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

221 results found

Atallah L, Aziz O, Gray E, Lo B, Yang G-Zet 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

Journal article

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

Conference paper

Atallah L, Aziz O, Gray E, Lo B, Yang G-Zet 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

Journal article

Lo B, Thiemjarus S, Panousopoulou A, Yang GZet al., 2013, Bio-inspired Design for Body Sensor Networks, IEEE Signal Processing Magazine (to appear)

Journal article

Wieboldt J, Atallah L, Kelly JL, Shrikrishna D, Gyi KM, Lo B, Yang GZ, Bilton D, Polkey MI, Hopkinson NSet 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

Journal article

Atallah L, Wiik A, Jones GG, Lo B, Cobb JP, Amis A, Yang G-Zet 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

Journal article

Atallah L, Lo B, Yang G-Z, 2012, Can pervasive sensing address current challenges in global healthcare?, J Epidemiol Glob Health, Vol: 2, Pages: 1-13

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.

Journal article

Atallah L, Mcllwraith D, Thiemjarus S, Lo B, Yang G-Zet al., 2012, Distributed inferencing with ambient and wearable sensors, WIRELESS COMMUNICATIONS & MOBILE COMPUTING, Vol: 12, Pages: 117-131, ISSN: 1530-8669

Journal article

Ali R, Atallah L, Lo B, Yang G-Zet 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

Journal article

Liu J, Johns E, Atallah L, Pettitt C, Lo B, Frost G, Yang G-Zet al., 2012, An intelligent food-intake monitoring system using wearable sensors, Pages: 154-160

Conference paper

Kwasnicki RM, Low DA, Wong C, Jarchi D, Lo B, Mathias CJ, Darzi A, Yang GZet al., 2012, Investigating the feasibility of using objective motion data to assist the diagnosis and management of cardiovascular autonomic dysfunction, Pages: 137-137

Conference paper

Lo B, Atallah L, Crewther B, Spehar-Deleze AM, Anastasova S, Conway P, Cook C, Drawer S, West A, Vadgama P, Yang GZet al., 2011, Pervasive sensing for athletic training, Delivering London 2012: ICT Enabling the Games, Pages: 53-62

Journal article

Pansiot J, Zhang Z, Lo B, Yang GZet al., 2011, WISDOM: wheelchair inertial sensors for displacement and orientation monitoring, MEASUREMENT SCIENCE AND TECHNOLOGY, Vol: 22, ISSN: 0957-0233

Journal article

Zhang ZQ, Pansiot J, Lo B, Yang GZet 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.

Conference paper

Atallah L, Jones GG, Ali R, Leong JJH, Lo B, Yang GZet 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.

Conference paper

Atallah L, Lo B, King R, Yang G-Zet 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

Journal article

Atallah L, Leong JJH, Lo B, Yang G-Zet al., 2011, Energy Expenditure Prediction Using a Miniaturized Ear-Worn Sensor, MEDICINE AND SCIENCE IN SPORTS AND EXERCISE, Vol: 43, Pages: 1369-1377, ISSN: 0195-9131

Journal article

Aziz O, Atallah L, Lo B, Gray E, Athanasiou T, Darzi A, Yang GZet 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.

Journal article

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

Conference paper

Tolkiehn M, Atallah L, Lo B, Yang Get 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.

Conference paper

Bennebroek M, Barroso A, Atallah L, Lo B, Yang GZet 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.

Conference paper

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.

Conference paper

Atallah L, Lo B, King R, Yang GZet 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.

Conference paper

Atallah L, Zhang J, Lo BPL, Shrikrishna D, Kelly JL, Jackson A, Polkey MI, Yang G, Hopkinson NSet 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

Journal article

Pansiot J, Elsaify A, Lo B, Yang GZet 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.

Conference paper

Valibeik S, Ballantyne J, Lo B, Darzi A, Yang GZet 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.

Conference paper

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.

Conference paper

ElHelw M, Pansiot J, McIlwraith D, Ali R, Lo B, Atallah Let 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.

Conference paper

Ali R, Atallah L, Lo B, Yang GZet 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.

Conference paper

Atallah L, Lo B, Yang GZ, Aziz Oet 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.

Conference paper

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