221 results found
Atallah L, Lo B, Ali R, et al., 2009, Real-Time Activity Classification Using Ambient and Wearable Sensors, IEEE TRANSACTIONS ON INFORMATION TECHNOLOGY IN BIOMEDICINE, Vol: 13, Pages: 1031-1039, ISSN: 1089-7771
King RC, Atallah L, Lo BPL, et al., 2009, Development of a Wireless Sensor Glove for Surgical Skills Assessment, IEEE TRANSACTIONS ON INFORMATION TECHNOLOGY IN BIOMEDICINE, Vol: 13, Pages: 673-679, ISSN: 1089-7771
Ali R, Atallah L, Lo B, et al., 2009, Transitional Activity Recognition with Manifold Embedding, 6th International Workshop on Wearable and Implantable Body Sensor Networks, Publisher: IEEE COMPUTER SOC, Pages: 98-+
Lo B, Pansiot J, Yang G-Z, 2009, Bayesian Analysis of Sub-Plantar Ground Reaction Force with BSN, 6th International Workshop on Wearable and Implantable Body Sensor Networks, Publisher: IEEE COMPUTER SOC, Pages: 133-137
Atallah L, Lo B, Yang G-Z, et al., 2009, Detecting Walking Gait Impairment with an Ear-worn Sensor, 6th International Workshop on Wearable and Implantable Body Sensor Networks, Publisher: IEEE COMPUTER SOC, Pages: 175-+
King RC, McIlwraith DG, Lo B, et al., 2009, Body Sensor Networks for Monitoring Rowing Technique, 6th International Workshop on Wearable and Implantable Body Sensor Networks, Publisher: IEEE COMPUTER SOC, Pages: 251-+
Patterson JAC, McIlwraith DG, Yang G-Z, 2009, A Flexible, Low Noise Reflective PPG Sensor Platform for Ear-Worn Heart Rate Monitoring, 6th International Workshop on Wearable and Implantable Body Sensor Networks, Publisher: IEEE COMPUTER SOC, Pages: 286-291
Valibeik S, Ballantyne J, Lo B, et al., 2009, Establishing Affective Human Robot Interaction through Contextual Information, 18th IEEE International Symposium on Robot and Human Interactive Communication, Publisher: IEEE, Pages: 1209-1214
Omre AH, 2009, Reducing Healthcare Costs with Wireless Technology, 6th International Workshop on Wearable and Implantable Body Sensor Networks, Publisher: IEEE COMPUTER SOC, Pages: 65-70
Aziz O, Lo B, Pansiot J, et al., 2008, From computers to ubiquitous computing by 2010: health care, PHILOSOPHICAL TRANSACTIONS OF THE ROYAL SOCIETY A-MATHEMATICAL PHYSICAL AND ENGINEERING SCIENCES, Vol: 366, Pages: 3805-3811, ISSN: 1364-503X
Atallah L, Lo B, Yang GZ, et al., 2008, Wirelessly accessible sensor populations (WASP) for elderly care monitoring, Pages: 2-7
This paper presents an application of a service-based architecture to pervasive monitoring of the elderly using ambient and wearable sensors. The design consideration of the model addresses heterogeneous computing and network resource utilization, allowing inter-operability and supporting dynamic environments to achieve system wide resource optimization. An application of this architecture is presented for assessing the activities of daily living, which is the basis for pervasive sensing for elderly care.
Andersen J, Lo B, Yang GZ, 2008, Experimental platform for usability testing of secure medical sensor network protocols, Pages: 179-182
Implementing security mechanisms such as access control for clinical use is a challenging research issue in BSN due to its required heterogeneous operating responses ranging from chronic diseases management to emergency care. To ensure the clinical uptake of the BSN technology, appropriately designed security mechanisms are essential. Several experimental sensor network platforms have emerged in recent years targeted for clinical use. However, few of them consider the importance of security issues such as privacy and access control, and how these can impact the usability of the platform, while others develop BSN security without considering how a prototype implementation would be received by clinicians in real-life situations. The purpose of this paper is to present our initial effort in building a flexible experimental platform for providing a basic infrastructure with symmetric AES encryption of sensor and configuration data with suitable user interfaces. The pluggable module provides the protocol for authentication and key generation such that modules with different security properties and respective user interface consequences can be easily compared and evaluated. ©2008 IEEE.
McIlwraith DG, Pansiot J, Thiemjarus S, et al., 2008, Probabilistic decision level fusion for real-time correlation of ambient and wearable sensors, Pages: 117-120
Fusing data from ambient and wearable sensors when performing in-home healthcare monitoring allows for high accuracy activity inference due to the complementary nature of sensing modalities. Where residences may house multiple occupants, we must automatically identify related data streams before fusion may occur, a process known as sensor correlation. In this paper a multi-objective variant of the Bayesian Framework for Feature Selection (BFFS) is used to construct small inter-sensor redundant feature sets which train efficient per-sensor activity classifiers. Probabilistic decision level fusion is then used to deal with noisy and erroneous sensor data and perform real-time correlation. The potential value of the proposed algorithm for pervasive sensing is demonstrated with both simulated and experimental data. ©2008 IEEE.
Atallah L, Elsaify A, Lo B, et al., 2008, Gaussian process prediction for cross channel consensus in body sensor networks, Proc. 5th Int. Workshop on Wearable and Implantable Body Sensor Networks, BSN2008, in conjunction with the 5th Int. Summer School and Symp. on Medical Devices and Biosensors, ISSS-MDBS 2008, Pages: 165-168
This paper presents a framework based on Gaussian Processes for assessing cross channel consensus in Body Sensor Network (BSN) data. Cross channel consensus can be observed by measuring the prediction error of one channel given the others, which could help in predicting missing data, correcting for noisy channels, or learning relationships between sensor channels over time. The method is evaluated with activities of daily living experiments with sensing data including heart rate, respiration and activity levels. The acquired prediction rates indicate the potential practical value of the technique for home-monitoring of chronically ill patients. ©2008 IEEE.
Wang L, Thiemjarus S, Lo B, et al., 2008, Toward A mixed-signal reconfigurable ASIC for real-time activity recognition, Pages: 227-230
In recent years, there have been increasing interests in context aware sensing based upon ultra-low power wearable sensors. These applications require efficient processing-on-node capabilities to minimise the overall power consumption and wireless transmission bandwidths. In this paper, a novel reconfigurable mixed-signal ASIC designed for real-time activity recognition has been proposed. The system architecture integrates all signal conditioning and data processing circuits onto a single silicon substrate with configurable analogue computing and artificial neuron network-inspired classification blocks. The ASIC is designed using conventional EDA tools and has been fabricated using AMS 0.35μ m CMOS technology with a final chip size of 23.8 mm2. An on-chip inferencing engine derived from off-chip training data has been developed. Both design considerations and implementation details of the ASIC are discussed. Preliminary simulation results indicate the desired performance of the ASIC for real-time activity classification. ©2008 IEEE.
The recent growth in popularity in sport climbing is partly due to the safe environment provided by indoor climbing walls, particularly for novice climbers. Sport climbing involves a wide range of skills and abilities. The purpose of this paper is to present a wearable sensing platform and an analysis framework for assessing general climbing performance during training. To provide the required freedom of movement, a single miniaturized ear-worn 3D accelerometer-based sensor is used. Independent features derived from the accelerometer data are then translated into climbing-specific measures, such as motion fluidity, strength, as well as endurance. Based on these indices, the overall level of the climber and the associated climbing styles can be quantified. ©2008 IEEE.
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.
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