five wireless accelerometer sensors on the wrist, upper arms and the back. A pattern recognition approach was used to classify six nursing activities offline. Time-domain features that included mean, standard deviation, energy, and correlation among accelerometer axes were found to be suitable features. On average, 1-Nearest Neighbour classifier was able to classify the activities with 84% accuracy. A novel algorithm was developed to adaptively segment the accelerometer signals to identify the start and stop time of each nursing activity. The overall accuracy of the algorithm for a total of 96 events performed by 8 subjects was approximately 87%. The accuracy was higher than 91% for 5 out of 8 subjects. The sequence of nursing activities was modelled by an 18-state Markov Chain. The model was evaluated by recently published data. The simulation results showed that the high-risk of cross-contamination decreases exponentially by frequency of HH and this happens more rapidly up to 50%-60% hand hygiene rate. It was also found that if the caregiver enters the room with high-risk of transferring infection to the current patient, given the assumptions in this study, only 55% HH is capable of reducing the risk of infection transfer to the lowest level. This may help to prevent the next patient from acquiring infection, preventing an infection outbreak. The model is also capable of simulating the effects of the imperfect HH on the risk of cross-contamination.

Hospital Acquired Infections (HAI) are a global patient safety challenge, costly to treat, and affect hundreds of millions of patients annually worldwide. It has been shown that the majority of HAI are transferred to patients by caregivers' hands and therefore, can be prevented by proper hand hygiene (HH). However, many factors including cognitive load, cause caregivers to forget to cleanse their hands. Hand hygiene compliance among caregivers remains low around the world. In this thesis I showed that it is possible to build a wearable accelerometer-based HH reminder system to identify ongoing nursing activities with the patient, indicate the high-risk activities, and prompt the caregivers to clean their hands. Eight subjects participated in this study, each wearing five wireless accelerometer sensors on the wrist, upper arms and the back. A pattern recognition approach was used to classify six nursing activities offline. Time-domain features that included mean, standard deviation, energy, and correlation among accelerometer axes were found to be suitable features. On average, 1-Nearest Neighbour classifier was able to classify the activities with 84% accuracy. A novel algorithm was developed to adaptively segment the accelerometer signals to identify the start and stop time of each nursing activity. The overall accuracy of the algorithm for a total of 96 events performed by 8 subjects was approximately 87%. The accuracy was higher than 91% for 5 out of 8 subjects. The sequence of nursing activities was modelled by an 18-state Markov Chain. The model was evaluated by recently published data. The simulation results showed that the high-risk of cross-contamination decreases exponentially by frequency of HH and this happens more rapidly up to 50%-60% hand hygiene rate. It was also found that if the caregiver enters the room with high-risk of transferring infection to the current patient, given the assumptions in this study, only 55% HH is capable of reducing the risk of infection transfer to the lowest level. This may help to prevent the next patient from acquiring infection, preventing an infection outbreak. The model is also capable of simulating the effects of the imperfect HH on the risk of cross-contamination.