A primary motivation of our research is the monitoring of physical, physiological, and biochemical parameters - in any environment and without activity restriction and behaviour modification - through using miniaturised, wireless Body Sensor Networks (BSN). Key research issues that are currently being addressed include novel sensor designs, ultra-low power microprocessor and wireless platforms, energy scavenging, biocompatibility, system integration and miniaturisation, processing-on-node technologies combined with novel ASIC design, autonomic sensor networks and light-weight communication protocols. Our research is aimed at addressing the future needs of life-long health, wellbeing and healthcare, particularly those related to demographic changes associated with an ageing population and patients with chronic illnesses. This research theme is therefore closely aligned with the IGHI’s vision of providing safe, effective and accessible technologies for both developed and developing countries.
Some of our latest works were exhibited at the 2015 Royal Society Summer Science Exhibition.
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Conference paperSun Y, Yang G, Lo B, 2018,
An artificial neural network framework for lower limb motion signal estimation with foot-mounted inertial sensors, IEEE Conference on Body Sensor Networks (BSN) 2018, Publisher: IEEE
This paper proposes a novel artificial neuralnetwork based method for real-time gait analysis with minimalnumber of Inertial Measurement Units (IMUs). Accurate lowerlimb attitude estimation has great potential for clinical gait di-agnosis for orthopaedic patients and patients with neurologicaldiseases. However, the use of multiple wearable sensors hinderthe ubiquitous use of inertial sensors for detailed gait analysis.This paper proposes the use of two IMUs mounted on theshoes to estimate the IMU signals at the shin, thigh and waistfor accurate attitude estimation of the lower limbs. By usingthe artificial neural network framework, the gait parameters,such as angle, velocity and displacements of the IMUs canbe estimated. The experimental results have shown that theproposed method can accurately estimate the IMUs signals onthe lower limbs based only on the IMU signals on the shoes,which demonstrates its potential for lower limb motion trackingand real-time gait analysis.
Conference paperGao A, Lo P, Lo B, 2018,
Food volume estimation for quantifying dietary intake with a wearable camera, Body Sensor Networks Conference 2018, Publisher: IEEE
A novel food volume measurement technique isproposed in this paper for accurate quantification of the dailydietary intake of the user. The technique is based on simul-taneous localisation and mapping (SLAM), a modified versionof convex hull algorithm, and a 3D mesh object reconstructiontechnique. This paper explores the feasibility of applying SLAMtechniques for continuous food volume measurement with amonocular wearable camera. A sparse map will be generatedby SLAM after capturing the images of the food item withthe camera and the multiple convex hull algorithm is appliedto form a 3D mesh object. The volume of the target objectcan then be computed based on the mesh object. Comparedto previous volume measurement techniques, the proposedmethod can measure the food volume continuously with no priorinformation such as pre-defined food shape model. Experimentshave been carried out to evaluate this new technique andshowed the feasibility and accuracy of the proposed algorithmin measuring food volume.
Conference paperSchmitz A, Thompson AJ, Berthet-Rayne P, et al., 2017,
Snake like continuum robots are increasingly used for minimally invasive surgery. Most robotic devices of this sort that have been reported to date are controlled in an open loop manner. Using shape sensing to provide closed loop feedback would allow for more accurate control of the robot's position and, hence, more precise surgery. Fiber Bragg Gratings, magnetic sensors and optical reflectance sensors have all been reported for this purpose but are often limited by their cost, size, stiffness or complexity of fabrication. To address this issue, we designed, manufactured and tested a prototype two-link robot with a built-in fiber-optic shape sensor that can deliver and control the position of a CO 2 -laser fiber for soft tissue ablation. The shape sensing is based on optical reflectance, and the device (which has a 4 mm outer diameter) is fabricated using 3D printing. Here we present proof-of-concept results demonstrating successful shape sensing - i.e. measurement of the angular displacement of the upper link of the robot relative to the lower link - in real time with a mean measurement error of only 0.7°.
Conference paperKassanos P, Yang G-Z, 2017,
A CMOS Programmable Phase Shifter for Compensating Synchronous Detection Bioimpedance Systems, 24th IEEE International Conference on Electronics, Circuits and Systems (ICECS), Publisher: IEEE, Pages: 218-221
Electrical bioimpedance is used in a wide variety of biomedical applications. A common circuit topology for extracting tissue impedance from voltage measurements following excitation with an ac current, is synchronous detection (SD), which requires both quadrature (Q) and in phase (I) signals. These are often obtained by a quadrature oscillator, which also drives a voltage controlled current source (VCCS). At high frequencies the VCCS introduces phase delays to the output current, leading to frequency dependent errors in the calculated impedance, as the injected current will no longer be in phase or in quadrature with I and Q. In this paper, a novel low power programmable CMOS phase shifter is presented, which is driven by two quadrature sine waves to produce an output signal between 0° and 90°. The proposed technique is implemented with linearized transconductors operating in weak inversion for linear transconductance programmability. In contrast to other solutions, the phase delay is not a function of the power supply or a capacitor, and thus it is not affected by changes in the power supply and is more chip area efficient. Preliminary simulation results demonstrating the operation of the topology are presented with a 0.18 pm TSMC technology.
Journal articleBerthelot ME, Yang GZ, Lo B, 2017,
In fasciocutaneous free flap surgery, close postoperative monitoring is crucial for detecting flap failure, as around 10% of cases require additional surgery due to compromised anastomosis. Different biochemical and biophysical techniques have been developed for continuous flap monitoring, however, they all have shortcoming in terms of reliability, elevated cost, potential risks to the patient and inability to adapt to the patient's phenotype. A wearable wireless device based on near infrared spectroscopy (NIRS) has been developed for continuous blood flow and perfusion monitoring by quantifying tissue oxygen saturation (StO2). This miniaturized and low cost device is designed for postoperative monitoring of flap viability. With self-calibration, the device can adapt itself to the characteristics of the patients' skin such as tone and thickness. An extensive study was conducted with 32 volunteers. The experimental results show that the device can obtain reliable StO2 measurements across different phenotypes (age, sex, skin tone and thickness). To assess its ability to detect flap failure, the sensor was validated with an animal study. Free groin flaps were performed on 16 Sprague Dawley rats. Results demonstrate the accuracy of the sensor in assessing flap viability and identifying the origin of failure (venous or arterial thrombosis).
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