205 results found
Ali R, ElHelw M, Atallah L, et al., 2008, Pattern mining for routine behaviour discovery in pervasive healthcare environments, Pages: 241-244
Pervasive sensing is set to transform the future of patient care by continuous and intelligent monitoring of patient well-being. In practice, the detection of patient activity patterns over different time resolutions can be a complicated procedure, entailing the utilisation of multi-tier software architectures and processing of large volumes of data. This paper describes a scalable, distributed software architecture that is suitable for managing continuous activity data streams generated from body sensor networks. A novel pattern mining algorithm is applied to pervasive sensing data to obtain a concise, variable-resolution representation of frequent activity patterns over time. The identification of such frequent patterns enables the observation of the inherent structure present in a patient's daily activity for analyzing routine behaviour and its deviations. © 2008 IEEE.
Thiemjarus S, Pansiot J, Mcllwraith D, et al., 2008, An integrated inferencing framework for context sensing, Pages: 270-274
This paper presents the use of distributed inferencing with resource optimisation and Spatio-Temporal Self-Organising Map (STSOM) for effectively combining the wearable and ambient sensors. STSOM is an efficient local processing technique which is also suitable for enhancing the temporal behaviour of the distributed inferencing model. To reduce the complexity of the distributed model, a multiobjective Bayesian framework for feature selection has been proposed for model learning. The validation of the techniques has been conducted with activity recognition with both wearable and ambient sensors in a lab-based home monitoring setting. © 2008 IEEE.
Lo B, Chung AJ, Stoyanov D, et al., 2008, Real-time intra-operative 3D tissue deformation recovery, Pages: 1387 -1390-1387 -1390
Wang L, Thiemjarus S, Lo B, et al., 2008, Toward A Mixed-Signal Reconfigurable ASIC for Real-Time Activity Recognition, 5th International Summer School and Symposium on Medical Devices and Biosensors, Publisher: IEEE, Pages: 113-+
McIlwraith DG, Pansiot J, Thiemjarus S, et al., 2008, Probabilistic Decision Level Fusion for Real-Time Correlation of Ambient and Wearable Sensors, 5th International Summer School and Symposium on Medical Devices and Biosensors, Publisher: IEEE, Pages: 256-259
Atallah L, Lo B, Yang G-Z, et al., 2008, Wirelessly Accessible Sensor Populations (WASP) for Elderly Care Monitoring, 2nd International Conference on Pervasive Computing Technologies for Healthcare, Publisher: IEEE, Pages: 3-+
Thiemjarus S, Pansiot J, Mcllwraith D, et al., 2008, An integrated inferencing framework for context sensing, 5th Int Conference on Information Technol and Applications in Biomedicine in Conjunction with the 2nd Int Symposium and Summer School on Biomedical and Health Engineering, Publisher: IEEE, Pages: 1-5
Lo B, Scarzanella MV, Stoyanov D, et al., 2008, Belief Propagation for Depth Cue Fusion in Minimally Invasive Surgery, 11th International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI2008), Publisher: SPRINGER-VERLAG BERLIN, Pages: 104-112, ISSN: 0302-9743
Ali R, ElHelw M, Atallah L, et al., 2008, Pattern mining for routine behaviour discovery in pervasive healthcare environments, 5th Int Conference on Information Technol and Applications in Biomedicine in Conjunction with the 2nd Int Symposium and Summer School on Biomedical and Health Engineering, Publisher: IEEE, Pages: 216-219
Atallah L, Elsaify A, Lo B, et al., 2008, Gaussian Process Prediction for Cross Channel Consensus in Body Sensor Networks, 5th International Summer School and Symposium on Medical Devices and Biosensors, Publisher: IEEE, Pages: 105-+
Pansiot J, King RC, McIlwraith DG, et al., 2008, ClimBSN: Climber Performance Monitoring with BSN, 5th International Summer School and Symposium on Medical Devices and Biosensors, Publisher: IEEE, Pages: 109-+
Andersen J, Lo B, Yang G-Z, 2008, - Experimental Platform for Usability Testing of Secure Medical Sensor Network Protocols, 5th International Summer School and Symposium on Medical Devices and Biosensors, Publisher: IEEE, Pages: 215-+
Lo B, Scarzanella MV, Stoyanov D, et al., 2008, Belief propagation for depth cue fusion in minimally invasive surgery., Med Image Comput Comput Assist Interv, Vol: 11, Pages: 104-112
In minimally invasive surgery, dense 3D surface reconstruction is important for surgical navigation and integrating pre- and intra-operative data. Despite recent developments in 3D tissue deformation techniques, their general applicability is limited by specific constraints and underlying assumptions. The need for accurate and robust tissue deformation recovery has motivated research into fusing multiple visual cues for depth recovery. In this paper, a Markov Random Field (MRF) based Bayesian belief propagation framework has been proposed for the fusion of different depth cues. By using the underlying MRF structure to ensure spatial continuity in an image, the proposed method offers the possibility of inferring surface depth by fusing the posterior node probabilities in a node's Markov blanket together with the monocular and stereo depth maps. Detailed phantom validation and in vivo results are provided to demonstrate the accuracy, robustness, and practical value of the technique.
Lo B, Yang GZ, 2007, Body sensor networks - research challenges and opportunities, Pages: 26-32
Recent advances in bionics, wireless network and computer technologies have enabled the realisation of miniaturised wireless biosensors for pervasive monitoring. Based on these technologies, the concept of Body Sensor Network (BSN) has been proposed to improve patient care, chronic disease management, and promote lifelong health and wellbeing for the ageing population. In order to provide a truly pervasive monitoring and sensing environment, a number of research issues have to be addressed. These include biosensor design, biocompatibility, wireless communication, power management, and autonomic sensing. The purpose of this paper is to provide an overview of the current BSN development and outline some of the research challenges and opportunities that it brings.
Wang L, Lo BPL, Yang G-Z, 2007, Multichannel Reflective PPG Earpiece Sensor With Passive Motion Cancellation, IEEE TRANSACTIONS ON BIOMEDICAL CIRCUITS AND SYSTEMS, Vol: 1, Pages: 235-241, ISSN: 1932-4545
James A, Vieira D, Lo B, et al., 2007, Eye-gaze driven surgical workflow segmentation, Pages: 110-117, ISSN: 0302-9743
In today's climate of clinical governance there is growing pressure on surgeons to demonstrate their competence, improve standards and reduce surgical errors. This paper presents a study on developing a novel eye-gaze driven technique for surgical assessment and workflow recovery. The proposed technique investigates the use of a Parallel Layer Perceptor (PLP) to automate the recognition of a key surgical step in a porcine laparoscopic cholecystectomy model. The classifier is eye-gaze contingent but combined with image based visual feature detection for improved system performance. Experimental results show that by fusing image instrument likelihood measures, an overall classification accuracy of 75% is achieved. © Springer-Verlag Berlin Heidelberg 2007.
Mountney P, Lo B, Thiemjarus S, et al., 2007, A probabilistic framework for tracking deformable soft tissue in minimally invasive surgery, Pages: 34-41, ISSN: 0302-9743
The use of vision based algorithms in minimally invasive surgery has attracted significant attention in recent years due to its potential in providing in situ 3D tissue deformation recovery for intra-operative surgical guidance and robotic navigation. Thus far, a large number of feature descriptors have been proposed in computer vision but direct application of these techniques to minimally invasive surgery has shown significant problems due to free-form tissue deformation and varying visual appearances of surgical scenes. This paper evaluates the current state-of-the-art feature descriptors in computer vision and outlines their respective performance issues when used for deformation tracking. A novel probabilistic framework for selecting the most discriminative descriptors is presented and a Bayesian fusion method is used to boost the accuracy and temporal persistency of soft-tissue deformation tracking. The performance of the proposed method is evaluated with both simulated data with known ground truth, as well as in vivo video sequences recorded from robotic assisted MIS procedures. © Springer-Verlag Berlin Heidelberg 2007.
Lo B, Yang GZ, 2007, Pervasive Sensing
Aziz O, Atallah L, Lo B, et al., 2007, A Pervasive Body Sensor Network for Measuring Postoperative Recovery at Home, Surgical Innovation, Vol: 14, Pages: 83-90
Patients going home following major surgery are susceptible to complications such as wound infection, abscess formation, malnutrition, poor analgesia, and depression, all of which can develop after the fifth postoperative day and slow recovery. Although current hospital recovery monitoring systems are effective during perioperative and early postoperative periods, they cannot be used when the patient is at home. Measuring and quantifying home recovery is currently a subjective and labor-intensive process. This case report highlights the development and piloting of a wireless body sensor network to monitor postoperative recovery at home in patients undergoing abdominal surgery. The device consists of wearable sensors (vital signs, motion) combined with miniaturized computers wirelessly linked to each other, thus allowing continuous monitoring of patients in a pervasive (unobtrusive) manner in any environment. Initial pilot work with results in both the simulated (with volunteers) and the real home environment (with patients) is presented.
Yang GZ, Wang L, Lo B, 2007, PPG sensor
Mountney P, Lo B, Thiernjarus S, et al., 2007, A Probabilistic framework for tracking deformable soft tissue in minimally invasive surgery, 10th International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI 2007), Publisher: SPRINGER-VERLAG BERLIN, Pages: 34-+, ISSN: 0302-9743
Lo B, Atallah L, Aziz O, et al., 2007, Real-time pervasive monitoring for postoperative care, 4th International Workshop on Wearable and Implantable Body Sensor Networks (BSN 2007), Publisher: SPRINGER, Pages: 122-+, ISSN: 1680-0737
Atallah L, ElHelw M, Pansiot J, et al., 2007, Behaviour profiling with ambient and wearable sensing, 4th International Workshop on Wearable and Implantable Body Sensor Networks (BSN 2007), Publisher: SPRINGER, Pages: 133-+, ISSN: 1680-0737
Katsiri E, Ho M, Wang L, et al., 2007, Embedded real-time heart variability analysis, 4th International Workshop on Wearable and Implantable Body Sensor Networks (BSN 2007), Publisher: SPRINGER, Pages: 128-+, ISSN: 1680-0737
Wang L, Lo B, Yang GZ, 2007, Reflective photoplethysmograph earpiece sensor for ubiquitous heart rate monitoring, 4th International Workshop on Wearable and Implantable Body Sensor Networks (BSN 2007), Publisher: SPRINGER, Pages: 179-+, ISSN: 1680-0737
James A, Vieira D, Lo B, et al., 2007, Eye-gaze driven surgical workflow segmentation, MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION- MICCAI 2007, PT 2, PROCEEDINGS, Vol: 4792, Pages: 110-+, ISSN: 0302-9743
Pansiot J, Stoyanov D, McIlwraith D, et al., 2007, Ambient and wearable sensor fusion for activity recognition in healthcare monitoring systems, 4th International Workshop on Wearable and Implantable Body Sensor Networks (BSN 2007), Publisher: SPRINGER, Pages: 208-+, ISSN: 1680-0737
Buchanan R, Doordan D, Margolin V, 2006, Introduction ('Design Issues'), DESIGN ISSUES, Vol: 22, Pages: 1-2, ISSN: 0747-9360
Guang-Zhong Yang, Surapa Thiemjarus, 2006, Spatial-Temporal Self Organising Map, WO 2006/097734
Aziz O, Lo B, King R, et al., 2006, Pervasive body sensor network: an approach to monitoring the post-operative surgical patient, Los Alamitos, International workshop on wearable and implantable body sensor networks, Publisher: IEEE Computer Soc, Pages: 13-16
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