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

 

Overview

Activity Profiling for Minimially Invasive Surgery

Activity Profiling for MISMinimially Invasive Surgery (MIS) or key hole surgery can greatly reduce the trauma of the patients and shorten the recovery period.  However, the complexity of the instrument controls, restricted vision and mobility, difficult hand-eye coordination, and lack of tactile preception of the MIS operations require a high degree of dexterity of the operator.  Existing research has shown that it is important to provide task-oriented analyses that capture the complexity of relationships between perceptual-motor, spatial, and experiential factors in addition to external factors such as experience and coordination of opreating team and the quality of equipment used.  All these are important to the investigation and priorisation of the education needs of the surgeons at all levels with a view to establish preferred models for structured education and training.
The objective of this work is to investigate the use of computer vision for profiling the activities (in terms of instrument tissue operation, operating team dynamics, etc) during minimal invasive surgeries such that key operational manoeuvres can be tracked and analysed.
 

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Body Sensor Networks

e-AR (ear-worn Activity Recognition) Sensor

e-AR Sensor

e-AR Sensor

The design of the e-AR sensor was inspired by the human inner ear. The human inner ear consists of an auditory system (the cochlea) and a balancing (vestibular) system.  Within the vestibular system there are two sensory mechanisms, called the semicircular canals and the otoliths for sensing the rotational and translational motions. To emulate the sensory functions of the human vestibular system, the e-AR sensor is equipped with a MEMS (Micro Electro-Mechanical System) 3-axis accelerometer which is capable of detecting acceleration in 3 dimensions (up and down, left and right, back and forth). An accelerometer consists of a mass, and when the sensor is moved, the mass moves. Electronic sensing components determine the acceleration. By positioning the accelerometer on the ear, the e-AR sensor can pick up similar information to the vestibular system, and this records the posture and activities of the user.

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Pervasive Sensing for Sports Training

ESPRIT BlackboxESPRIT Blackbox HTML5 Display

In situ measurements of athletes’ physiological parameters during training and competitions are essential for identifying the underlying elements which affects sport performances. To enable real-time continuous measurements of athletes’ performance indices during training and competitions, a number of pervasive sensing technologies have been introduced under the ESPRIT (Elite Sport Performance Research in Training) Programme. Through working closely with sports governing bodies, technologies developed have been validated for different sport exemplars. Under the programme, different novel sensing technologies have been introduced from body worn biomotion sensors, wheel chair velocity and tracking system, to rowing blackbox, and video tracking system.

 
Pervasive sensor for gait analysis

e-AR SensorGait analysis is an important part of orthopedics, rehabilitation, sport medicine and biomechanics. To quantify gait, motion capture systems, electromyography (EMG) sensors and force plates (or foot pressure insoles) are commonly used to capture kinematics, muscle contraction, and Ground Reaction Force (GRF). To measure GRF, force plates or foot pressure insoles are commonly used; however, both systems are costly, thus limiting their use mainly to dedicated biomechanics laboratories. The measurements performed are also constrained to brief time periods which may or may not represent the normal walking/running conditions. A novel concept of an ear-worn sensor is introduced for pervasive sensing of GRF patterns, and a hierarchical Bayesian network is developed to estimate the planar force distribution from the raw e-AR sensor signals. The approach has been validated against commercially available foot pressure sensing insoles and it has been shown that the sensor can accurately estimate the plantar force distribution.

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Collaborators

Margaret Mulholland, Swiss Cottage School, People with Learning Difficulties, 2014

Guest Lectures

Body Sensor Networks for healthcare, wellbeing and sport, Workshop on Wearable Sweat Sensors for Sports and Health, Dublin City University, Dublin, 2013

Platforms for Sports Performance Monitoring, Workshop on Pervasive Sensing in Sports and Extreme Environments in conjunction with BSN2012, Imperial College London, 2012

Pervasive sensing for sports, wellbeing and healthcare applications, OBN (Oxfordshire Biotechnology Network ) BioTuesday, Oxford, 2011

Sports Technologies: New multimedia and remote sensing for the enhancement of sports performance, LTN Sports Technologies - Evening Networking Event, Queen Mary BioScience Innovation Centre, London, 2010

ESPRIT - Application of embedded systems in sports performance monitoring, ICTES 2010, Bangkok, Thailand, 2010

Body Sensor Networks – Research Challenges and Opportunities, IET Body Centric Seminar, The IET – Savoy Place London, 2007

UbiSense, Ubimon – Progress and demonstrators, United Kingdom/Denmark Pervasive Healthcare Seminar, Aarhus, Denmark, 2006

Body Sensor Networks – An infrastructure for pervasive healthcare system, IEEE BioCAS 2006, British Library, London, UK, 2006

Engineering in Health, IEE, IEE Midlands Engineering Centre, Birmingham, 2005

Research Staff

Wong,C

Research Student Supervision

Chen,ZK, Vision Guidance and Control for the Personalised Stent Graft Manufacturing Robot

Georgina Kirby,, Objective assessment of surgical dexterity with pervasive sensors

Huen,YD, An Assistive Wearable Robot for Rehabilitation

Jonquiere,HDTDL, An Operating System for Internet of Things (IoT)

Middlecote,D, Indoor localisation with Bluetooth Low Energy and an Inertial Sensor

Onyenso,K, A Sensor Enabled Sock Insole for Patients with Diabetic Peripheral Neuropathy

Purewal,A, An Interactive Game for Children with Learning Difficulties

Rankine,S, A Wearable Sensor System for Capturing Challenging Behaviours

Ruoxi Yu,, Autonomic Sensing for Body Sensor Networks