Imperial College London

DrBennyLo

Faculty of MedicineDepartment of Surgery & Cancer

Senior Lecturer
 
 
 
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Contact

 

+44 (0)20 7594 0806benny.lo Website

 
 
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Location

 

B414BBessemer BuildingSouth Kensington Campus

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Summary

 

Publications

Publication Type
Year
to

206 results found

Chen C-M, Kwasnicki RM, Curto VF, Yang G-Z, Lo BPLet 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

Journal article

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.

Journal article

McCrory M, Sun M, Sazonov E, Frost G, Anderson A, Jia W, Jobarteh ML, Maitland K, Steiner-Asiedu M, Ghosh T, Higgins JA, Baranowski T, Lo Bet al., 2019, Methodology for objective, passive, image- and sensor-based assessment of dietary intake, meal-timing, and food-related activity in Ghana and Kenya (P13-028-19)., Current Developments in Nutrition, Vol: 3, Pages: 1247-1247, ISSN: 2475-2991

Objectives: Herein we describe a new system we have developed for assessment of dietary intake, meal timing, and food-related activities, adapted for use in low- and middle-income countries. Methods: System components include one or more wearable cameras (the Automatic Ingestion Monitor-2 (AIM), an eyeglasses-mounted wearable chewing sensor and micro-camera; ear-worn camera; the eButton, a camera attached to clothes; and eHat, a camera attached to a visor worn by the mother when feeding infants and toddlers), and custom software for evaluation of dietary intake from food-based images and sensor-detected food intake. General protocol: The primary caregiver of the family uses one or more wearable cameras during all waking hours. The cameras aim directly in front of the participant and capture images every few seconds, thereby providing multiple images of all food-related activities throughout the day. The camera may be temporarily removed for short periods to preserve privacy, such as during bathing and personal care. For analysis, images and sensor signals are processed by the study team in custom software. The images are time-stamped, arranged in chronological order, and linked with sensor-detected eating occasions. The software also incorporates food composition databases of choice such as the West African Foods Database, a Kenyan Foods Database, and the USDA Food Composition Database, allowing for image-based dietary assessment by trained nutritionists. Images can be linked with nutritional analysis and tagged with an activity label (e.g., food shopping, child feeding, cooking, eating). Assessment of food-related activities such as food-shopping, food gathering from gardens, cooking, and feeding of other family members by the primary caregiver can help provide context for dietary intake and additional information to increase accuracy of dietary assessment and analysis of eating behavior. Examples of the latter include assessment of specific ingredients in prepared

Journal article

Berthelot M, Henry FP, Hunter J, Leff D, Wood S, Jallali N, Dex E, Ladislava L, Lo B, Yang GZet al., Pervasive wearable device for free tissue transfer monitoring based on advanced data analysis: clinical study report, Journal of Biomedical Optics, ISSN: 1083-3668

Free tissue transfer (FTT) surgery for breast reconstruction following mastectomy has become a routineoperation with high success rates. Although failure is low, it can have a devastating impact on patient recovery,prognosis and psychological well-being. Continuous and objective monitoring of tissue oxygen saturation (StO2) hasshown to reduce failure rates through rapid detection time of postoperative vascular complications. We have developeda pervasive wearable wireless device that employs near infrared spectroscopy (NIRS) to continuously monitor FTTviaStO2measurement. Previously tested on different models, this paper introduces the results of a clinical study. Thegoal of the study is to demonstrate the developed device can reliably detectStO2variations in a clinical setting: 14patients were recruited. Advanced data analysis were performed on theStO2variations, the relativeStO2gradientchange, and, the classification of theStO2within different clusters of blood occlusion level (from 0% to 100% at 25%step) based on previous studies made on a vascular phantom and animals. The outcomes of the clinical study concurwith previous experimental results and the expected biological responses. This suggests the device is able to correctlydetect perfusion changes and provide real-time assessment on the viability of the FTT in a clinical setting.

Journal article

Sun Y, Lo B, 2019, An artificial neural network framework for gait based biometrics, IEEE Journal of Biomedical and Health Informatics, Vol: 23, Pages: 987-998, 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.

Journal article

Bernstein A, Varghese RJ, Liu J, Zhang Z, Lo Bet 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.

Book chapter

Singh RK, Varghese RJ, Liu J, Zhang Z, Lo Bet 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.

Book chapter

Zhang Y, Zhang Y, Lo B, Xu Wet al., 2019, Wearable ECG signal processing for automated cardiac arrhythmia classification using CFASE-based feature selection, ISSN: 0266-4720

© 2019 John Wiley & Sons, Ltd 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.

Conference paper

Ahmed MR, Zhang Y, Feng Z, Lo B, Inan OT, Liao Het 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.

Journal article

Lo FP-W, Sun Y, Qiu J, Lo Bet al., 2018, Food Volume Estimation Based on Deep Learning View Synthesis from a Single Depth Map, NUTRIENTS, Vol: 10, ISSN: 2072-6643

Journal article

Teachasrisaksakul K, Wu L, Yang G-Z, Lo Bet 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%.

Conference paper

Berthelot M, Lo B, Yang G-Z, Leff Det al., 2018, Pilot study: Free flap monitoring using a new tissue oxygen saturation (StO2) device, European Journal of Surgical Oncology, Vol: 44, Pages: 900-900, ISSN: 0748-7983

Journal article

Gu X, Deligianni F, Lo B, Chen W, Yang Get al., 2018, Markerless gait analysis based on a single RGB camera, International Conference on Wearable and Implantable Body Sensor Networks, Publisher: IEEE, ISSN: 2376-8894

Gait analysis is an important tool for monitoring and preventing injuries as well as to quantify functional decline in neurological diseases and elderly people. In most cases, it is more meaningful to monitor patients in natural living environments with low-end equipment such as cameras and wearable sensors. However, inertial sensors cannot provide enough details on angular dynamics. This paper presents a method that uses a single RGB camera to track the 2D joint coordinates with state-of-the-art vision algorithms. Reconstruction of the 3D trajectories uses sparse representation of an active shape model. Subsequently, we extract gait features and validate our results in comparison with a state-of-the-art commercial multi-camera tracking system. Our results are comparable to those from the current literature based on depth cameras and optical markers to extract gait characteristics.

Conference paper

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.

Conference paper

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.

Conference paper

Lo BPL, Innovative Sensing Technologies for Developing Countries, IEEE Biomedical and Health Informatics BHI 2018

Conference paper

Friedl KE, Hixson JD, Buller MJ, Lo Bet al., 2018, Guest editorial - 13th Body Sensor Networks Symposium, IEEE Journal of Biomedical and Health Informatics, Vol: 22, Pages: 3-4, ISSN: 2168-2194

Journal article

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.

Conference paper

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.

Conference paper

Lo BPL, Guo Y, Zhang Y, Mursalin M, Xu Wet 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.

Conference paper

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).

Journal article

Deligianni F, Wong CW, Lo B, Yang Get 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.

Journal article

Sun Y, Wong C, Yang GZ, Lo Bet 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.

Conference paper

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.

Conference paper

Deligianni F, Ravi D, Roots S, Rosa B, Sun Y, Wong C, Yang G-Z, Lo Bet 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.

Conference paper

Zhang R, Ravi D, Yang G-Z, Lo BENNYet 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.

Conference paper

Ravi D, Wong C, Lo B, Yang Get 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.

Journal article

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.

Conference paper

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.

Conference paper

Ravi D, Wong C, Deligianni F, Berthelot M, Andreu-Perez J, Lo B, Yang Get al., Deep learning for health informatics, IEEE Journal of Biomedical and Health Informatics, Vol: 21, Pages: 4-21, ISSN: 2168-2208

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.

Journal article

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