Publications
292 results found
Chen C-M, Onyenso K, Yang G-Z, et al., 2015, A Multi-Sensor Platform for Monitoring Diabetic Peripheral Neuropathy, IEEE 12th International Conference on Wearable and Implantable Body Sensor Networks (BSN), Publisher: IEEE
Kirby GSJ, Kwasnicki RM, Hargrove C, et al., 2014, Wireless body sensor for objective assessment of surgical performance on a standardised FLS task, Pages: 147-153
Advances in Body Sensor Networks have prompted increasing numbers of low cost, miniaturised sensors being used in many different applications with one being the capture of hand movement data for surgical skills assessment. Despite these advances, existing assessment techniques are still predominantly subjective and resource demanding. Combining surgical training with a reliable objective assessment technique would ensure that trainees are correctly evaluated and credentialed as they progress through their training hence, ensuring competence and reducing critical medical errors. This paper proposes the use of wearable, wireless inertial sensors for capturing motion data and enabling objective assessment of trainee surgeons' performance in carrying out one of the FLS (Fundamentals of Laparoscopic surgery) tasks; the peg transfer. A novel approach has been developed for the segmenting of specific peg movements enabling performance to be measured entirely objectively. The features derived from the whole task as well as features for each of the segmented movements were analysed through unsupervised machine learning algorithms to look for useful measures of performance as well as patterns to identify differences between expert and trainee performance. Encouraging results in the peg transfer task, where a successful classification of expertise was obtained for all participants against gold standard assessment, prompt further investigation into the development of advanced performance metrics for a wider range of surgical training tasks.
Wong C, Zhang Z, Lo B, et al., 2014, Markerless motion capture using appearance and inertial data, Pages: 6907-6910
Atallah L, Wiik A, Lo B, et al., 2014, Gait asymmetry detection in older adults using a light ear-worn sensor, PHYSIOLOGICAL MEASUREMENT, Vol: 35, Pages: N29-N40, ISSN: 0967-3334
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- Citations: 15
Zheng Y-L, Ding X-R, Poon CCY, et al., 2014, Unobtrusive sensing and wearable devices for health informatics, IEEE Transactions on Biomedical Engineering, Vol: 61, Pages: 1538-1554, ISSN: 0018-9294
The aging population, prevalence of chronic diseases, and outbreaks of infectious diseases are some of the major challenges of our present-day society. To address these unmet healthcare needs, especially for the early prediction and treatment of major diseases, health informatics, which deals with the acquisition, transmission, processing, storage, retrieval, and use of health information, has emerged as an active area of interdisciplinary research. In particular, acquisition of health-related information by unobtrusive sensing and wearable technologies is considered as a cornerstone in health informatics. Sensors can be weaved or integrated into clothing, accessories, and the living environment, such that health information can be acquired seamlessly and pervasively in daily living. Sensors can even be designed as stick-on electronic tattoos or directly printed onto human skin to enable long-term health monitoring. This paper aims to provide an overview of four emerging unobtrusive and wearable technologies, which are essential to the realization of pervasive health information acquisition, including: 1) unobtrusive sensing methods, 2) smart textile technology, 3) flexible-stretchable-printable electronics, and 4) sensor fusion, and then to identify some future directions of research.
Jarchi D, Lo B, Ieong E, et al., 2014, Validation of the e-AR sensor for gait event detection using the Parotec foot insole with application to post-operative recovery monitoring, 11th International Conference on Wearable and Implantable Body Sensor Networks, Publisher: IEEE, Pages: 127-131
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- Citations: 7
Chen CM, Kwasnicki R, Lo B, et al., 2014, Wearable Tissue Oxygenation Monitoring Sensor and a Forearm Vascular Phantom Design for Data Validation, 11th International Conference on Wearable and Implantable Body Sensor Networks, Publisher: IEEE, Pages: 64-68
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- Citations: 14
Li L, Atallah L, Lo B, et al., 2014, Feature Extraction from Ear-Worn Sensor Data for Gait Analysis, IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI), Publisher: IEEE, Pages: 560-563
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- Citations: 2
Yu R, Yang G-Z, Lo BPL, 2014, Autonomic Body Sensor Networks, IEEE MTT-S International Microwave Workshop Series on: RF and Wireless Technologies for Biomedical and Healthcare Applications (IMWS-Bio 2014), Publisher: IEEE
Lo B, Panousopoulou A, Thiemjarus S, et al., 2014, Autonomic Sensing, Body Sensor Networks, Publisher: Springer London, Pages: 405-462, ISBN: 9781447163732
Atallah L, Aziz O, Gray E, et al., 2013, An Ear-Worn Sensor for the Detection of Gait Impairment After Abdominal Surgery, SURGICAL INNOVATION, Vol: 20, Pages: 86-94, ISSN: 1553-3506
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- Citations: 9
Atallah L, Aziz O, Gray E, et al., 2013, An Ear-Worn Sensor for the Detection of Gait Impairment After Abdominal Surgery, SURGICAL INNOVATION, Vol: 20, Pages: 86-94, ISSN: 1553-3506
Lo B, Thiemjarus S, Panousopoulou A, et al., 2013, Bio-inspired Design for Body Sensor Networks, IEEE Signal Processing Magazine (to appear)
Ali R, Lo B, Yang G-Z, 2013, Unsupervised routine profiling in free-living conditions - can smartphone apps provide insights?, IEEE International Conference on Body Sensor Networks, Publisher: IEEE
Wieboldt J, Atallah L, Kelly JL, et al., 2012, Effect of acute exacerbations on skeletal muscle strength and physical activity in cystic fibrosis, JOURNAL OF CYSTIC FIBROSIS, Vol: 11, Pages: 209-215, ISSN: 1569-1993
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- Citations: 23
Atallah L, Wiik A, Jones GG, et al., 2012, Validation of an ear-worn sensor for gait monitoring using a force-plate instrumented treadmill, GAIT & POSTURE, Vol: 35, Pages: 674-676, ISSN: 0966-6362
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- Citations: 37
Atallah L, Lo B, Yang G-Z, 2012, Can pervasive sensing address current challenges in global healthcare?, Journal of Epidemiology and Global Health, Vol: 2, Pages: 1-13, ISSN: 2210-6006
Important challenges facing global healthcare include the increase in the number of people affected by escalating healthcare costs, chronic and infectious diseases, the need for better and more affordable elderly care and expanding urbanisation combined with air and water pollution. Recent advances in pervasive sensing technologies have led to miniaturised sensor networks that can be worn or integrated within the living environment without affecting a person's daily patterns. These sensors promise to change healthcare from snapshot measurements of physiological parameters to continuous monitoring enabling clinicians to provide guidance on a daily basis. This article surveys several of the solutions provided by these sensor platforms from elderly care to neonatal monitoring and environmental mapping. Some of the opportunities available and the challenges facing the adoption of such technologies in large-scale epidemiological studies are also discussed.
Atallah L, Mcllwraith D, Thiemjarus S, et al., 2012, Distributed inferencing with ambient and wearable sensors, WIRELESS COMMUNICATIONS & MOBILE COMPUTING, Vol: 12, Pages: 117-131, ISSN: 1530-8669
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- Citations: 1
Ali R, Atallah L, Lo B, et al., 2012, Detection and Analysis of Transitional Activity in Manifold Space, IEEE TRANSACTIONS ON INFORMATION TECHNOLOGY IN BIOMEDICINE, Vol: 16, Pages: 119-128, ISSN: 1089-7771
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- Citations: 13
Kwasnicki RM, Low DA, Wong C, et al., 2012, Investigating the feasibility of using objective motion data to assist the diagnosis and management of cardiovascular autonomic dysfunction, Pages: 137-137
Liu J, Johns E, Atallah L, et al., 2012, An intelligent food-intake monitoring system using wearable sensors, Pages: 154-160
Lo B, Atallah L, Crewther B, et al., 2011, Pervasive sensing for athletic training, Delivering London 2012: ICT Enabling the Games, Pages: 53-62
Pansiot J, Zhang Z, Lo B, et al., 2011, WISDOM: wheelchair inertial sensors for displacement and orientation monitoring, MEASUREMENT SCIENCE AND TECHNOLOGY, Vol: 22, ISSN: 0957-0233
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- Citations: 24
Zhang ZQ, Pansiot J, Lo B, et al., 2011, Human back movement analysis using BSN, Pages: 13-18
Human back movement estimation is clinically important for assessing patients with back pain. Most current techniques are limited to simple spinal movement angles without consideration of surrounding muscle movement and backplane rotation and torsion. These three dimensional analysis is fraught with difficulties due to the complex nature of the movement and sensor placement. In this paper, a consistent method based on multiple Body Sensor Network (BSN) nodes for the measurement of 3D bending and twist of the back is proposed. In our method, five BSN nodes, each consisting of a three axis accelerometer, a gyroscope and a magnetometer, are placed at the human back. Euler angles are then defined to represent the orientation for human back segments, kinematics analysis is then derived. An unscented Kalman filter (UKF) is deployed to estimate the defined Euler angles. Detailed experimental results have shown the feasibility and effectiveness of the proposed measurement and analysis framework. © 2011 IEEE.
Atallah L, Jones GG, Ali R, et al., 2011, Observing recovery from knee-replacement surgery by using wearable sensors, Pages: 29-34
A progressive improvement in gait following knee arthroplasty surgery can be observed during walking and transitional activities such as sitting/standing. Accurate assessment of such changes traditionally requires the use of a gait lab, which is often impractical, expensive, and labour intensive. Quantifying gait impairment following knee arthroplasty by employing wearable sensors allows for continuous monitoring of recovery. This study employed a recognised protocol of activities both pre-operatively, and at regular intervals up to twenty-four weeks post-total knee arthroplasty. The results suggest that a wearable miniaturised ear-worn sensor is potentially useful in monitoring post-operative recovery, and in identifying patients who fail to improve as expected, thus facilitating early clinical review and intervention. © 2011 IEEE.
Atallah L, Lo B, King R, et al., 2011, Sensor Positioning for Activity Recognition Using Wearable Accelerometers, IEEE TRANSACTIONS ON BIOMEDICAL CIRCUITS AND SYSTEMS, Vol: 5, Pages: 320-329, ISSN: 1932-4545
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- Citations: 219
Atallah L, Leong JJH, Lo B, et al., 2011, Energy Expenditure Prediction Using a Miniaturized Ear-Worn Sensor, MEDICINE & SCIENCE IN SPORTS & EXERCISE, Vol: 43, Pages: 1369-1377, ISSN: 0195-9131
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- Citations: 19
Aziz O, Atallah L, Lo B, et al., 2011, Ear-worn body sensor network device: an objective tool for functional postoperative home recovery monitoring., J Am Med Inform Assoc, Vol: 18, Pages: 156-159
Patients' functional recovery at home following surgery may be evaluated by monitoring their activities of daily living. Existing tools for assessing these activities are labor-intensive to administer and rely heavily on recall. This study describes the use of a wireless ear-worn activity recognition sensor to monitor postoperative activity levels continuously using a Bayesian activity classification framework. The device was used to monitor the postoperative recovery of five patients following abdominal surgery. Activity was classified into four groups ranging from very low (level 0) to high (level 3). Overall, patients were found to be undertaking a higher proportion of level 0 activities on postoperative day 1 which was gradually replaced by higher-level activities over the next 3 days. This study demonstrates how a pervasive healthcare technology can objectively monitor functional recovery in the unsupervised home setting. This may be a useful adjunct to existing postoperative monitoring systems.
Tolkiehn M, Atallah L, Lo B, et al., 2011, Direction sensitive fall detection using a triaxial accelerometer and a barometric pressure sensor, Publisher: IEEE, Pages: 369-372
Falling is one of the leading causes of serious health decline or injury-related deaths in the elderly. For survivors of a fall, the resulting health expenses can be a devastating burden, largely because of the long recovery time and potential comorbidities that ensue. The detection of a fall is, therefore, important in care of the elderly for decreasing the reaction time by the care-givers especially for those in care who are particularly frail or living alone. Recent advances in motion-sensor technology have enabled wearable sensors to be used efficiently for pervasive care of the elderly. In addition to fall detection, it is also important to determine the direction of a fall, which could help in the location of joint weakness or post-fall fracture. This work uses a waist-worn sensor, encompassing a 3D accelerometer and a barometric pressure sensor, for reliable fall detection and the determination of the direction of a fall. Also assessed is an efficient analysis framework suitable for on-node implementation using a low-power micro-controller that involves both feature extraction and fall detection. A detailed laboratory analysis is presented validating the practical application of the system.
Ellul J, Lo B, Yang G-Z, 2011, The BSNOS Platform: A Body Sensor Networks Targeted Operating System and Toolset, 5th International Conference on Sensor Technologies and Applications (SENSORCOMM) / 1st International Workshop on Sensor Networks for Supply Chain Management (WSNSCM), Publisher: IARIA XPS PRESS, Pages: 381-386
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- Citations: 3
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