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

202 results found

Chen C-M, Onyenso K, Yang G-Z, Lo Bet 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

Conference paper

Poon CCY, Lo BPL, Yuce MR, Alomainy A, Hao Yet al., 2015, Body Sensor Networks: In the Era of Big Data and Beyond., IEEE Rev Biomed Eng, Vol: 8, Pages: 4-16

Body sensor networks (BSN) have emerged as an active field of research to connect and operate sensors within, on or at close proximity to the human body. BSN have unique roles in health applications, particularly to support real-time decision making and therapeutic treatments. Nevertheless, challenges remain in designing BSN nodes with antennas that operate efficiently around, ingested or implanted inside the human body, as well as new methods to process the heterogeneous and growing amount of data on-node and in a distributed system for optimized performance and power consumption. As the battery operating time and sensor size are two important factors in determining the usability of BSN nodes, ultralow power transceivers, energy-aware network protocol, data compression, on-node processing, and energy-harvesting techniques are highly demanded to ultimately achieve a self-powered BSN.

Journal article

Wong C, Zhang Z, Lo B, Yang G-Zet al., 2014, Markerless motion capture using appearance and inertial data, Pages: 6907-6910

Conference paper

Zheng Y-L, Ding X-R, Poon CCY, Lo BPL, Zhang H, Zhou X-L, Yang G-Z, Zhao N, Zhang Y-Tet al., 2014, Unobtrusive Sensing and Wearable Devices for Health Informatics, IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, Vol: 61, Pages: 1538-1554, ISSN: 0018-9294

Journal article

Atallah L, Wiik A, Lo B, Cobb JP, Amis AA, Yang G-Zet al., 2014, Gait asymmetry detection in older adults using a light ear-worn sensor, PHYSIOLOGICAL MEASUREMENT, Vol: 35, Pages: N29-N40, ISSN: 0967-3334

Journal article

Chen CM, Kwasnicki R, Lo B, Yang GZet 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

Conference paper

Jarchi D, Lo B, Ieong E, Nathwani D, Yang G-Zet 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

Conference paper

Li L, Atallah L, Lo B, Yang G-Zet 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

Conference paper

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

Conference paper

Kirby GSJ, Kwasnicki RM, Hargrove C, Rees JL, Sodergren MH, Yang GZ, Lo BPLet al., 2014, Wireless body sensor for objective assessment of surgical performance on a standardised FLS task, Pages: 147-153

Copyright © 2014 ICST. 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.

Conference paper

Lo B, Panousopoulou A, Thiemjarus S, Yang G-Zet al., 2014, Autonomic Sensing, Body Sensor Networks, Publisher: Springer London, Pages: 405-462, ISBN: 9781447163732

Book chapter

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

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