222 results found
Zhang G, Mei Z, Zhang Y, et al., 2020, A Noninvasive Blood Glucose Monitoring System Based on Smartphone PPG Signal Processing and Machine Learning, IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, Vol: 16, Pages: 7209-7218, ISSN: 1551-3203
Di Camillo B, Nicosia G, Buffa F, et al., 2020, Guest editorial data science in smart healthcare: Challenges and opportunities, IEEE Journal of Biomedical and Health Informatics, Vol: 24, Pages: 3041-3043, ISSN: 2168-2194
Zhang D, Barbot A, Lo B, et al., 2020, Distributed Force Control for Microrobot Manipulation via Planar Multi-Spot Optical Tweezer, ADVANCED OPTICAL MATERIALS, ISSN: 2195-1071
Chen C-M, Anastasova S, Zhang K, et al., 2020, Towards Wearable and Flexible Sensors and Circuits Integration for Stress Monitoring, IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS, Vol: 24, Pages: 2208-2215, ISSN: 2168-2194
Gu X, Guo Y, Deligianni F, et al., 2020, Cross-Subject and Cross-Modal Transfer for Generalized Abnormal Gait Pattern Recognition, IEEE Transactions on Neural Networks and Learning Systems, ISSN: 1045-9227
For abnormal gait recognition, pattern-specific features indicating abnormalities are interleaved with the subject-specific differences representing biometric traits. Deep representations are, therefore, prone to overfitting, and the models derived cannot generalize well to new subjects. Furthermore, there is limited availability of abnormal gait data obtained from precise Motion Capture (Mocap) systems because of regulatory issues and slow adaptation of new technologies in health care. On the other hand, data captured from markerless vision sensors or wearable sensors can be obtained in home environments, but noises from such devices may prevent the effective extraction of relevant features. To address these challenges, we propose a cascade of deep architectures that can encode cross-modal and cross-subject transfer for abnormal gait recognition. Cross-modal transfer maps noisy data obtained from RGBD and wearable sensors to accurate 4-D representations of the lower limb and joints obtained from the Mocap system. Subsequently, cross-subject transfer allows disentangling subject-specific from abnormal pattern-specific gait features based on a multiencoder autoencoder architecture. To validate the proposed methodology, we obtained multimodal gait data based on a multicamera motion capture system along with synchronized recordings of electromyography (EMG) data and 4-D skeleton data extracted from a single RGBD camera. Classification accuracy was improved significantly in both Mocap and noisy modalities.
Gu X, 2020, Cross-Subject and Cross-Modal Transfer for Generalized Abnormal Gait Pattern Recognition, IEEE Transactions on Neural Networks and Learning Systems, ISSN: 1045-9227
Zhang D, Wu Z, Chen J, et al., 2020, Automatic Microsurgical Skill Assessment Based on Cross-Domain Transfer Learning, IEEE ROBOTICS AND AUTOMATION LETTERS, Vol: 5, Pages: 4148-4155, ISSN: 2377-3766
Kassanos P, Berthelot M, Kim JA, et al., 2020, Smart sensing for surgery from tethered devices to wearables and implantables, IEEE Systems Man and Cybernetics Magazine, Vol: 6, Pages: 39-48, ISSN: 2333-942X
Recent developments in wearable electronics have fueled research into new materials, sensors, and microelectronic technologies for the realization of devices that have increased functionality and performance. This is further enhanced by advances in fabr ication methods and printing techniques, stimulating research on implantables and the advancement of existing medical devices. This article provides an overview of new designs, embodiments, fabrication methods, instrumentation, and informatics as well as the challenges in developing and deploying such devices and clinical applications that can benefit from them. The need for and use of these technologies across the perioperative surgical-care pathway are highlighted, along with a vision for the future and how these tools can be adopted by potential end users and health-care systems.
Lo FPW, Sun Y, Qiu J, et al., 2020, Image-based food classification and volume estimation for dietary assessment: a review., IEEE Journal of Biomedical and Health Informatics, Vol: 24, Pages: 1926-1939, ISSN: 2168-2194
A daily dietary assessment method named 24-hour dietary recall has commonly been used in nutritional epidemiology studies to capture detailed information of the food eaten by the participants to help understand their dietary behaviour. However, in this self-reporting technique, the food types and the portion size reported highly depends on users' subjective judgement which may lead to a biased and inaccurate dietary analysis result. As a result, a variety of visual-based dietary assessment approaches have been proposed recently. While these methods show promises in tackling issues in nutritional epidemiology studies, several challenges and forthcoming opportunities, as detailed in this study, still exist. This study provides an overview of computing algorithms, mathematical models and methodologies used in the field of image-based dietary assessment. It also provides a comprehensive comparison of the state of the art approaches in food recognition and volume/weight estimation in terms of their processing speed, model accuracy, efficiency and constraints. It will be followed by a discussion on deep learning method and its efficacy in dietary assessment. After a comprehensive exploration, we found that integrated dietary assessment systems combining with different approaches could be the potential solution to tackling the challenges in accurate dietary intake assessment.
Xiong J, Liang X, Zhao L, et al., 2020, Improving accuracy of heart failure detection using data refinement, Entropy: international and interdisciplinary journal of entropy and information studies, Vol: 22, Pages: 520-520, ISSN: 1099-4300
Due to the wide inter- and intra-individual variability, short-term heart rate variability (HRV) analysis (usually 5 min) might lead to inaccuracy in detecting heart failure. Therefore, RR interval segmentation, which can reflect the individual heart condition, has been a key research challenge for accurate detection of heart failure. Previous studies mainly focus on analyzing the entire 24-h ECG recordings from all individuals in the database which often led to poor detection rate. In this study, we propose a set of data refinement procedures, which can automatically extract heart failure segments and yield better detection of heart failure. The procedures roughly contain three steps: (1) select fast heart rate sequences, (2) apply dynamic time warping (DTW) measure to filter out dissimilar segments, and (3) pick out individuals with large numbers of segments preserved. A physical threshold-based Sample Entropy (SampEn) was applied to distinguish congestive heart failure (CHF) subjects from normal sinus rhythm (NSR) ones, and results using the traditional threshold were also discussed. Experiment on the PhysioNet/MIT RR Interval Databases showed that in SampEn analysis (embedding dimension m = 1, tolerance threshold r = 12 ms and time series length N = 300), the accuracy value after data refinement has increased to 90.46% from 75.07%. Meanwhile, for the proposed procedures, the area under receiver operating characteristic curve (AUC) value has reached 95.73%, which outperforms the original method (i.e., without applying the proposed data refinement procedures) with AUC of 76.83%. The results have shown that our proposed data refinement procedures can significantly improve the accuracy in heart failure detection.
Varghese RJ, Nguyen A, Burdet E, et al., 2020, Nonlinearity Compensation in A Multi-DoF Shoulder Sensing Exosuit for Real-Time Teleoperation, 2020 3rd IEEE International Conference on Soft Robotics, RoboSoft 2020, Pages: 668-675
© 2020 IEEE. The compliant nature of soft wearable robots makes them ideal for complex multiple degrees of freedom (DoF) joints, but also introduce additional structural nonlinearities. Intuitive control of these wearable robots requires robust sensing to overcome the inherent nonlinearities. This paper presents a joint kinematics estimator for a bio-inspired multi-DoF shoulder exosuit capable of compensating the encountered nonlinearities. To overcome the nonlinearities and hysteresis inherent to the soft and compliant nature of the suit, we developed a deep learning-based method to map the sensor data to the joint space. The experimental results show that the new learning-based framework outperforms recent state-of-the-art methods by a large margin while achieving 12ms inference time using only a GPU-based edge-computing device. The effectiveness of our combined exosuit and learning framework is demonstrated through real-time teleoperation with a simulated NAO humanoid robot.
Varghese RJ, Lo BPL, Yang G-Z, 2020, Design and Prototyping of a Bio-Inspired Kinematic Sensing Suit for the Shoulder Joint: Precursor to a Multi-DoF Shoulder Exosuit, Publisher: IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Jobarteh ML, McCrory MA, Lo B, et al., 2020, Development and validation of objective, passive dietary assessment Method for estimating food and nutrient intake in households in Low and Middle-Income Countries (LMICs): a study protocol, Current Developments in Nutrition, Vol: 4, Pages: 1-11, ISSN: 2475-2991
Malnutrition is a major concern in low- and middle-income countries (LMIC), but the full extent of nutritional deficiencies remains unknown largely due to lack of accurate assessment methods. This study seeks to develop and validate an objective, passive method of estimating food and nutrient intake in households in Ghana and Uganda. Household members (including under-5s and adolescents) are assigned a wearable camera device to capture images of their food intake during waking hours. Using custom software, images captured are then used to estimate an individual's food and nutrient (i.e., protein, fat, carbohydrate, energy, and micronutrients) intake. Passive food image capture and assessment provides an objective measure of food and nutrient intake in real time, minimizing some of the limitations associated with self-reported dietary intake methods. Its use in LMIC could potentially increase the understanding of a population's nutritional status, and the contribution of household food intake to the malnutrition burden. This project is registered at clinicaltrials.gov (NCT03723460).
Zhang Y, Guo Y, Yang P, et al., 2020, Epilepsy Seizure Prediction on EEG Using Common Spatial Pattern and Convolutional Neural Network, IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS, Vol: 24, Pages: 465-474, ISSN: 2168-2194
Zhang Y, Zhang Y, Lo B, et al., 2020, Wearable ECG signal processing for automated cardiac arrhythmia classification using CFASE‐based feature selection, Expert Systems, Vol: 37, Pages: 1-13, ISSN: 0266-4720
Classification of electrocardiogram (ECG) signals is obligatory for the automatic diagnosis of cardiovascular disease. With the recent advancement of low‐cost wearable ECG device, it becomes more feasible to utilize ECG for cardiac arrhythmia classification in daily life. In this paper, we propose a lightweight approach to classify five types of cardiac arrhythmia, namely, normal beat (N), atrial premature contraction (A), premature ventricular contraction (V), left bundle branch block beat (L), and right bundle branch block beat (R). The combined method of frequency analysis and Shannon entropy is applied to extract appropriate statistical features. Information gain criterion is employed to select features that the results show that 10 highly effective features can obtain performance measures comparable to those obtained by using the complete features. The selected features are then fed to the input of Random Forest, K‐Nearest Neighbour, and J48 for classification. To evaluate classification performance, tenfold cross validation is used to verify the effectiveness of our method. Experimental results show that Random Forest classifier demonstrates significant performance with the highest sensitivity of 98.1%, the specificity of 99.5%, the precision of 98.1%, and the accuracy of 98.08%, outperforming other representative approaches for automated cardiac arrhythmia classification.
Lo FP-W, Sun Y, Qiu J, et al., 2020, Point2Volume: A vision-based dietary assessment approach using view synthesis, IEEE Transactions on Industrial Informatics, Vol: 16, Pages: 577-586, ISSN: 1551-3203
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.
Zheng Y, Ghovanloo M, Lo BPL, et al., 2019, Introduction to the special issue on wearable and flexible integrated sensors for screening, diagnostics, and treatment, IEEE Transactions on Biomedical Circuits and Systems, Vol: 13, Pages: 1300-1303, ISSN: 1932-4545
The papers in this special issue present a selection of high quality research papers on wearable and flexible integrated sensors for screening, diagnostics, and treatment. Emerging flexible and wearable physical sensing devices create huge potential for many vital healthcare and biomedical applications including artificial electronic skins, physiological monitoring and assessment systems, therapeutic and drug delivery platforms, etc. Monitoring of vital physiological parameters in hospital and/or home environments has been of tremendous interests to healthcare practitioners for a long time. Robust and reliable sensors with excellent flexibility and stretchability are essential in the development of pervasive health monitoring systems with the capability of continuously tracking physiological signals of human body without conspicuous discomfort and invasiveness.
Lo B, Zhang Y, Inan OT, et al., 2019, Guest editorial: special issue on pervasive sensing and machine learning for mental health, IEEE Journal of Biomedical and Health Informatics, Vol: 23, Pages: 2245-2246, ISSN: 2168-2194
The seven papers included in this special section focus on machine learning applications for the mental health industry. Mental health is one of the major global health issues affecting substantially more people than other noncommunicable diseases. Much research has been focused on developing novel technologies for tackling this global health challenge, including the development of advanced analytical techniques based on extensive datasets and multimodal acquisition for early detection and treatment of mental illnesses. The papers in this issue are dedicated to cover the related topics on technological advancements for mental health care and diagnosis with a focus on pervasive sensing and machine learning.
Berthelot M, Ashcroft J, Boshier P, et al., 2019, Use of near infrared spectroscopy and implantable Doppler for postoperative monitoring of free tissue transfer for breast reconstruction: a systematic review and meta-analysis, Plastic and Reconstructive Surgery Global Open, Vol: 7, Pages: 1-8, ISSN: 2169-7574
Background: Failure to accurately assess the perfusion of free tissue transfer (FTT) in the early postoperative periodmay contribute to failure, which is a source of major patient morbidity and healthcare costs.Goal: This systematic review and meta-analysis aims to evaluate and appraise current evidence for the use of nearinfrared spectroscopy (NIRS) and/or implantable Doppler (ID) devices compared with conventional clinicalassessment (CCA) for postoperative monitoring of FTT in reconstructive breast surgery.Methods: A systematic literature search was performed in accordance with the PRISMA guidelines. Studies in humansubjects published within the last decade relevant to the review question were identified. Meta-analysis using randomeffects models of FTT failure rate and STARD scoring were then performed on the retrieved publications.Results: 19 studies met the inclusions criteria. For NIRS and ID, the mean sensitivity for the detection of FTT failure is99.36% and 100% respectively, with average specificity of 99.36% and 97.63% respectively. From studies withsufficient reported data, meta-analysis results demonstrated that both NIRS (OR = 0.09 [0.02, 0.36], P < 0.001) and ID(OR = 0.39 [0.27, 0.95], P = 0.04) were associated with significant reduction of FTT failure rates compared to CCA.Conclusion: The use of ID and NIRS provide equivalent outcomes in detecting FTT failure and were superior to CCA.The ability to acquire continuous objective physiological data regarding tissue perfusion is a perceived advantage ofthese techniques. Reduced clinical staff workload and minimised hospital costs are also perceived as positiveconsequences of their use.
Chen C-M, Kwasnicki RM, Curto VF, et al., 2019, Tissue oxygenation sensor and an active in vitro phantom for sensor Validation, IEEE Sensors Journal, Vol: 19, Pages: 8233-8240, ISSN: 1530-437X
A free flap is a tissue reconstruction procedure where healthy tissue is harvested to cover up vital structures after wound debridement. Microvascular anastomoses are carried out to join the arteries and veins of the flap with recipient vessels near the target site. Continuous monitoring is required to identify the flap failure and enable early intervention to salvage the flap. Although there are medical instruments that can assist surgeons in monitoring flap viability, high upfront costs and time-consuming data interpretation have hindered the use of such technologies in practice. Surgeons still rely largely on the clinical examination to monitor flaps after operations. This paper presents a low-cost, low-power (6.6 mW), and miniaturized Hamlyn StO 2 (tissue oxygen saturation) sensor that can be embodied as a plaster and attached to a flap for real-time monitoring. Similar to the design of oxygen saturation (SpO 2 /SaO 2 ) sensors, the Hamlyn StO 2 sensor was designed based on photoplethysmography (PPG), but with a different target of quantifying tissue perfusion rather than capturing pulsatile flow. To understand the spectral response to oxygenation/deoxygenation and vascular flow, an active in vitro silicone phantom was developed. The new sensor was validated using the silicone phantom and compared with a commercially available photospectroscopy and laser Doppler machine (O2C, LEA, Germany). In addition, in vivo experiments were conducted using a brachial pressure cuff forearm ischemia model. The experiment results have shown a high correlation between the proposed sensor and the O2C machine (r = 0.672 and p <; 0.001), demonstrating the potential value of the of the proposed low-cost sensor in post-operative free flap monitoring.
Qiu J, Lo FP-W, Sun Y, et al., 2019, British Machine Vision Conference 2019, BMVC 2019
Qiu J, Lo FPW, Sun Y, et al., 2019, Mining discriminative food regions for accurate food recognition, BMVC 2019, Publisher: British Machine Vision Conference
Automatic food recognition is the very first step towards passive dietary monitoring. In this paper, we address the problem of food recognition by mining discriminative food regions. Taking inspiration from Adversarial Erasing, a strategy that progressively discovers discriminative object regions for weakly supervised semantic segmentation, we propose a novel network architecture in which a primary network maintains the base accuracy of classifying an input image, an auxiliary network adversarially mines discriminative food regions, and a region network classifies the resulting mined regions. The global (the original input image) and the local (the mined regions) representations are then integrated for the final prediction. The proposed architecture denoted as PAR-Net is end-to-end trainable, and highlights discriminative regions in an online fashion. In addition, we introduce a new fine-grained food dataset named as Sushi-50, which consists of 50 different sushi categories. Extensive experiments have been conducted to evaluate the proposed approach. On three food datasets chosen (Food-101, Vireo-172, andSushi-50), our approach performs consistently and achieves state-of-the-art results (top-1 testing accuracy of 90:4%, 90:2%, 92:0%, respectively) compared with other existing approaches.
Guo Y, Sun M, Lo FPW, et al., 2019, Visual guidance and automatic control for robotic personalized stent graft manufacturing, 2019 International Conference on Robotics and Automation (ICRA), Publisher: IEEE, Pages: 8740-8746
Personalized stent graft is designed to treat Abdominal Aortic Aneurysms (AAA). Due to the individual difference in arterial structures, stent graft has to be custom made for each AAA patient. Robotic platforms for autonomous personalized stent graft manufacturing have been proposed in recently which rely upon stereo vision systems for coordinating multiple robots for fabricating customized stent grafts. This paper proposes a novel hybrid vision system for real-time visual-sevoing for personalized stent-graft manufacturing. To coordinate the robotic arms, this system is based on projecting a dynamic stereo microscope coordinate system onto a static wide angle view stereo webcam coordinate system. The multiple stereo camera configuration enables accurate localization of the needle in 3D during the sewing process. The scale-invariant feature transform (SIFT) method and color filtering are implemented for stereo matching and feature identifications for object localization. To maintain the clear view of the sewing process, a visual-servoing system is developed for guiding the stereo microscopes for tracking the needle movements. The deep deterministic policy gradient (DDPG) reinforcement learning algorithm is developed for real-time intelligent robotic control. Experimental results have shown that the robotic arm can learn to reach the desired targets autonomously.
Zhang K, Chen C-M, Anastasova S, et al., 2019, Roll-to-roll processable OTFT-based amplifier and application for pH sensing, IEEE 16th International Conference on Wearable and Implantable Body Sensor Networks (BSN), Publisher: IEEE, ISSN: 2376-8886
The prospect of roll-to-roll (R2R) processable Organic Thin Film Transistors (OTFTs) and circuits has attracted attention due to their mechanical flexibility and low cost of manufacture. This work will present a flexible electronics application for pH sensing with flexible and wearable signal processing circuits. A transimpedance amplifier was designed and fabricated on a polyethylene naphthalate (PEN) substrate prototype sheet that consists of 54 transistors. Different types and current ratios of current mirrors were initially created and then a suitable simple 1:3 current mirror (200nA) was selected to present the best performance of the proposed OTFT based transimpedance amplifier (TIA). Finally, this transimpedance amplifier was connected to a customized needle-based pH sensor that was induced as microfluidic collector for potential disease diagnosis and healthcare monitoring.
Rosa BG, Anastasova-Ivanova S, Lo B, et al., 2019, Towards a fully automatic food intake recognition system using acoustic, image capturing and glucose measurements, IEEE 16th International Conference on Wearable and Implantable Body Sensor Networks (BSN), Publisher: IEEE, ISSN: 2376-8886
Food intake is a major healthcare issue in developed countries that has become an economic and social burden across all sectors of society. Bad food intake habits lead to increased risk for development of obesity in children, young people and adults, with the latter more prone to suffer from health diseases such as diabetes, shortening the life expectancy. Environmental, cultural and behavioural factors have been appointed to be responsible for altering the balance between energy intake and expenditure, resulting in excess body weight. Methods to counteract the food intake problem are vast and include self-reported food questionnaires, body-worn sensors that record the sound, pressure or movements in the mouth and GI tract or image-based approaches that recognize the different types of food being ingested. In this paper we present an ear-worn device to track food intake habits by recording the acoustic signal produced by the chewing movements as well as the glucose level amperiometrically. Combined with a small camera on a future version of the device, we hope to deliver a complete system to control dietary habits with caloric intake estimation during satiation and deficit during satiety periods, which can be adapted to the physiology of each user.
Chen S, Kang L, Lu Y, et al., 2019, Discriminative information added by wearable sensors for early screening - a case study on diabetic peripheral neuropathy, IEEE 16th International Conference on Wearable and Implantable Body Sensor Networks (BSN), Publisher: IEEE, Pages: 1-4, ISSN: 2376-8886
Wearable inertial sensors have demonstrated their potential to screen for various neuropathies and neurological disorders. Most such research has been based on classification algorithms that differentiate the control group from the pathological group, using biomarkers extracted from wearable data as predictors. However, such methods often lack quantitative evaluation of how much information provided by the wearable biomarkers contributes to the overall prediction. Despite promising results from internal cross validation, their utility in clinical practice remains unclear. In this paper, we highlight in a case study - early screening for diabetic peripheral neuropathy (DPN) - evaluation methods for quantifying the contribution of wearable inertial sensors. Using a quick-to-deploy wearable sensor system, we collected 106 in-hospital diabetic patients' gait data and developed logistic regression models to predict the risk of a diabetic patient having DPN. Adopting various metrics, we evaluated the discriminative information added by gait biomarkers and how much it improved screening. The results show that the proposed wearable system added useful information significantly to the existing clinical standards, and boosted the C-index significantly from 0.75 to 0.84, surpassing the current survey-based screening methods used in clinics.
Sun Y, Lo FP-W, Lo B, 2019, A deep learning approach on gender and age recognition using a single inertial sensor, IEEE 16th International Conference on Wearable and Implantable Body Sensor Networks (BSN), Publisher: IEEE, ISSN: 2376-8886
Extracting human attributes, such as gender and age, from biometrics have received much attention in recent years. Gender and age recognition can provide crucial information for applications such as security, healthcare, and gaming. In this paper, a novel deep learning approach on gender and age recognition using a single inertial sensors is proposed. The proposed approach is tested using the largest available inertial sensor-based gait database with data collected from more than 700 subjects. To demonstrate the robustness and effectiveness of the proposed approach, 10 trials of inter-subject Monte-Carlo cross validation were conducted, and the results show that the proposed approach can achieve an averaged accuracy of 86.6%±2.4% for distinguishing two age groups: teen and adult, and recognizing gender with averaged accuracies of 88.6%±2.5% and 73.9%±2.8% for adults and teens respectively.
Qiu J, Lo FP-W, Lo B, 2019, Assessing individual dietary intake in food sharing scenarios with a 360 camera and deep learning, IEEE 16th International Conference on Wearable and Implantable Body Sensor Networks (BSN), Publisher: IEEE, ISSN: 2376-8886
A novel vision-based approach for estimating individual dietary intake in food sharing scenarios is proposed in this paper, which incorporates food detection, face recognition and hand tracking techniques. The method is validated using panoramic videos which capture subjects' eating episodes. The results demonstrate that the proposed approach is able to reliably estimate food intake of each individual as well as the food eating sequence. To identify the food items ingested by the subject, a transfer learning approach is designed. 4, 200 food images with segmentation masks, among which 1,500 are newly annotated, are used to fine-tune the deep neural network for the targeted food intake application. In addition, a method for associating detected hands with subjects is developed and the outcomes of face recognition are refined to enable the quantification of individual dietary intake in communal eating settings.
Lo FP-W, Sun Y, Qiu J, et al., 2019, A novel vision-based approach for dietary assessment using deep learning view synthesis, IEEE 16th International Conference on Wearable and Implantable Body Sensor Networks (BSN), Publisher: IEEE, ISSN: 2376-8886
Dietary assessment system has proven as an effective tool to evaluate the eating behavior of patients suffering from diabetes and obesity. To assess the dietary intake, the traditional method is to carry out a 24-hour dietary recall (24HR), a structured interview aimed at capturing information on food items and portion size consumed by participants. However, unconscious biases are developed easily due to individual's subjective perception in this self-reporting technique which may lead to inaccuracy. Thus, this paper proposed a novel vision-based approach for estimating the volume of food items based on deep learning view synthesis and depth sensing techniques. In this paper, a point completion network is applied to perform 3D reconstruction of food items using a single depth image captured from any convenient viewing angle. Compared to previous approaches, the proposed method has addressed several key challenges in vision-based dietary assessment, such as view occlusion and scale ambiguity. Experiments have been carried out to examine this approach and showed the feasibility of the algorithm in accurate estimation of food volume.
Sun Y, Lo FPW, Lo B, 2019, EEG-based user identification system using 1D-convolutional long short-term memory neural networks, Expert Systems with Applications, Vol: 125, Pages: 259-267, ISSN: 0957-4174
Electroencephalographic (EEG) signals have been widely used in medical applications, yet the use of EEG signals as user identification systems for healthcare and Internet of Things (IoT) systems has only gained interests in the last few years. The advantages of EEG-based user identification systems lie in its dynamic property and uniqueness among different individuals. However, it is for this reason that manually designed features are not always adapted to the needs. Therefore, a novel approach based on 1D Convolutional Long Short-term Memory Neural Network (1D-Convolutional LSTM) for EEG-based user identification system is proposed in this paper. The performance of the proposed approach was validated with a public database consists of EEG data of 109 subjects. The experimental results showed that the proposed network has a very high averaged accuracy of 99.58%, when using only 16 channels of EEG signals, which outperforms the state-of-the-art EEG-based user identification methods. The combined use of CNNs and LSTMs in the proposed 1D-Convolutional LSTM can greatly improve the accuracy of user identification systems by utilizing the spatiotemporal features of the EEG signals with LSTM, and lowering cost of the systems by reducing the number of EEG electrodes used in the systems.
This data is extracted from the Web of Science and reproduced under a licence from Thomson Reuters. You may not copy or re-distribute this data in whole or in part without the written consent of the Science business of Thomson Reuters.