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.
2 column colour block - Research areas
2 column colour block - Research areas 2
- Showing results for:
- Reset all filters
Conference paperGuo Y, Zhang Y, Mursalin M, et al., 2018,
Automated epileptic seizure detection by analyzing wearable EEG signals using extended correlation-based feature selection, IEEE BSN 2018, Publisher: IEEE, Pages: 66-69
Electroencephalogram (EEG) that measures the electrical activity of the brain has been widely employed for diagnosing epilepsy which is one kind of brain abnormalities. With the advancement of low-cost wearable brain-computer interface devices, it is possible to monitor EEG for epileptic seizure detection in daily use. However, it is still challenging to develop seizure classification algorithms with a considerable higher accuracy and lower complexity. In this study, we propose a lightweight method which can reduce the number of features for a multiclass classification to identify three different seizure statuses (i.e., Healthy, Interictal and Epileptic seizure) through EEG signals with a wearable EEG sensors using Extended Correlation-Based Feature Selection (ECFS). More specifically, there are three steps in our proposed approach. Firstly, the EEG signals were segmented into five frequency bands and secondly, we extract the features while the unnecessary feature space was eliminated by developing the ECFS method. Finally, the features were fed into five different classification algorithms, including Random Forest, Support Vector Machine, Logistic Model Trees, RBF Network and Multilayer Perceptron. Experimental results have shown that Logistic Model Trees provides the highest accuracy of 97.6% comparing to other classifiers.
Conference paperGu X, Deligianni F, Lo B, et al., 2018,
Gait analysis is an important tool for monitoring and preventing injuries as well as to quantify functional decline in neurological diseases and elderly people. In most cases, it is more meaningful to monitor patients in natural living environments with low-end equipment such as cameras and wearable sensors. However, inertial sensors cannot provide enough details on angular dynamics. This paper presents a method that uses a single RGB camera to track the 2D joint coordinates with state-of-the-art vision algorithms. Reconstruction of the 3D trajectories uses sparse representation of an active shape model. Subsequently, we extract gait features and validate our results in comparison with a state-of-the-art commercial multi-camera tracking system. Our results are comparable to those from the current literature based on depth cameras and optical markers to extract gait characteristics.
Book chapterThompson AJ, Yang G-Z, 2018,
Optical imaging and sensing modalities have been used in medical diagnosis for many years. An obvious example is endoscopy, which allows remote wide-field imaging of internal tissues using optical fibers and/or miniature charge-coupled device (CCD) cameras. While techniques such as endoscopy provide useful tools for clinicians, they do not typically allow a complete diagnosis to be made. Instead, physical biopsies may be required to confirm or refute the presence of disease. Furthermore, endoscopic procedures are both invasive and time-consuming. As such, much research is currently directed toward the development of devices that can provide a complete in vivo diagnosis without the requirement for a physical biopsy. Ideally, such devices should also be minimally or non-invasive, and they should provide immediate identification of disease at the point of care. Additionally, there is significant interest in the development of implantable diagnostic devices that can be left within patients’ bodies for extended periods of time (for several days or longer). Such systems could be used for automated disease diagnosis, and example applications include the detection of post-surgical infections as well as monitoring of the health status of patients undergoing chemotherapy. This chapter focuses on the development of optical instruments that can provide in situ diagnosis at the point of care, with an emphasis on progress towards miniature devices that may function as implants in the future.
Conference paperLo BPL, 2018,
Innovative sensing technologies for developing countries, IEEE Biomedical and Health Informatics BHI 2018
Journal articlePower MC, Thompson A, Anastasova-Ivanova S, et al., 2018,
A monolithic force-sensitive 3D microgripper fabricated on the tip of an optical fiber using 2-photon polymerization, Small, Vol: 14, Pages: 1703964-1-1703964-10, ISSN: 1613-6810
Microscale robotic devices have myriad potential applications including drug delivery, biosensing, cell manipulation, and microsurgery. In this work, a tethered, 3D, compliant grasper with an integrated force sensor is presented, the entirety of which is fabricated on the tip of an optical fiber in a single-step process using 2-photon polymerization. This gripper can prove useful for the interrogation of biological microstructures such as alveoli, villi, or even individual cells. The position of the passively actuated grasper is controlled via micromanipulation of the optical fiber, and the microrobotic device measures approximately 100 µm in length and breadth. The force estimation is achieved using optical interferometry: high-dimensional spectral readings are used to train artificial neural networks to predict the axial force exerted on/by the gripper. The design, characterization, and testing of the grasper are described and its real-time force-sensing capability with an accuracy below 2.7% of the maximum calibrated force is demonstrated.
This data is extracted from the Web of Science and reproduced under a licence from Thomson Reuters. You may not copy or re-distribute this data in whole or in part without the written consent of the Science business of Thomson Reuters.