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Contemporary physiotherapy and rehabilitation practice uses subjective measures for motion evaluation and requires time-consuming supervision. Algorithms that can accurately segment patient movement would provide valuable data for progress tracking and on-line patient feedback. In this paper, we propose a two-class classifier approach to label each data point in the patient movement data as either...
Pattern recognition based myoelectric prostheses (MP) need a training procedure for calibrating the classifier. Due to the non-stationarity inhered in surface electromyography (sEMG) signals, the system should be retrained day by day in long-term use of MP. To boost the training procedure in later periods, we propose a method, namely Mixed-LDA, which computes the parameters of LDA through combining...
In this study we propose a novel atrial activity-based method for atrial fibrillation (AF) identification that detects the absence of normal sinus rhythm (SR) P-waves from the surface ECG. The proposed algorithm extracts nine features from P-waves during SR and develops a statistical model to describe the distribution of the features. The Expectation-Maximization algorithm is applied to a training...
In this paper, we propose a new method for classifying patients with pulmonary emphysema and healthy subjects using lung sounds. Using conventional classification methods, every boundary between inspiratory and expiratory phases in successive respiratory sounds are detected manually prior to automatic classification. However, manual segmentation must be performed accurately and has therefore created...
Various methods for detecting optic disc and macula in fundus images have been developed. Our aim is to propose a fairly easy method for detecting both features jointly. This is achieved by first correcting inhomogenous luminosity using a polynomial approximation of the background of the images. Secondly, the use of the cross-correlation in the frequency domain between the images and a steerable template...
It is important to classify retinal blood vessels into arterioles and venules for computerised analysis of the vasculature and to aid discovery of disease biomarkers. For instance, zone B is the standardised region of a retinal image utilised for the measurement of the arteriole to venule width ratio (AVR), a parameter indicative of microvascular health and systemic disease. We introduce a Least Square-Support...
Hypoglycemia is a common side-effect of insulin therapy for patients with type 1 diabetes mellitus (T1DM) and is the major limiting factor to maintain tight glycemic control. The deficiency in glucose counter-regulation may even lead to severe hypoglycaemia. It is always threatening to the well-being of patients with T1DM since more severe hypoglycemia leads to seizures or loss of consciousness and...
For the last decade, remarkable progress has been made in the field of cardiovascular disease treatment. However, these complex medical procedures require a combination of rich experience and technical skills. In this paper, a 3D virtual reality simulator for core skills training in minimally invasive surgery is presented. The system can generate realistic 3D vascular models segmented from patient...
High-level spinal cord injury (SCI) survivors face every day two related problems: recovering motor skills and regaining functional independence. Body machine interfaces (BoMIs) empower people with sever motor disabilities with the ability to control an external device, but they also offer the opportunity to focus concurrently on achieving rehabilitative goals. In this study we developed a portable,...
The purpose of this study is to build a cost-effective and easy-to-popularize laparoscopic training system based on improving traditional training box. The system has the capability of objective skills assessment and the function of automatic recording of training process and results, as well as 3-dimensional coordinate tracking of instruments. The results of pilot experiment in laparoscopic-assisted...
Vertebroplasty and kyphoplasty are commonly used minimally invasive methods to treat vertebral compression fractures. Novice surgeons gather surgical skills in different ways, mainly by “learning by doing” or training on models, specimens or simulators. Currently, a new training modality, an augmented reality simulator for minimally invasive spine surgeries, is going to be developed. An important...
This paper presents a lower body reaction test that utilizes a new portable ultra-sound based motion capture system (MobiFit) combined with a synchronized visual stimulus. This novel system was tested first for criterion validity and agreement against a gold standard laboratory based optical motion capture system (CODA). It was subsequently tested in the field during Gaelic football (GAA) team gym...
This paper proposed a novel method to select the effective Electroencephalography (EEG) channels for the motor imagery tasks based on the inconsistencies from multiple classifiers. The inconsistency criterion for channel selection was designed based on the fluctuation of the classification accuracies among different classifiers when the noisy channels were included. These noisy channels were then...
Microfluidic diagnostics for use in the developing world face a number of unique challenges. Doctors and nurses in developing countries are best suited to addresses these challenges, but they lack the resources and training needed to develop their own microfluidic diagnostics. To address this need, we are developing a system of Multifluidic Evolutionary Components or MECs, “building blocks” that can...
Identification of drug candidates is an important but also difficult process. Given drug resistance bacteria that we face, this process has become more important to identify protein candidates that demonstrate antibacterial activity. The aim of this study is therefore to develop a bioinformatics approach that is more capable of identifying a small but effective set of proteins that are expected to...
We developed and tested a seizure detection algorithm based on two measures of nonlinear and linear dynamics, that is, the adaptive short-term maximum Lyapunov exponent (ASTLmax) and the adaptive Teager energy (ATE). The algorithm was tested on long-term (0.5–11.7 days) continuous EEG recordings from five patients (3 with intracranial and 2 with scalp EEG) with a total of 56 seizures, producing a...
Most existing vision-based methods for gaze tracking need a tedious calibration process. In this process, subjects are required to fixate on a specific point or several specific points in space. However, it is hard to cooperate, especially for children and human infants. In this paper, a new calibration-free gaze tracking system and method is presented for automatic measurement of visual acuity in...
EEG signals, which can record the electrical activity along the scalp, provide researchers a reliable channel for investigating human emotional states. In this paper, a new algorithm, manifold regularized extreme learning machine (MRELM), is proposed for recognizing human emotional states (positive, neutral and negative) from EEG data, which were previously evoked by watching different types of movie...
We present a study in which participants were trained in several sessions to control a (comparatively simple) robot via an EEG-/motor imagery-based Brain-Computer Interface (BCI). In the final (experiment) session pairs of participants were formed and each participant controlled one of two robots in a shared space. EEG data was recorded synchronously from both participants. We performed a joint data...
Multivariate Pattern Analysis (MVPA) is frequently used to decode cognitive states from brain activities in fMRI study. Due to the discrepancy between sample and feature size, MVPA methods are suffered from the overfitting problem. This paper addresses this issue by introducing sparse modelling along with its advanced decoding method, Compressive Sensing (CS). As brain voxels have highly correlated...
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