121 research outputs found
Adaptive Bayesian networks for video processing
ABSTRACT Due to its static nature, the inference capability of Bayesian Networks (BNs) oflen deteriorates when the basis of input data varies, especially in video processing applications where the environment often changes constantly. This paper presents an adaptive BN where the network parameters are adjusted in accordance to input variations. An efficient re-training method is introduced for updating the parameters and the proposed network is applied to shadow removal in video sequence processing with quantitative results demonstrating the significance of adapting the network with environmental changes
A Wearable Sensing Framework for Improving Personal and Oral Hygiene for People with Developmental Disabilities
People with developmental disabilities often face difficulties in coping with daily activities and many require constant support. One of the major health issues for people with developmental disabilities is personal hygiene. Many lack the ability, poor memory or lack of attention to carry out normal daily activities like brushing teeth and washing hands. Poor personal hygiene may result in increased susceptibility to infection and other health issues. To enable independent living and improve the quality of care for people with developmental abilities, this paper proposes a new wearable sensing framework to monitoring personal hygiene. Based on a smartwatch, this framework is designed as a pervasive monitoring and learning tool to provide detailed evaluation and feedback to the user on hand washing and tooth brushing. A preliminary study was conducted to assess the performance of the approach, and the results showed the reliability and robustness of the framework in quantifying and assessing hand washing and tooth brushing activities
Wireless Wearable Photoplethysmography Sensors for Continuous Blood Pressure Monitoring
Blood Pressure (BP) is a crucial vital sign taken into consideration for the general assessment of patient’s condition: patients with hypertension or hypotension are advised to record their BP routinely. Particularly, hypertension is emphasized by stress, diabetic neuropathy and coronary heart diseases and could lead to stroke. Therefore, routine and long-term monitoring can enable early detection of symptoms and prevent life-threatening events. The gold standard method for measuring BP is the use of a stethoscope and sphygmomanometer to detect systolic and diastolic pressures. However, only discrete measurements are taken. To enable pervasive and continuous monitoring of BP, recent methods have been proposed: pulse arrival time (PAT) or PAT difference (PATD) between different body parts are based on the combination of electrocardiogram (ECG) and photoplethysmography (PPG) sensors. Nevertheless, this technique could be quite obtrusive as in addition to at least two contacts/electrodes to measure the differential voltage across the left arm/leg/chest and the right arm/leg/chest, ECG measurements are easily corrupted by motion artefacts. Although such devices are small, wearable and relatively convenient to use, most devices are not designed for continuous BP measurements. This paper introduces a novel PPG-based pervasive sensing platform for continuous measurements of BP. Based on the principle of using PAT to estimate BP, two PPG sensors are used to measure the PATD between the earlobe and the wrist to measure BP. The device is compared with a gold standard PPG sensor and validation of the concept is conducted with a preliminary study involving 9 healthy subjects. Results show that the mean BP and PATD are correlated with a 0.3 factor. This preliminary study shows the feasibility of continuous monitoring of BP using a pair of PPG placed on the ear lobe and wrist with PATD measurements is possible
Point2Volume: A vision-based dietary assessment approach using view synthesis
Dietary assessment is an important tool for nutritional epidemiology studies. To assess the dietary intake, the common approach is to carry out 24-h dietary recall (24HR), a structured interview conducted by experienced dietitians. Due to the unconscious biases in such self-reporting methods, many research works have proposed the use of vision-based approaches to provide accurate and objective assessments. In this article, a novel vision-based method based on real-time three-dimensional (3-D) reconstruction and deep learning view synthesis is proposed to enable accurate portion size estimation of food items consumed. A point completion neural network is developed to complete partial point cloud of food items based on a single depth image or video captured from any convenient viewing position. Once 3-D models of food items are reconstructed, the food volume can be estimated through meshing. Compared to previous methods, our method has addressed several major challenges in vision-based dietary assessment, such as view occlusion and scale ambiguity, and it outperforms previous approaches in accurate portion size estimation
Automated epileptic seizure detection by analyzing wearable EEG signals using extended correlation-based feature selection
Electroencephalogram (EEG) that measures the electrical activity of the brain has been widely employed for diagnosing epilepsy which is one kind of brain abnormalities. With the advancement of low-cost wearable brain-computer interface devices, it is possible to monitor EEG for epileptic seizure detection in daily use. However, it is still challenging to develop seizure classification algorithms with a considerable higher accuracy and lower complexity. In this study, we propose a lightweight method which can reduce the number of features for a multiclass classification to identify three different seizure statuses (i.e., Healthy, Interictal and Epileptic seizure) through EEG signals with a wearable EEG sensors using Extended Correlation-Based Feature Selection (ECFS). More specifically, there are three steps in our proposed approach. Firstly, the EEG signals were segmented into five frequency bands and secondly, we extract the features while the unnecessary feature space was eliminated by developing the ECFS method. Finally, the features were fed into five different classification algorithms, including Random Forest, Support Vector Machine, Logistic Model Trees, RBF Network and Multilayer Perceptron. Experimental results have shown that Logistic Model Trees provides the highest accuracy of 97.6% comparing to other classifiers
Optical fabrication and characterisation of SU-8 disk photonic waveguide heterostructure cavities
In order to demonstrate cavity quantum electrodynamics using photonic crystal (PhC) cavities fabricated around self-assembled quantum dots (QDs), reliable spectral and spatial overlap between the cavity mode and the quantum dot is required. We present a method for using photoresist to optically fabricate heterostructure cavities in a PhC waveguide with a combined photolithography and micro-photoluminescence spectroscopy system. The system can identify single QDs with a spatial precision of ±25 nm, and we confirm the creation of high quality factor cavity modes deterministically placed with the same spatial precision. This method offers a promising route towards bright, on-chip single photon sources for quantum information applications
Design and prototyping of a bio-inspired kinematic sensing suit for the shoulder joint: precursor to a multi-DoF shoulder exosuit
Soft wearable robots represent a promising new design paradigm for rehabilitation and active assistance applications. Their compliant nature makes them ideal for complex joints, but intuitive control of these robots require robust and compliant sensing mechanisms. In this work, we introduce the sensing framework for a multiple degrees-of-freedom shoulder exosuit capable of sensing the kinematics of the joint. The proposed sensing system is inspired by the body's embodied kinematic sensing, and the organisation of muscles and muscle synergies responsible for shoulder movements. A motion-capture-based evaluation study of the developed framework confirmed conformance with the behaviour of the muscles that inspired its routing. This validation of the tendon-routing hypothesis allows for it to be extended to the actuation framework of the exosuit in the future. The sensor-to-joint-space mapping is based on multivariate multiple regression and derived using an Artificial Neural Network. Evaluation of the derived mapping achieved root mean square error of ≈5.43° and ≃3.65° for the azimuth and elevation joint angles measured over 29,500 frames (4+ minutes) of motion-capture data
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Non-polar InGaN quantum dot emission with crystal-axis oriented linear polarization
Polarization sensitive photoluminescence is performed on single non-polar InGaN quantum dots. The studied InGaN quantum dots are found to have linearly polarized emission with a common polarization direction defined by the [0001] crystal axis. Around half of ∼40 studied dots have a polarization degree of 1. For those lines with a polarization degree less than 1, we can resolve fine structure splittings between −800 μeV and +800 μeV, with no clear correlation between fine structure splitting and emission energy.This research was supported by the Engineering and Physical Sciences Research Council (EPSRC) UK (Grant No. EP/H047816/1).This is the accepted manuscript of a paper published in Applied Physics Letters (Reid BPL, Kocher C, Zhu T, Oehler F, Chan CCS, Oliver RA, Taylor RA, Applied Physics Letters, 2015, 106, 171108, doi:10.1063/1.4919656). The final version is available at http://dx.doi.org/10.1063/1.491965
Observations of Rabi oscillations in a non-polar InGaN quantum dot
Experimental observation of Rabi rotations between an exciton excited state and the crystal ground state in a single non-polar InGaN quantum dot is presented. The exciton excited state energy is determined by photoluminescence excitation spectroscopy using two-photon excitation from a pulsed laser. The population of the exciton excited state is seen to undergo power dependent damped Rabi oscillations.This research was supported by the Engineering and Physical Sciences Research Council (EPSRC) U.K. (Grant No. EP/H047816/1).This version is the author accepted manuscript. The published version can also be found on the publisher's website at: http://scitation.aip.org/content/aip/journal/apl/104/26/10.1063/1.4886961 © 2014 AIP Publishing LL
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