197 results found
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
© 2019 Elsevier Ltd 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.
Bernstein A, Varghese RJ, Liu J, et al., 2019, An Assistive Ankle Joint Exoskeleton for Gait Impairment, Biosystems and Biorobotics, Pages: 658-662
© 2019, Springer Nature Switzerland AG. Motor rehabilitation and assistance post-stroke are becoming a major concern for healthcare services with an increasingly aging population. Wearable robots can be a technological solution to support gait rehabilitation and to provide assistance to enable users to carry out activities of daily living independently. To address the need for long-term assistance for stroke survivors suffering from drop foot, this paper proposes a low-cost, assistive ankle joint exoskeleton for gait assistance. The proposed exoskeleton is designed to provide ankle foot support thus enabling normal walking gait. Baseline gait reading was recorded from two force sensors attached to a custom-built shoe insole of the exoskeleton. From our experiments, the average maximum force during heel-strike (63.95 N) and toe-off (54.84 N) were found, in addition to the average period of a gait cycle (1.45 s). The timing and force data were used to control the actuation of tendons of the exoskeleton to prevent the foot from preemptively hitting the ground during swing phase.
Singh RK, Varghese RJ, Liu J, et al., 2019, A multi-sensor fusion approach for intention detection, Biosystems and Biorobotics, Pages: 454-458
© Springer Nature Switzerland AG 2019. For assistive devices to seamlessly and promptly assist users with activities of daily living (ADL), it is important to understand the user’s intention. Current assistive systems are mostly driven by unimodal sensory input which hinders their accuracy and responses. In this paper, we propose a context-aware sensor fusion framework to detect intention for assistive robotic devices which fuses information from a wearable video camera and wearable inertial measurement unit (IMU) sensors. A Naive Bayes classifier is used to predict the intent to move from IMU data and the object classification results from the video data. The proposed approach can achieve an accuracy of 85.2% in detecting movement intention.
Ahmed MR, Zhang Y, Feng Z, et al., 2019, Neuroimaging and Machine Learning for Dementia Diagnosis: Recent Advancements and Future Prospects., IEEE Rev Biomed Eng, Vol: 12, Pages: 19-33
Dementia, a chronic and progressive cognitive declination of brain function caused by disease or impairment, is becoming more prevalent due to the aging population. A major challenge in dementia is achieving accurate and timely diagnosis. In recent years, neuroimaging with computer-aided algorithms have made remarkable advances in addressing this challenge. The success of these approaches is mostly attributed to the application of machine learning techniques for neuroimaging. In this review paper, we present a comprehensive survey of automated diagnostic approaches for dementia using medical image analysis and machine learning algorithms published in the recent years. Based on the rigorous review of the existing works, we have found that, while most of the studies focused on Alzheimer's disease, recent research has demonstrated reasonable performance in the identification of other types of dementia remains a major challenge. Multimodal imaging analysis deep learning approaches have shown promising results in the diagnosis of these other types of dementia. The main contributions of this review paper are as follows. 1) Based on the detailed analysis of the existing literature, this paper discusses neuroimaging procedures for dementia diagnosis. 2) It systematically explains the most recent machine learning techniques and, in particular, deep learning approaches for early detection of dementia.
Lo FP-W, Sun Y, Qiu J, et al., 2018, Food Volume Estimation Based on Deep Learning View Synthesis from a Single Depth Map, NUTRIENTS, Vol: 10, ISSN: 2072-6643
Sun Y, Lo B, 2018, An artificial neural network framework for gait based biometrics, IEEE Journal of Biomedical and Health Informatics, ISSN: 2168-2194
OAPA As the popularity of wearable and implantable Body Sensor Network (BSN) devices increases, there is a growing concern regarding the data security of such power-constrained miniaturized medical devices. With limited computational power, BSN devices are often not able to provide strong security mechanisms to protect sensitive personal and health information, such as one's physiological data. Consequently, many new methods of securing Wireless Body Area Networks (WBANs) have been proposed recently. One effective solution is the Biometric Cryptosystem (BCS) approach. BCS exploits physiological and behavioral biometric traits, including face, iris, fingerprints, Electrocardiogram (ECG), and Photoplethysmography (PPG). In this paper, we propose a new BCS approach for securing wireless communications for wearable and implantable healthcare devices using gait signal energy variations and an Artificial Neural Network (ANN) framework. By simultaneously extracting similar features from BSN sensors using our approach, binary keys can be generated on demand without user intervention. Through an extensive analysis on our BCS approach using a gait dataset, the results have shown that the binary keys generated using our approach have high entropy for all subjects. The keys can pass both NIST and Dieharder statistical tests with high efficiency. The experimental results also show the robustness of the proposed approach in terms of the similarity of intra-class keys and the discriminability of the inter-class keys.
Teachasrisaksakul K, Wu L, Yang G-Z, et al., 2018, Hand Gesture Recognition with Inertial Sensors., 40th International Conference of the IEEE Engineering in Medicine and Biology Society, Publisher: IEEE, Pages: 3517-3520, ISSN: 1557-170X
Dyscalculia is a learning difficulty hindering fundamental arithmetical competence. Children with dyscalculia often have difficulties in engaging in lessons taught with traditional teaching methods. In contrast, an educational game is an attractive alternative. Recent educational studies have shown that gestures could have a positive impact in learning. With the recent development of low cost wearable sensors, a gesture based educational game could be used as a tool to improve the learning outcomes particularly for children with dyscalculia. In this paper, two generic gesture recognition methods are proposed for developing an interactive educational game with wearable inertial sensors. The first method is a multilayered perceptron classifier based on the accelerometer and gyroscope readings to recognize hand gestures. As gyroscope is more power demanding and not all low-cost wearable device has a gyroscope, we have simplified the method using a nearest centroid classifier for classifying hand gestures with only the accelerometer readings. The method has been integrated into open-source educational games. Experimental results based on 5 subjects have demonstrated the accuracy of inertial sensor based hand gesture recognitions. The results have shown that both methods can recognize 15 different hand gestures with the accuracy over 93%.
Berthelot M, Yang GZ, Lo B, 2018, Tomographic probe for perfusion analysis in deep layer tissue, Pages: 86-89
© 2018 IEEE. Continuous buried soft tissue free flap postoperative monitoring is crucial to detect flap failure and enable early intervention. In this case, clinical assessment is challenging as the flap is buried and only implantable or hand held devices can be used for regular monitoring. These devices have limitations in their price, usability and specificity. Near-infrared spectroscopy (NIRS) has shown promising results for superficial free flap postoperative monitoring, but it has not been considered for buried free flap, mainly due to the limited penetration depth of conventional approaches. A wearable wireless tomographic probe has been developed for continuous monitoring of tissue perfusion at different depths. Using the NIRS method, blood flow can be continuously measured at different tissue depths. This device has been designed following conclusions of extensive computerised simulations and it has been validated using a vascular phantom.
Sun Y, Lo B, Random number generation using inertial measurement unit signals for on-body IoT devices, Living in the Internet of Things: Cybersecurity of the IoT - A PETRAS, IoTUK and IET Event, Publisher: IET
With increasing popularity of wearable and implantable tech-nologies for medical applications, there is a growing concernon the security and data protection of the on-body Internet-of-Things (IoT) devices. As a solution, cryptographic system isoften adopted to encrypt the data, and Random Number Gen-erator (RNG) is of vital importance to such system. This paperproposes a new random number generation method for secur-ing on-body IoT devices based on temporal signal variationsof the outputs of the Inertial Measurement Units (IMU) wornby the users while walking. As most new wearable and im-plantable devices have built-in IMUs and walking gait signalscan be extracted from these body sensors, this method can beapplied and integrated into the cryptographic systems of thesenew devices. To generate the random numbers, this method di-vides IMU signals into gait cycles and generates bits by com-paring energy differences between the sensor signals in a gaitcycle and the averaged IMU signals in multiple gait cycles.The generated bits are then re-indexed in descending orderby the absolute values of the associated energy differences tofurther randomise the data and generate high-entropy randomnumbers. Two datasets were used in the studies to generaterandom numbers, where were rigorously tested and passed fourwell-known randomness test suites, namely NIST-STS, ENT,Dieharder, and RaBiGeTe.
Lo BPL, Innovative Sensing Technologies for Developing Countries, IEEE Biomedical and Health Informatics BHI 2018
Friedl KE, Hixson JD, Buller MJ, et al., 2018, Guest editorial - 13th Body Sensor Networks Symposium, IEEE Journal of Biomedical and Health Informatics, Vol: 22, Pages: 3-4, ISSN: 2168-2194
Gao A, Lo P, Lo B, Food volume estimation for quantifying dietary intake with a wearable camera, Body Sensor Networks Conference 2018, Publisher: IEEE
A novel food volume measurement technique isproposed in this paper for accurate quantification of the dailydietary intake of the user. The technique is based on simul-taneous localisation and mapping (SLAM), a modified versionof convex hull algorithm, and a 3D mesh object reconstructiontechnique. This paper explores the feasibility of applying SLAMtechniques for continuous food volume measurement with amonocular wearable camera. A sparse map will be generatedby SLAM after capturing the images of the food item withthe camera and the multiple convex hull algorithm is appliedto form a 3D mesh object. The volume of the target objectcan then be computed based on the mesh object. Comparedto previous volume measurement techniques, the proposedmethod can measure the food volume continuously with no priorinformation such as pre-defined food shape model. Experimentshave been carried out to evaluate this new technique andshowed the feasibility and accuracy of the proposed algorithmin measuring food volume.
Sun Y, Yang G, Lo B, An artificial neural network framework for lower limb motion signal estimation with foot-mounted inertial sensors, IEEE Conference on Body Sensor Networks (BSN) 2018, Publisher: IEEE
This paper proposes a novel artificial neuralnetwork based method for real-time gait analysis with minimalnumber of Inertial Measurement Units (IMUs). Accurate lowerlimb attitude estimation has great potential for clinical gait di-agnosis for orthopaedic patients and patients with neurologicaldiseases. However, the use of multiple wearable sensors hinderthe ubiquitous use of inertial sensors for detailed gait analysis.This paper proposes the use of two IMUs mounted on theshoes to estimate the IMU signals at the shin, thigh and waistfor accurate attitude estimation of the lower limbs. By usingthe artificial neural network framework, the gait parameters,such as angle, velocity and displacements of the IMUs canbe estimated. The experimental results have shown that theproposed method can accurately estimate the IMUs signals onthe lower limbs based only on the IMU signals on the shoes,which demonstrates its potential for lower limb motion trackingand real-time gait analysis.
Gu X, Deligianni F, Lo B, et al., Markerless Gait Analysis Based on a Single RGB Camera, International Conference on Wearable and Implantable Body Sensor Networks, Publisher: IEEE
Lo BPL, Guo Y, Zhang Y, et al., Automated epileptic seizure detection by analyzing wearable EEG signals using extended correlation-based feature selection, IEEE BSN 2018, Publisher: IEEE
Electroencephalogram (EEG)that measures the electrical activity of the brainhasbeen widely employedfordiagnosingepilepsywhich is onekind of brainabnormalities. With theadvancement of low-costwearablebrain-computer interfacedevices,it is possible to monitor EEG forepileptic seizure detectionin daily use. However,it is still challenging to develop seizure classificationalgorithms with a considerable higheraccuracy and lower complexity. In this study, we proposea lightweight method which can reduce the number of features for a multiclass classificationto identify three different seizure statuses(i.e., Healthy, Interictal and Epileptic seizure)throughEEGsignalswith a wearable EEG sensorsusingExtended Correlation-Based Feature Selection(ECFS).More specifically, there are three steps in our proposed approach. Firstly, the EEG signals were segmented into fivefrequency bandsand secondly, we extractthe features while the unnecessary feature space was eliminated by developing the ECFS method.Finally, the features were fed intofive different classification algorithms, including Random Forest, Support Vector Machine, Logistic Model Trees, RBF Networkand Multilayer Perception. Experimental results have shownthatLogistic Model Treesprovides the highest accuracy of97.6% comparing toother classifiers.
Berthelot ME, Yang GZ, Lo B, 2017, A self-calibrated tissue viability sensor for free flap monitoring, IEEE Journal of Biomedical and Health Informatics, Vol: 22, Pages: 5-14, ISSN: 2168-2194
In fasciocutaneous free flap surgery, close postoperative monitoring is crucial for detecting flap failure, as around 10% of cases require additional surgery due to compromised anastomosis. Different biochemical and biophysical techniques have been developed for continuous flap monitoring, however, they all have shortcoming in terms of reliability, elevated cost, potential risks to the patient and inability to adapt to the patient's phenotype. A wearable wireless device based on near infrared spectroscopy (NIRS) has been developed for continuous blood flow and perfusion monitoring by quantifying tissue oxygen saturation (StO2). This miniaturized and low cost device is designed for postoperative monitoring of flap viability. With self-calibration, the device can adapt itself to the characteristics of the patients' skin such as tone and thickness. An extensive study was conducted with 32 volunteers. The experimental results show that the device can obtain reliable StO2 measurements across different phenotypes (age, sex, skin tone and thickness). To assess its ability to detect flap failure, the sensor was validated with an animal study. Free groin flaps were performed on 16 Sprague Dawley rats. Results demonstrate the accuracy of the sensor in assessing flap viability and identifying the origin of failure (venous or arterial thrombosis).
Deligianni F, Wong CW, Lo B, et al., 2017, A fusion framework to estimate plantar ground force distributions and ankle dynamics, Information Fusion, Vol: 41, Pages: 255-263, ISSN: 1566-2535
Gait analysis plays an important role in several conditions, including the rehabilitation of patients with orthopaedic problems and the monitoring of neurological conditions, mental health problems and the well-being of elderly subjects. It also constitutes an index of good posture and thus it can be used to prevent injuries in athletes and monitor mental health in typical subjects. Usually, accurate gait analysis is based on the measurement of ankle dynamics and ground reaction forces. Therefore, it requires expensive multi-camera systems and pressure sensors, which cannot be easily employed in a free-living environment. We propose a fusion framework that uses an ear worn activity recognition (e-AR) sensor and a single video camera to estimate foot angle during key gait events. To this end we use canonical correlation analysis with a fused-lasso penalty in a two-steps approach that firstly learns a model of the timing distribution of ground reaction forces based on e-AR signal only and subsequently models the eversion/inversion as well as the dorsiflexion of the ankle based on the combined features of e-AR sensor and the video. The results show that incorporating invariant features of angular ankle information from the video recordings improves the estimation of the foot progression angle, substantially.
Berthelot M, Yang GZ, Lo B, 2017, Preliminary study for hemodynamics monitoring using a wearable device network, Wearable and Implantable Body Sensor Networks (BSN), 2017 IEEE 14th International Conference on, Publisher: IEEE, Pages: 115-118
Blood flow, posture and phenotype (such as age, sex, smoking habit or physical activity) are closely related to vascular health. Episodic monitoring of the vascular system in clinical setting can lead to late diagnose. Inexpensive wearable devices for continuous monitoring of vascular parameters have been widely used, however, they often have limitations in data interpretation: changes in the environment setting can significantly affect the meaning of the results. This paper proposes a low cost networked body worn sensors for real-Time analysis of hemodynamics and reports preliminary results on the relation between blood flow (measured through pulse arrival time (PAT)), the effect of postures and age ranges based on experiments with 13 volunteers of different age ranges ( < 25 years old and > 50 years old). Standing, supine and sitting postures were investigated while photoplethysmograph (PPG) sensors were placed at different locations (ear, wrist and ankle). Results show the PAT changes according to the investigated locations and postures for both age group. Also, the average PAT values of the older group are generally higher than those of the younger group. In the older group, the average PAT value is higher for the supine posture than that of the sitting posture which is itself higher than that of the standing posture. In the younger group, the average PAT is higher in supine than that of the sitting and standing postures which have similar average PAT values. This indicates that hemodynamics vary with posture and age.
Sun Y, Wong C, Yang GZ, et al., 2017, Secure key generation using gait features for Body Sensor Networks, IEEE EMBS Annual International Body Sensor Networks Conference, Publisher: IEEE, Pages: 206-210
With increasing popularity of wearable and Body Sensor Networks technologies, there is a growing concern on the security and data protection of such low-power pervasive devices. With very limited computational power, BSN sensors often cannot provide the necessary data protection to collect and process sensitive personal information. Since conventional network security schemes are too computationally demanding for miniaturized BSN sensors, new methods of securing BSNs have proposed, in which Biometric Cryptosystem (BCS) appears to be an effective solution. With regards to BCS security solutions, physiological traits, such as an individual's face, iris, fingerprint, electrocardiogram (ECG), and photoplethysmogram (PPG) have been widely exploited. However, behavioural traits such as gait are rarely studied. In this paper, a novel lightweight symmetric key generation scheme based on the timing information of gait is proposed. By extracting similar timing information from gait acceleration signals simultaneously from body worn sensors, symmetric keys can be generated on all the sensor nodes at the same time. Based on the characteristics of generated keys and BSNs, a fuzzy commitment based key distribution scheme is also developed to distribute the keys amongst the sensor nodes.
Zhang R, Ravi D, Yang G-Z, et al., A personalized air quality sensing system – a preliminary study on assessing the air quality of London Underground stations, Body Sensor Networks Conference (BSN’17), Publisher: IEEE
Recent studies have shown that air pollution has a negative impacton people’s health, especially for patients with respiratory and cardiac diseases (e.g. COPD, asthma, ischemic heart disease). Although there are already many air quality monitoring stations in major cities, such as London, these stations are sparsely located, and the periodic collection of information is insufficient to provide the granularity needed to assess the environmental risk for an individual (e.g. to avoid exacerbation). Wearable devices, on the other hand, are more suitable in this context, providing a better estimation of the air quality in the proximity of the person. Therefore, relevant warnings and information on health risks can be provided in real-time. As a proof of concept, we have developed a wearable sensor for continuous monitoring of air quality around the user, and a preliminarystudy was conducted to validate the sensor and assess the air quality in London underground stations. Based on the PM2.5 (particulate matter with a diameter of 2.5μm), temperature and location information, a model is generated for predicting the air quality of each station at different times. Our preliminary results have shown that there are significant differences in air quality among stations and metro lines. It also demonstrates that wearable sensors can provide necessary information for users to make travel arrangements that minimize their exposure to polluted air.
Deligianni F, Ravi D, Roots S, et al., Pervasive monitoring of mental health for preventing financial distress, Body Sensor Networks Conference (BSN’17), Publisher: IEEE
Mental health disorders are rankedamong the top twenty main causes of disability worldwide. It was found that thereis an intriguing relationship between mental health problems and financial difficulties.Current technology uses mobile apps for self-monitoring of mental health conditions with a potential to avoid debt crisis caused by mental illness. In this paper, we propose the use of a wearable sensor as an objective evaluation tool for monitoring the emotional well-being of the subject. By fusing the sensory data with financial data, an intelligent self-guard system is proposed for preventing excessive spending caused by mental condition.
Ravi D, Wong C, Lo B, et al., 2016, A deep learning approach to on-node sensor data analytics for mobile or wearable devices, IEEE Journal of Biomedical and Health Informatics, Vol: 21, Pages: 56-64, ISSN: 2168-2208
The increasing popularity of wearable devices inrecent years means that a diverse range of physiological and functionaldata can now be captured continuously for applicationsin sports, wellbeing, and healthcare. This wealth of informationrequires efficient methods of classification and analysis wheredeep learning is a promising technique for large-scale data analytics.Whilst deep learning has been successful in implementationsthat utilize high performance computing platforms, its use onlow-power wearable devices is limited by resource constraints.In this paper, we propose a deep learning methodology, whichcombines features learnt from inertial sensor data together withcomplementary information from a set of shallow features toenable accurate and real-time activity classification. The design ofthis combined method aims to overcome some of the limitationspresent in a typical deep learning framework where on-nodecomputation is required. To optimize the proposed method forreal-time on-node computation, spectral domain pre-processingis used before the data is passed onto the deep learning framework.The classification accuracy of our proposed deep learningapproach is evaluated against state-of-the-art methods using bothlaboratory and real world activity datasets. Our results show thevalidity of the approach on different human activity datasets,outperforming other methods, including the two methods usedwithin our combined pipeline. We also demonstrate that thecomputation times for the proposed method are consistent withthe constraints of real-time on-node processing on smartphonesand a wearable sensor platform.
Zhang Y, Berthelot M, Lo BPL, 2016, Wireless Wearable Photoplethysmography Sensors for ContinuousBlood Pressure Monitoring, IEEE Wireless Health 2016, Publisher: IEEE
Blood Pressure (BP) is a crucial vital sign takeninto consideration for the general assessment of patient’s condition:patients with hypertension or hypotension are advisedto record their BP routinely. Particularly, hypertension isemphasized by stress, diabetic neuropathy and coronary heartdiseases and could lead to stroke. Therefore, routine andlong-term monitoring can enable early detection of symptomsand prevent life-threatening events. The gold standard methodfor measuring BP is the use of a stethoscope and sphygmomanometerto detect systolic and diastolic pressures. However,only discrete measurements are taken. To enable pervasiveand continuous monitoring of BP, recent methods have beenproposed: pulse arrival time (PAT) or PAT difference (PATD)between different body parts are based on the combinationof electrocardiogram (ECG) and photoplethysmography (PPG)sensors. Nevertheless, this technique could be quite obtrusiveas in addition to at least two contacts/electrodes to measurethe differential voltage across the left arm/leg/chest and theright arm/leg/chest, ECG measurements are easily corruptedby motion artefacts. Although such devices are small, wearableand relatively convenient to use, most devices are not designedfor continuous BP measurements. This paper introduces anovel PPG-based pervasive sensing platform for continuousmeasurements of BP. Based on the principle of using PAT toestimate BP, two PPG sensors are used to measure the PATDbetween the earlobe and the wrist to measure BP. The device iscompared with a gold standard PPG sensor and validation ofthe concept is conducted with a preliminary study involving 9healthy subjects. Results show that the mean BP and PATD arecorrelated with a 0.3 factor. This preliminary study shows thefeasibility of continuous monitoring of BP using a pair of PPGplaced on the ear lobe and wrist with PATD measurements ispossible.
Wijayasingha L, Lo BPL, 2016, A Wearable Sensing Framework for Improving Personal and Oral Hygiene for People with Developmental Disabilities, IEEE Wireless Health 2016, Publisher: IEEE
People with developmental disabilities often facedifficulties in coping with daily activities and many requireconstant support. One of the major health issues for peoplewith developmental disabilities is personal hygiene. Many lackthe ability, poor memory or lack of attention to carry outnormal daily activities like brushing teeth and washing hands.Poor personal hygiene may result in increased susceptibility toinfection and other health issues. To enable independent livingand improve the quality of care for people with developmentalabilities, this paper proposes a new wearable sensingframework to monitoring personal hygiene. Based on asmartwatch, this framework is designed as a pervasivemonitoring and learning tool to provide detailed evaluation andfeedback to the user on hand washing and tooth brushing. Apreliminary study was conducted to assess the performance ofthe approach, and the results showed the reliability androbustness of the framework in quantifying and assessing handwashing and tooth brushing activities.
With a massive influx of multimodality data, the roleof data analytics in health informatics has grown rapidly in thelast decade. This has also prompted increasing interests in thegeneration of analytical, data driven models based on machinelearning in health informatics. Deep learning, a technique withits foundation in artificial neural networks, is emerging in recentyears as a powerful tool for machine learning, promising toreshape the future of artificial intelligence. Rapid improvementsin computational power, fast data storage and parallelization havealso contributed to the rapid uptake of the technology in additionto its predictive power and ability to generate automaticallyoptimized high-level features and semantic interpretation fromthe input data. This article presents a comprehensive up-to-datereview of research employing deep learning in health informatics,providing a critical analysis of the relative merit and potentialpitfalls of the technique as well as its future outlook. The papermainly focuses on key applications of deep learning in the fields oftranslational bioinformatics, medical imaging, pervasive sensing,medical informatics and public health.
Ding X-R, Zhao N, Yang G-Z, et al., 2016, Continuous blood pressure measurement from invasive to unobtrusive: celebration of 200th birth anniversary of Carl Ludwig, IEEE Journal of Biomedical and Health Informatics, Vol: 20, Pages: 1455-1465, ISSN: 2168-2208
The year 2016 marks the 200th birth anniversary of Carl Friedrich Wilhelm Ludwig (1816-1895). As one of the most remarkable scientists, Ludwig invented the kymograph, which for the first time enabled the recording of continuous blood pressure (BP), opening the door to the modern study of physiology. Almost a century later, intraarterial BP monitoring through an arterial line has been used clinically. Subsequently, arterial tonometry and volume clamp method were developed and applied in continuous BP measurement in a noninvasive way. In the last two decades, additional efforts have been made to transform the method of unobtrusive continuous BP monitoring without the use of a cuff. This review summarizes the key milestones in continuous BP measurement; that is, kymograph, intraarterial BP monitoring, arterial tonometry, volume clamp method, and cuffless BP technologies. Our emphasis is on recent studies of unobtrusive BP measurements as well as on challenges and future directions.
Chen S, Lach J, Lo B, et al., 2016, Toward Pervasive Gait Analysis With Wearable Sensors: A Systematic Review, IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS, Vol: 20, Pages: 1521-1537, ISSN: 2168-2194
Huen D, Liu J, Lo B, 2016, An integrated wearable robot for tremor suppression with context aware sensing, 13th IEEE International Conference on Wearable and Implantable Body Sensor Networks (BSN), Publisher: IEEE, Pages: 312-317, ISSN: 2376-8886
Abstract:Tremor is a neurological disorder which can significantly impede the daily functions of patients. The available treatments for patients with tremor are mainly pharmacotherapy and neurosurgery, but these treatments often have side effects. A wearable exoskeleton can potentially provide the assistance needed for patients with Parkinsonian or essential tremor to carry out daily activities and enable independent living. This paper presents the design and development of a 3D printed lightweight tremor suppression wearable exoskeleton. One of the major technical challenges for wearable robot is to maintain long battery life meanwhile miniature in size for practical use. This paper proposes an integrated approach where context aware Body Sensor Networks (BSN) sensors are incorporated to characterize voluntary and tremor movement, and detect activities of daily life (ADL). With the contextual information, the system can determine the intention of the user, optimize its control and minimize its power consumption by providing the necessary suppression only when needed. The preliminary result has shown that the wearable robot prototype can reduce the amplitude of simulated tremor by around 77%, and accurately identify different ADL with accuracy above 70%.
Berthelot M, Chen C-M, Yang G-Z, et al., 2016, Wireless wearable self-calibrated sensor for perfusion assessment of myocutaneous tissue, 13th IEEE International Conference on Wearable and Implantable Body Sensor Networks (BSN), Publisher: IEEE, Pages: 171-176, ISSN: 2376-8886
Abstract:Blood flow and perfusion monitoring are critical appraisal to ensure survival of tissue flap after reconstructive surgery. Many techniques have been developed over the years: from optical to chemical, invasive or not, they all have limitations in their price, risks and adaptiveness to the patient. A wireless wearable self-calibrated device, based on near infrared spectroscopy (NIRS) was developed for blood flow and perfusion monitoring contingent on tissue oxygen saturation (StO2). The use of such device is particularly relevant in the case of free flap myocutaneous reconstructive surgery; postoperative monitoring of the flap is crucial for a prompt intervention in case of thrombosis. Although failure rate is low, the rate of additional surgery following anastomosis problem is about 50%. NIRS has shown promising results for the monitoring of free flap, however lack of adaptation to its environment (ambient light) and users (body mass index (BMI), skin tone, alcohol and smoking habits or physical activity level) hinders the practical use of this technique. To overcome those limitations, a self-calibrated approach is introduced. Tested with is chaemia and cold water experiments on healthy subjects of different skin tones, its ability to personalize its calibration is demonstrated. Furthermore, using a vascular phantom, it is also able to detect pulses, differentiate venous and arterial coloured-like fluids with distinct clusters and detect significant changes in simulated partial venous occlusion. Placed in the trained classifier, partial occlusion data showed similar results between predicted and true classification. Further analysis from partial occlusion data showed that distinct clusters for 75% and 100% occlusion emerged.
Ravi D, Wong C, Lo B, et al., 2016, Deep learning for human activity recognition: A resource efficient implementation on low-power devices, 2016 IEEE 13th International Conference on Wearable and Implantable Body Sensor Networks, Publisher: IEEE, Pages: 71-76, ISSN: 2376-8894
Human Activity Recognition provides valuable contextual information for wellbeing, healthcare, and sport applications. Over the past decades, many machine learning approaches have been proposed to identify activities from inertial sensor data for specific applications. Most methods, however, are designed for offline processing rather than processing on the sensor node. In this paper, a human activity recognition technique based on a deep learning methodology is designed to enable accurate and real-time classification for low-power wearable devices. To obtain invariance against changes in sensor orientation, sensor placement, and in sensor acquisition rates, we design a feature generation process that is applied to the spectral domain of the inertial data. Specifically, the proposed method uses sums of temporal convolutions of the transformed input. Accuracy of the proposed approach is evaluated against the current state-of-the-art methods using both laboratory and real world activity datasets. A systematic analysis of the feature generation parameters and a comparison of activity recognition computation times on mobile devices and sensor nodes are also presented.
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