Publications
291 results found
Pavlidou A, Lo B, 2021, Artificial ear - a wearable device for the hearing impaired, 17th International Conference on Wearable and Implantable Body Sensor Networks (BSN), Publisher: IEEE, ISSN: 2376-8886
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Wang Y, Lo B, 2021, A Soft Inflatable Elbow-Assistive Robot for Children with Cerebral Palsy, 17th International Conference on Wearable and Implantable Body Sensor Networks (BSN), Publisher: IEEE, ISSN: 2376-8886
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Kang P, Jiang S, Shull PB, et al., 2021, Feasibility Validation on Healthy Adults of a Novel Active Vibrational Sensing Based Ankle Band for Ankle Flexion Angle Estimation, IEEE OPEN JOURNAL OF ENGINEERING IN MEDICINE AND BIOLOGY, Vol: 2, Pages: 314-319
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Lo FPW, Guo Y, Sun Y, et al., 2021, Deep3DRanker: A Novel Framework for Learning to Rank 3D Models with Self-Attention in Robotic Vision, IEEE International Conference on Robotics and Automation (ICRA), Publisher: IEEE, Pages: 4341-4347, ISSN: 1050-4729
Hu M, Kassanos P, Keshavarz M, et al., 2021, Electrical and Mechanical Characterization of Carbon-Based Elastomeric Composites for Printed Sensors and Electronics, International Conference on Flexible and Printable Sensors and Systems (FLEPS), Publisher: IEEE
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Chen X, Jiang S, Lo B, 2020, Subject-independent slow fall detection with wearable sensors via deep learning, 2020 IEEE SENSORS, Publisher: IEEE, Pages: 1-4
One of the major healthcare challenges is elderly fallers. A fall can lead to disabilities and even mortality. With the current Covid-19 pandemic, insufficient resources could be provided for the care of elderlies, and care workers often may not be able to visit them. Therefore, a fall may get undetected or delayed leading to serious harm or consequences. Automatic fall detection systems could provide the necessary detection and warnings for timely intervention. Although many sensor-based fall detection systems have been proposed, most systems focus on the sudden fall and have not considered the slow fall scenario, a typical fall instance for elderly fallers. In this paper, a robust activity (RA) and slow fall detection system is proposed. The system consists of a waist-worn wearable sensor embedded with an inertial measurement unit (IMU) and a barometer, and a reference ambient barometer. A deep neural network (DNN) is developed for fusing the sensor data and classifying fall events. The results have shown that the IMU-barometer design yield better detection of fall events and the DNN approach (90.33% accuracy) outperforms traditional machine learning algorithms.
Chen X, Jiang S, Li Z, et al., 2020, A pervasive respiratory monitoring sensor for COVID-19 pandemic, IEEE Open Journal of Engineering in Medicine and Biology, Vol: 2, Pages: 11-16, ISSN: 2644-1276
Goal: The SARS-CoV-2 viral infection could cause severe acute respiratory syndrome, disturbing the regular breathing and leading to continuous coughing. Automatic respiration monitoring systems could provide the necessary metrics and warnings for timely intervention, especially for those with mild symptoms. Current respiration detection systems are expensive and too obtrusive for any large-scale deployment. Thus, a low-cost pervasive ambient sensor is proposed. Methods: We will posit a barometer on the working desk and develop a novel signal processing algorithm with a sparsity-based filter to remove the similar-frequency noise. Three modes (coughing, breathing and others) will be conducted to detect coughing and estimate different respiration rates. Results: The proposed system achieved 97.33% accuracy of cough detection and 98.98% specificity of respiration rate estimation. Conclusions: This system could be used as an effective screening tool for detecting subjects suffering from COVID-19 symptoms and enable large scale monitoring of patients diagnosed with or recovering.
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
Blood glucose level needs to be monitored regularly to manage the health condition of hyperglycemic patients. The current glucose measurement approaches still rely on invasive techniques which are uncomfortable and raise the risk of infection. To facilitate daily care at home, in this article, we propose an intelligent, noninvasive blood glucose monitoring system which can differentiate a user's blood glucose level into normal, borderline, and warning based on smartphone photoplethysmography (PPG) signals. The main implementation processes of the proposed system include 1) a novel algorithm for acquiring PPG signals using only smartphone camera videos; 2) a fitting-based sliding window algorithm to remove varying degrees of baseline drifts and segment the signal into single periods; 3) extracting characteristic features from the Gaussian functions by comparing PPG signals at different blood glucose levels; 4) categorizing the valid samples into three glucose levels by applying machine learning algorithms. Our proposed system was evaluated on a data set of 80 subjects. Experimental results demonstrate that the system can separate valid signals from invalid ones at an accuracy of 97.54% and the overall accuracy of estimating the blood glucose levels reaches 81.49%. The proposed system provides a reference for the introduction of noninvasive blood glucose technology into daily or clinical applications. This article also indicates that smartphone-based PPG signals have great potential to assess an individual's blood glucose level.
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
The fifteen articles in this special section focus on data science used in smart healthcare applications. A shift toward a data-driven socio-economic health model is occurring. This is the result of the increased volume, velocity and variety of data collected from the public and private sector in healthcare, and biology in general. In the past five-years, there has been an impressive development of computational intelligence and informatics methods for application to health and biomedical science. However, the effective use of data to address the scale and scope of human health problems has yet to realize its full potential. The barriers limiting the impact of practical application of standard data mining and machine learning methods have been inherent to the characteristics of health data. Besides the volume of the data (‘big data’), these are challenging due to their heterogeneity, complexity, variability and dynamic nature. Finally, data management and interpretability of the results have been limited by practical challenges in implementing new and also existing standards across the different health providers and research institutions. The scope of this Special issue is to discuss some of these challenges and opportunities in health and biological data science, with particular focus on the infrastructure, software, methods and algorithms needed to analyze large datasets in biological and clinical research.
Mu F, Gu X, Guo Y, et al., 2020, Unsupervised domain adaptation for position-independent IMU based gait analysis, 2020 IEEE SENSORS, Publisher: IEEE, Pages: 1-4
Inertial measurement units (IMUs) together with advanced machine learning algorithms have enabled pervasive gait analysis. However, the worn positions of IMUs can be varied due to movements, and they are difficult to standardize across different trials, causing signal variations. Such variation contributes to a bias in the underlying distribution of training and testing data, and hinder the generalization ability of a computational gait analysis model. In this paper, we propose a position-independent IMU based gait analysis framework based on unsupervised domain adaptation. It is based on transferring knowledge from the trained data positions to a novel position without labels. Our framework was validated on gait event detection and pathological gait pattern recognition tasks based on different computational models and achieved consistently high performance on both tasks.
Chen J, Zhang D, Munawar A, et al., 2020, Supervised semi-autonomous control for surgical robot based on Banoian optimization, 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Publisher: IEEE, Pages: 2943-2949
The recent development of Robot-Assisted Minimally Invasive Surgery (RAMIS) has brought much benefit to ease the performance of complex Minimally Invasive Surgery (MIS) tasks and lead to more clinical outcomes. Compared to direct master-slave manipulation, semi-autonomous control for the surgical robot can enhance the efficiency of the operation, particularly for repetitive tasks. However, operating in a highly dynamic in-vivo environment is complex. Supervisory control functions should be included to ensure flexibility and safety during the autonomous control phase. This paper presents a haptic rendering interface to enable supervised semi-autonomous control for a surgical robot. Bayesian optimization is used to tune user-specific parameters during the surgical training process. User studies were conducted on a customized simulator for validation. Detailed comparisons are made between with and without the supervised semi-autonomous control mode in terms of the number of clutching events, task completion time, master robot end-effector trajectory and average control speed of the slave robot. The effectiveness of the Bayesian optimization is also evaluated, demonstrating that the optimized parameters can significantly improve users' performance. Results indicate that the proposed control method can reduce the operator's workload and enhance operation efficiency.
Zhang D, Lo FP-W, Zheng J-Q, et al., 2020, Data-driven microscopic pose and depth estimation for optical microrobot manipulation, ACS Photonics, Vol: 7, Pages: 3003-3014, ISSN: 2330-4022
Optical microrobots have a wide range of applications in biomedical research for both in vitro and in vivo studies. In most microrobotic systems, the video captured by a monocular camera is the only way for visualizing the movements of microrobots, and only planar motion, in general, can be captured by a monocular camera system. Accurate depth estimation is essential for 3D reconstruction or autofocusing of microplatforms, while the pose and depth estimation are necessary to enhance the 3D perception of the microrobotic systems to enable dexterous micromanipulation and other tasks. In this paper, we propose a data-driven method for pose and depth estimation in an optically manipulated microrobotic system. Focus measurement is used to obtain features for Gaussian Process Regression (GPR), which enables precise depth estimation. For mobile microrobots with varying poses, a novel method is developed based on a deep residual neural network with the incorporation of prior domain knowledge about the optical microrobots encoded via GPR. The method can simultaneously track microrobots with complex shapes and estimate the pose and depth values of the optical microrobots. Cross-validation has been conducted to demonstrate the submicron accuracy of the proposed method and precise pose and depth perception for microrobots. We further demonstrate the generalizability of the method by adapting it to microrobots of different shapes using transfer learning with few-shot calibration. Intuitive visualization is provided to facilitate effective human-robot interaction during micromanipulation based on pose and depth estimation results.
Zhang D, Barbot A, Lo B, et al., 2020, Distributed force control for microrobot manipulation via planar multi-spot optical tweezer, Advanced Optical Materials, Vol: 8, Pages: 1-15, ISSN: 2195-1071
Optical tweezers (OT) represent a versatile tool for micro‐manipulation. To avoid damages to living cells caused by illuminating laser directly on them, microrobots controlled by OT can be used for manipulation of cells or living organisms in microscopic scale. Translation and planar rotation motion of microrobots can be realized by using a multi‐spot planar OT. However, out‐of‐plane manipulation of microrobots is difficult to achieve with a planar OT. This paper presents a distributed manipulation scheme based on multiple laser spots, which can control the out‐of‐plane pose of a microrobot along multiple axes. Different microrobot designs have been investigated and fabricated for experimental validation. The main contributions of this paper include: i) development of a generic model for the structure design of microrobots which enables multi‐dimensional (6D) control via conventional multi‐spot OT; ii) introduction of the distributed force control for microrobot manipulation based on characteristic distance and power intensity distribution. Experiments are performed to demonstrate the effectiveness of the proposed method and its potential applications, which include indirect manipulation of micro‐objects.
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
Excessive stress is one of the main causes of mental illness. Long-term exposure of stress could affect one's physiological wellbeing (such as hypertension) and psychological condition (such as depression). Multisensory information such as heart rate variability (HRV) and pH can provide suitable information about mental and physical stress. This paper proposes a novel approach for stress condition monitoring using disposable flexible sensors. By integrating flexible amplifiers with a commercially available flexible polyvinylidene difluoride (PVDF) mechanical deformation sensor and a pH-type chemical sensor, the proposed system can detect arterial pulses from the neck and pH levels from sweat located in the back of the body. The system uses organic thin film transistor (OTFT)-based signal amplification front-end circuits with modifications to accommodate the dynamic signal ranges obtained from the sensors. The OTFTs were manufactured on a low-cost flexible polyethylene naphthalate (PEN) substrate using a coater capable of Roll-to-Roll (R2R) deposition. The proposed system can capture physiological indicators with data interrogated by Near Field Communication (NFC). The device has been successfully tested with healthy subjects, demonstrating its feasibility for real-time stress monitoring.
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
The assessment of microsurgical skills for Robot-Assisted Microsurgery (RAMS) still relies primarily on subjective observations and expert opinions. A general and automated evaluation method is desirable. Deep neural networks can be used for skill assessment through raw kinematic data, which has the advantages of being objective and efficient. However, one of the major issues of deep learning for the analysis of surgical skills is that it requires a large database to train the desired model, and the training process can be time-consuming. This letter presents a transfer learning scheme for training a model with limited RAMS datasets for microsurgical skill assessment. An in-house Microsurgical Robot Research Platform Database (MRRPD) is built with data collected from a microsurgical robot research platform (MRRP). It is used to verify the proposed cross-domain transfer learning for RAMS skill level assessment. The model is fine-tuned after training with the data obtained from the MRRP. Moreover, microsurgical tool tracking is developed to provide visual feedback while task-specific metrics and the other general evaluation metrics are provided to the operator as a reference. The method proposed has shown to offer the potential to guide the operator to achieve a higher level of skills for microsurgical operation.
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.
Varghese RJ, Nguyen A, Burdet E, et al., 2020, Nonlinearity compensation in a multi-DoF shoulder sensing exosuit for real-time teleoperation, 3rd IEEE International Conference on Soft Robotics (RoboSoft), Publisher: IEEE, Pages: 668-675
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.
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, 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, IEEE Robotics and Automation Letters, Vol: 5, Pages: 540-547, ISSN: 2377-3766
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.
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
Epilepsy seizure prediction paves the way of timely warning for patients to take more active and effective intervention measures. Compared to seizure detection that only identifies the inter-ictal state and the ictal state, far fewer researches have been conducted on seizure prediction because the high similarity makes it challenging to distinguish between the pre-ictal state and the inter-ictal state. In this paper, a novel solution on seizure prediction is proposed using common spatial pattern (CSP) and convolutional neural network (CNN). Firstly, artificial preictal EEG signals based on the original ones are generated by combining the segmented pre-ictal signals to solve the trial imbalance problem between the two states. Secondly, a feature extractor employing wavelet packet decomposition and CSP is designed to extract the distinguishing features in both the time domain and the frequency domain. It can improve overall accuracy while reducing the training time. Finally, a shallow CNN is applied to discriminate between the pre-ictal state and the inter-ictal state. Our proposed solution is evaluated on 23 patients' data from Boston Children's Hospital-MIT scalp EEG dataset by employing a leave-one-out cross-validation, and it achieves a sensitivity of 92.2% and false prediction rate of 0.12/h. Experimental result demonstrates that the proposed approach outperforms most state-of-the-art methods.
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.
Sun Y, Lo FP-W, Lo B, 2019, Security and privacy for the internet of medical things enabled healthcare systems: a survey, IEEE Access, Vol: 7, Pages: 183339-183355, ISSN: 2169-3536
With the increasing demands on quality healthcare and the raising cost of care, pervasive healthcare is considered as a technological solutions to address the global health issues. In particular, the recent advances in Internet of Things have led to the development of Internet of Medical Things (IoMT). Although such low cost and pervasive sensing devices could potentially transform the current reactive care to preventative care, the security and privacy issues of such sensing system are often overlooked. As the medical devices capture and process very sensitive personal health data, the devices and their associated communications have to be very secured to protect the user's privacy. However, the miniaturized IoMT devices have very limited computation power and fairly limited security schemes can be implemented in such devices. In addition, with the widespread use of IoMT devices, managing and ensuring the security of IoMT systems are very challenging and which are the major issues hindering the adoption of IoMT for clinical applications. In this paper, the security and privacy challenges, requirements, threats, and future research directions in the domain of IoMT are reviewed providing a general overview of the state-of-the-art approaches.
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.
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