Ye, L, Guo, Y, Do, L, Yu, H, Nguyen, H & Su, SW 2019, 'A fast-converge, real-time auto-calibration algorithm for triaxial accelerometer', MEASUREMENT SCIENCE AND TECHNOLOGY, vol. 30, no. 6.View/Download from: UTS OPUS or Publisher's site
Yu, H, Ye, L, Naik, GR, Song, R, Nguyen, HT & Su, S 2018, 'Nonparametric Dynamical Model of Cardiorespiratory Responses at the Onset and Offset of Treadmill Exercises', Medical and Biological Engineering and Computing, vol. 56, no. 12, pp. 2337-2351.View/Download from: UTS OPUS or Publisher's site
This paper applies a nonparametric modelling method with kernel-based regularization to estimate the carbon dioxide production during jogging exercises. The kernel selection and regularization strategies have been discussed; several commonly used kernels are compared regarding the goodness-of-fit, sensitivity, and stability. Based on that, the most appropriate kernel is then selected for the construction of the regularization term. Both the onset and offset of the jogging exercises are investigated. We compare the identified nonparametric models, which include both impulse response models and step response models for the two periods, as well as the relationship between oxygen consumption and carbon dioxide production. The result statistically indicates that the steady-state gain of the carbon dioxide production in the onset of exercise is bigger than that in the offset while the response time of both onset and offset are similar. Compared with oxygen consumption, the response speed of carbon dioxide production is slightly slower in both onset and offset period while its steady-state gains are similar for both periods. The effectiveness of the kernel-based method for the dynamic modelling of cardiorespiratory response to exercise is also well demonstrated.
Ye, L, Argha, A, Yu, H, Celler, BG, Nguyen, HT & Su, S 2018, 'Dynamic characteristics of oxygen consumption.', BioMedical Engineering OnLine, vol. 17, no. 1, pp. 44-44.View/Download from: UTS OPUS or Publisher's site
Previous studies have indicated that oxygen uptake ([Formula: see text]) is one of the most accurate indices for assessing the cardiorespiratory response to exercise. In most existing studies, the response of [Formula: see text] is often roughly modelled as a first-order system due to the inadequate stimulation and low signal to noise ratio. To overcome this difficulty, this paper proposes a novel nonparametric kernel-based method for the dynamic modelling of [Formula: see text] response to provide a more robust estimation.Twenty healthy non-athlete participants conducted treadmill exercises with monotonous stimulation (e.g., single step function as input). During the exercise, [Formula: see text] was measured and recorded by a popular portable gas analyser ([Formula: see text], COSMED). Based on the recorded data, a kernel-based estimation method was proposed to perform the nonparametric modelling of [Formula: see text]. For the proposed method, a properly selected kernel can represent the prior modelling information to reduce the dependence of comprehensive stimulations. Furthermore, due to the special elastic net formed by [Formula: see text] norm and kernelised [Formula: see text] norm, the estimations are smooth and concise. Additionally, the finite impulse response based nonparametric model which estimated by the proposed method can optimally select the order and fit better in terms of goodness-of-fit comparing to classical methods.Several kernels were introduced for the kernel-based [Formula: see text] modelling method. The results clearly indicated that the stable spline (SS) kernel has the best performance for [Formula: see text] modelling. Particularly, based on the experimental data from 20 participants, the estimated response from the proposed method with SS kernel was significantly better than the results from the benchmark method [i.e., prediction error method (PEM)] ([Formula: see text] vs [Formula: see text]).The proposed nonparametric modelling m...
Yu, H, Guo, K, Luo, J, Cao, K, Nguyen, HT & Su, S 2017, 'The Classification for Equilibrium Triad Sensory Loss based on sEMG Signals of Calf Muscles', Proceedings of the 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC'17), 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society 2017, IEEE, Jeju Island, Korea, pp. 2143-2145.View/Download from: UTS OPUS or Publisher's site
Surface Electromyography (sEMG) has been commonly applied for analysing the electrical activities of skeletal muscles. The sensory system of maintaining posture balance includes vision, proprioception and vestibular senses. In this work, an attempt is made to classify whether the body is missing one of the sense during balance control by using sEMG signals. A trial of combination with different features and muscles is also developed. The results demonstrate that the classification accuracy between vision loss and the normal condition is higher than the one between vestibular sense loss and normal condition. When using different features and muscles, the impact on classification results is also different. The outcomes of this study could aid the development of sEMG
based classification for the function of sensory systems during human balance movement.
Guo, K, Candra, H, Yu, H, Li, H, Nguyen, HT & Su, S 2017, 'EEG-based Emotion Classification using Innovative Features and Combined SVM and HMM Classifier', Proceedings of the 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC'17), 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC'17), IEEE, Jeju Island, Korea, pp. 489-492.View/Download from: UTS OPUS
Emotion classification is one of the state-of-theart
topics in biomedical signal research, and yet a significant
portion remains unknown. This paper offers a novel approach
with a combined classifier to recognise human emotion states
based on electroencephalogram (EEG) signal. The objective is
to achieve high accuracy using the combined classifier designed,
which categorises the extracted features calculated from time
domain features and Discrete Wavelet Transform (DWT). Two
innovative designs are involved in this project: a novel variable
is established as a new feature and a combined SVM and
HMM classifier is developed. The result shows that the joined
features raise the accuracy by 5% on valence axis and 1.5% on
arousal axis. The combined classifier can improve the accuracy
by 3% comparing with SVM classifier. One of the important
applications for high accuracy emotion classification system is
offering a powerful tool for psychologists to diagnose emotion
related mental diseases and the system developed in this project
has the potential to serve such purpose.