Publications
292 results found
Bennebroek M, Barroso A, Atallah L, et al., 2010, Deployment of wireless sensors for remote elderly monitoring
The FP6 project "Wireless Accessible Sensor Populations" (WASP) has developed an end-to-end infrastructure for the deployment and enterprise integration of wireless sensor nodes. The infrastructure is generic and allows for optimisation for a variety of applications by the development of dedicated services that can be distributed over (wearable and ambient) sensor nodes, the WSN gateway, and the enterprise (backend) system. Key to many applications, such as elderly care considered in this paper, is to optimise the battery lifetime of wearable sensor nodes that can be (remotely) customized to the monitoring needs of individual persons and to the quality-of-service demands for offered services. The WASP infrastructure provides practical solutions for these targets and is being validated for realistic elderly care scenarios. These scenario's aim to support the elderly in (semi-) independent Ambient Assisted Living settings as well as to provide health workers with effective means of studying transient deterioration and behavior changes characteristic to the ageing population. © 2010 IEEE.
Atallah L, Lo B, King R, et al., 2010, Sensor placement for activity detection using wearable accelerometers, Pages: 24-29
Activities of daily living are important for assessing changes in physical and behavioural profiles of the general population over time, particularly for the elderly and patients with chronic diseases. Although accelerometers are widely integrated with wearable sensors for activity classification, the positioning of the sensors and the selection of relevant features for different activity groups still pose interesting research challenges. This paper investigates wearable sensor placement at different body positions and aims to provide a framework that can answer the following questions: (i) What is the ideal sensor location for a given group of activities? (ii) Of the different time-frequency features that can be extracted from wearable accelerometers, which ones are most relevant for discriminating different activity types? © 2010 IEEE.
Pansiot J, Lo B, Yang GZ, 2010, Swimming stroke kinematic analysis with BSN, Pages: 153-158
The recent maturity of body sensor networks has enabled a wide range of applications in sports, well-being and healthcare [1]. In this paper, we hypothesise that a single unobtrusive head-worn inertial sensor can be used to infer certain biomotion details of specific swimming techniques. The sensor, weighing only seven grams is mounted on the swimmer's goggles, limiting the disturbance to a minimum. Features extracted from the recorded acceleration such as the pitch and roll angles allow to recognise the type of stroke, as well as basic biomotion indices. The system proposed represents a non-intrusive, practical deployment of wearable sensors for swimming performance monitoring. © 2010 IEEE.
Atallah L, Zhang J, Lo BPL, et al., 2010, Validation Of An Ear Worn Sensor For Activity Monitoring In COPD, AMERICAN JOURNAL OF RESPIRATORY AND CRITICAL CARE MEDICINE, Vol: 181, ISSN: 1073-449X
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- Citations: 5
Valibeik S, Ballantyne J, Lo B, et al., 2009, Establishing affective human robot interaction through contextual information, Pages: 867-872
Determining human intention is a challenging task for establishing affective human robot interaction. The aim of this paper is to provide a vision based framework to achieve a level of understanding about people in an environment before engaging in active communication or interaction. The proposed method combines multiple cues in a Bayesian framework to identify people in the scene and determine potential intentions. To improve the system performance, contextual feedback is used, which allows the Bayesian network to evolve and adjust itself according to the surrounding environment. Our results demonstrate the effectiveness of the technique in dealing with human-robot interaction in a relatively crowded environment. © 2009 IEEE.
Pansiot J, Elsaify A, Lo B, et al., 2009, RACKET: Real-time autonomous computation of kinematic elements in tennis, Pages: 773-779
This paper proposes the use of a Visual Sensor Network (VSN) for tracking the motion of tennis players on court in real-time. The proposed autonomous and wireless VSN nodes are miniaturised and powered by battery, making them ideally suited for monitoring training sessions and matches at any location. With the proposed framework, the player is tracked in the image plane using a statistical background model and efficient on-node processing. To improve the usability of the system, the normal markings on the tennis court are used as a calibration grid and the calibration algorithm is implemented with on-node processing. The node further incorporates an HTTP server to simplify transmission and interrogation of the on-node processing results by using mobile devices. The proposed system is capable of tracking a tennis player at 10 to 15 frames per seconds. Multiple nodes are deployed simultaneously either to track several players or to enhance the tracking accuracy of a single player. Features related to motion and game tactics are used to guide the training sessions and refine player tactics. ©2009 Crown.
Lo B, Pansiot J, Yang GZ, 2009, Bayesian analysis of sub-plantar ground reaction force with BSN, Pages: 133-137
The assessment of Ground Reaction Forces (GRF) is important for gait analysis for sports, pathological gaits and rehabilitation. To capture GRF, force plates and foot pressure insoles are commonly used. Due to cost and portability issues, such systems are mostly limited to lab-based studies. Longterm, continuous and pervasive measurement of GRF is not feasible. This paper presents a novel concept of using an earworn sensor for pervasive gait analysis. By emulating the human vestibular system, the bio-inspired design sensor effectively captures the shock wave generated by the GRF. A hierarchical Bayesian network is developed to estimate the plantar force distribution from the ear sensor signals. The accuracy of the ear sensor for detecting GRF is demonstrated by comparing the results with a high-accuracy commercial foot pressure insole system. © 2009 IEEE.
ElHelw M, Pansiot J, McIlwraith D, et al., 2009, An integrated multi-sensing framework for pervasive healthcare monitoring
Pervasive healthcare provides an effective solution for monitoring the wellbeing of elderly, quantifying post-operative patient recovery and monitoring the progression of neurodegenerative diseases such as Parkinson's. However, developing functional pervasive systems is a complex task that entails the creation of appropriate sensing platforms, integration of versatile technologies for data stream management and development of elaborate data analysis techniques. This paper describes a complete and an integrated multi-sensing framework, with which the sensing platforms, data fusion and analysis algorithms, and software architecture suitable for pervasive healthcare applications are presented. The potential value of the proposed framework for pervasive patient monitoring is demonstrated and initial results obtained from our current research experiences are described.
Ali R, Atallah L, Lo B, et al., 2009, Transitional activity recognition with manifold embedding, Pages: 98-102
Activity monitoring is an important part of pervasive sensing, particularly for assessing activities of daily living for elderly patients and those with chronic diseases. Previous studies have mainly focused on binary transitions between activities, but have overlooked detailed transitional patterns. For patient studies, this transition period can be prolonged and may be indicative of the progression of disease. To observe, as well as quantify, transitional activities, a manifold embedding approach is proposed in this paper. The method uses a spectral graph partitioning and transition labelling approach for identifying principal and transitional activity patterns. The practical value of the work is demonstrated through laboratory experiments for identifying specific transitions and detecting simulated motion impairment. © 2009 IEEE.
Atallah L, Lo B, Yang GZ, et al., 2009, Detecting walking gait impairment with an ear-worn sensor, Pages: 175-180
This paper investigates an ear worn sensor for the development of a gait analysis framework. Instead of explicitly defining gait features that indicate injury or impairment, an automatic method of feature extraction and selection is proposed. The proposed framework uses multi-resolution wavelet analysis and margin based feature selection. It was validated on three datasets; the first simulating a leg injury, the second simulating abdominal impairment that could result from surgery or injury and the third is a dataset collected from a patient during recovery from leg injury. The method shows a clear distinction of gait between injured and normal walking. It also illustrates the fact that using source separation before pattern classification can significantly improve the proposed gait analysis framework. © 2009 IEEE.
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
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- Citations: 63
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
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- Citations: 47
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
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
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- Citations: 41
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-+
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- Citations: 19
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-+
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- Citations: 29
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
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- Citations: 14
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
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- Citations: 4
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-+
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- Citations: 6
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
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- Citations: 16
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.
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.
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.
Pansiot J, King RC, McIlwraith DG, et al., 2008, ClimBSN: Climber performance monitoring with BSN, Pages: 33-36
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.
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.
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.
Lo B, Chung AJ, Stoyanov D, et al., 2008, Real-time intra-operative 3D tissue deformation recovery, Pages: 1387 -1390-1387 -1390
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
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