197 results found
Sun Y, Lo FPW, Lo B, 2019, EEG-based user identification system using 1D-convolutional long short-term memory neural networks, Expert Systems with Applications, Vol: 125, Pages: 259-267, ISSN: 0957-4174
© 2019 Elsevier Ltd Electroencephalographic (EEG) signals have been widely used in medical applications, yet the use of EEG signals as user identification systems for healthcare and Internet of Things (IoT) systems has only gained interests in the last few years. The advantages of EEG-based user identification systems lie in its dynamic property and uniqueness among different individuals. However, it is for this reason that manually designed features are not always adapted to the needs. Therefore, a novel approach based on 1D Convolutional Long Short-term Memory Neural Network (1D-Convolutional LSTM) for EEG-based user identification system is proposed in this paper. The performance of the proposed approach was validated with a public database consists of EEG data of 109 subjects. The experimental results showed that the proposed network has a very high averaged accuracy of 99.58%, when using only 16 channels of EEG signals, which outperforms the state-of-the-art EEG-based user identification methods. The combined use of CNNs and LSTMs in the proposed 1D-Convolutional LSTM can greatly improve the accuracy of user identification systems by utilizing the spatiotemporal features of the EEG signals with LSTM, and lowering cost of the systems by reducing the number of EEG electrodes used in the systems.
Singh RK, Varghese RJ, Liu J, et al., 2019, A multi-sensor fusion approach for intention detection, Biosystems and Biorobotics, Pages: 454-458
© Springer Nature Switzerland AG 2019. For assistive devices to seamlessly and promptly assist users with activities of daily living (ADL), it is important to understand the user’s intention. Current assistive systems are mostly driven by unimodal sensory input which hinders their accuracy and responses. In this paper, we propose a context-aware sensor fusion framework to detect intention for assistive robotic devices which fuses information from a wearable video camera and wearable inertial measurement unit (IMU) sensors. A Naive Bayes classifier is used to predict the intent to move from IMU data and the object classification results from the video data. The proposed approach can achieve an accuracy of 85.2% in detecting movement intention.
Bernstein A, Varghese RJ, Liu J, et al., 2019, An Assistive Ankle Joint Exoskeleton for Gait Impairment, Biosystems and Biorobotics, Pages: 658-662
© 2019, Springer Nature Switzerland AG. Motor rehabilitation and assistance post-stroke are becoming a major concern for healthcare services with an increasingly aging population. Wearable robots can be a technological solution to support gait rehabilitation and to provide assistance to enable users to carry out activities of daily living independently. To address the need for long-term assistance for stroke survivors suffering from drop foot, this paper proposes a low-cost, assistive ankle joint exoskeleton for gait assistance. The proposed exoskeleton is designed to provide ankle foot support thus enabling normal walking gait. Baseline gait reading was recorded from two force sensors attached to a custom-built shoe insole of the exoskeleton. From our experiments, the average maximum force during heel-strike (63.95 N) and toe-off (54.84 N) were found, in addition to the average period of a gait cycle (1.45 s). The timing and force data were used to control the actuation of tendons of the exoskeleton to prevent the foot from preemptively hitting the ground during swing phase.
Ahmed MR, Zhang Y, Feng Z, et al., 2019, Neuroimaging and Machine Learning for Dementia Diagnosis: Recent Advancements and Future Prospects., IEEE Rev Biomed Eng, Vol: 12, Pages: 19-33
Dementia, a chronic and progressive cognitive declination of brain function caused by disease or impairment, is becoming more prevalent due to the aging population. A major challenge in dementia is achieving accurate and timely diagnosis. In recent years, neuroimaging with computer-aided algorithms have made remarkable advances in addressing this challenge. The success of these approaches is mostly attributed to the application of machine learning techniques for neuroimaging. In this review paper, we present a comprehensive survey of automated diagnostic approaches for dementia using medical image analysis and machine learning algorithms published in the recent years. Based on the rigorous review of the existing works, we have found that, while most of the studies focused on Alzheimer's disease, recent research has demonstrated reasonable performance in the identification of other types of dementia remains a major challenge. Multimodal imaging analysis deep learning approaches have shown promising results in the diagnosis of these other types of dementia. The main contributions of this review paper are as follows. 1) Based on the detailed analysis of the existing literature, this paper discusses neuroimaging procedures for dementia diagnosis. 2) It systematically explains the most recent machine learning techniques and, in particular, deep learning approaches for early detection of dementia.
Lo FP-W, Sun Y, Qiu J, et al., 2018, Food Volume Estimation Based on Deep Learning View Synthesis from a Single Depth Map, NUTRIENTS, Vol: 10, ISSN: 2072-6643
Sun Y, Lo B, 2018, An artificial neural network framework for gait based biometrics, IEEE Journal of Biomedical and Health Informatics, ISSN: 2168-2194
OAPA As the popularity of wearable and implantable Body Sensor Network (BSN) devices increases, there is a growing concern regarding the data security of such power-constrained miniaturized medical devices. With limited computational power, BSN devices are often not able to provide strong security mechanisms to protect sensitive personal and health information, such as one's physiological data. Consequently, many new methods of securing Wireless Body Area Networks (WBANs) have been proposed recently. One effective solution is the Biometric Cryptosystem (BCS) approach. BCS exploits physiological and behavioral biometric traits, including face, iris, fingerprints, Electrocardiogram (ECG), and Photoplethysmography (PPG). In this paper, we propose a new BCS approach for securing wireless communications for wearable and implantable healthcare devices using gait signal energy variations and an Artificial Neural Network (ANN) framework. By simultaneously extracting similar features from BSN sensors using our approach, binary keys can be generated on demand without user intervention. Through an extensive analysis on our BCS approach using a gait dataset, the results have shown that the binary keys generated using our approach have high entropy for all subjects. The keys can pass both NIST and Dieharder statistical tests with high efficiency. The experimental results also show the robustness of the proposed approach in terms of the similarity of intra-class keys and the discriminability of the inter-class keys.
Teachasrisaksakul K, Wu L, Yang G-Z, et al., 2018, Hand Gesture Recognition with Inertial Sensors., 40th International Conference of the IEEE Engineering in Medicine and Biology Society, Publisher: IEEE, Pages: 3517-3520, ISSN: 1557-170X
Dyscalculia is a learning difficulty hindering fundamental arithmetical competence. Children with dyscalculia often have difficulties in engaging in lessons taught with traditional teaching methods. In contrast, an educational game is an attractive alternative. Recent educational studies have shown that gestures could have a positive impact in learning. With the recent development of low cost wearable sensors, a gesture based educational game could be used as a tool to improve the learning outcomes particularly for children with dyscalculia. In this paper, two generic gesture recognition methods are proposed for developing an interactive educational game with wearable inertial sensors. The first method is a multilayered perceptron classifier based on the accelerometer and gyroscope readings to recognize hand gestures. As gyroscope is more power demanding and not all low-cost wearable device has a gyroscope, we have simplified the method using a nearest centroid classifier for classifying hand gestures with only the accelerometer readings. The method has been integrated into open-source educational games. Experimental results based on 5 subjects have demonstrated the accuracy of inertial sensor based hand gesture recognitions. The results have shown that both methods can recognize 15 different hand gestures with the accuracy over 93%.
Deligianni F, Wong C, Lo B, et al., 2018, A fusion framework to estimate plantar ground force distributions and ankle dynamics, INFORMATION FUSION, Vol: 41, Pages: 255-263, ISSN: 1566-2535
Guo Y, Zhang Y, Mursalin M, et al., 2018, Automated epileptic seizure detection by analyzing wearable EEG signals using extended correlation-based feature selection, Pages: 66-69
© 2018 IEEE. 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.
Sun Y, Yang GZ, Lo B, 2018, An artificial neural network framework for lower limb motion signal estimation with foot-mounted inertial sensors, Pages: 132-135
© 2018 IEEE. This paper proposes a novel artificial neural network based method for real-time gait analysis with minimal number of Inertial Measurement Units (IMUs). Accurate lower limb attitude estimation has great potential for clinical gait diagnosis for orthopaedic patients and patients with neurological diseases. However, the use of multiple wearable sensors hinder the ubiquitous use of inertial sensors for detailed gait analysis. This paper proposes the use of two IMUs mounted on the shoes to estimate the IMU signals at the shin, thigh and waist for accurate attitude estimation of the lower limbs. By using the artificial neural network framework, the gait parameters, such as angle, velocity and displacements of the IMUs can be estimated. The experimental results have shown that the proposed method can accurately estimate the IMUs signals on the lower limbs based only on the IMU signals on the shoes, which demonstrates its potential for lower limb motion tracking and real-time gait analysis.
Gao A, Lo FPW, Lo B, 2018, Food volume estimation for quantifying dietary intake with a wearable camera, Pages: 110-113
© 2018 IEEE. A novel food volume measurement technique is proposed in this paper for accurate quantification of the daily dietary intake of the user. The technique is based on simultaneous localisation and mapping (SLAM), a modified version of convex hull algorithm, and a 3D mesh object reconstruction technique. This paper explores the feasibility of applying SLAM techniques for continuous food volume measurement with a monocular wearable camera. A sparse map will be generated by SLAM after capturing the images of the food item with the camera and the multiple convex hull algorithm is applied to form a 3D mesh object. The volume of the target object can then be computed based on the mesh object. Compared to previous volume measurement techniques, the proposed method can measure the food volume continuously with no prior information such as pre-defined food shape model. Experiments have been carried out to evaluate this new technique and showed the feasibility and accuracy of the proposed algorithm in measuring food volume.
Gu X, Deligianni F, Lo B, et al., 2018, Markerless gait analysis based on a single RGB camera, Pages: 42-45
© 2018 IEEE. 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.
Berthelot M, Yang GZ, Lo B, 2018, Tomographic probe for perfusion analysis in deep layer tissue, Pages: 86-89
© 2018 IEEE. Continuous buried soft tissue free flap postoperative monitoring is crucial to detect flap failure and enable early intervention. In this case, clinical assessment is challenging as the flap is buried and only implantable or hand held devices can be used for regular monitoring. These devices have limitations in their price, usability and specificity. Near-infrared spectroscopy (NIRS) has shown promising results for superficial free flap postoperative monitoring, but it has not been considered for buried free flap, mainly due to the limited penetration depth of conventional approaches. A wearable wireless tomographic probe has been developed for continuous monitoring of tissue perfusion at different depths. Using the NIRS method, blood flow can be continuously measured at different tissue depths. This device has been designed following conclusions of extensive computerised simulations and it has been validated using a vascular phantom.
Lo BPL, Innovative Sensing Technologies for Developing Countries, IEEE Biomedical and Health Informatics BHI 2018
Berthelot M, Yang G-Z, Lo B, 2018, A Self-Calibrated Tissue Viability Sensor for Free Flap Monitoring, IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS, Vol: 22, Pages: 5-14, ISSN: 2168-2194
Sun Y, Lo B, 2018, Random number generation using inertial measurement unit signals for On-Body IoT devices
© 2018 Institution of Engineering and Technology. All rights reserved. With increasing popularity of wearable and implantable technologies for medical applications, there is a growing concern on the security and data protection of the on-body Internet-of-Things (IoT) devices. As a solution, cryptographic system is often adopted to encrypt the data, and Random Number Generator (RNG) is of vital importance to such system. This paper proposes a new random number generation method for securing on-body IoT devices based on temporal signal variations of the outputs of the Inertial Measurement Units (IMU) worn by the users while walking. As most new wearable and implantable devices have built-in IMUs and walking gait signals can be extracted from these body sensors, this method can be applied and integrated into the cryptographic systems of these new devices. To generate the random numbers, this method divides IMU signals into gait cycles and generates bits by comparing energy differences between the sensor signals in a gait cycle and the averaged IMU signals in multiple gait cycles. The generated bits are then re-indexed in descending order by the absolute values of the associated energy differences to further randomise the data and generate high-entropy random numbers. Two datasets were used in the studies to generate random numbers, where were rigorously tested and passed four well-known randomness test suites, namely NIST-STS, ENT, Dieharder, and RaBiGeTe.
Deligianni F, Ravi D, Roots S, et al., Pervasive monitoring of mental health for preventing financial distress, Body Sensor Networks Conference (BSN’17), Publisher: IEEE
Mental health disorders are rankedamong the top twenty main causes of disability worldwide. It was found that thereis an intriguing relationship between mental health problems and financial difficulties.Current technology uses mobile apps for self-monitoring of mental health conditions with a potential to avoid debt crisis caused by mental illness. In this paper, we propose the use of a wearable sensor as an objective evaluation tool for monitoring the emotional well-being of the subject. By fusing the sensory data with financial data, an intelligent self-guard system is proposed for preventing excessive spending caused by mental condition.
Sun Y, Wong C, Yang G-Z, et al., 2017, Secure Key Generation Using Gait Features for Body Sensor Networks, 14th Annual IEEE International Conference on Wearable and Implantable Body Sensor Networks (BSN), Publisher: IEEE, Pages: 206-210, ISSN: 2376-8886
Zhang R, Ravi D, Yang G-Z, et al., 2017, A Personalized Air Quality Sensing System - A preliminary study on assessing the air quality of London Underground Stations, 14th Annual IEEE International Conference on Wearable and Implantable Body Sensor Networks (BSN), Publisher: IEEE, Pages: 111-114, ISSN: 2376-8886
Ravi D, Wong C, Lo B, et al., 2017, A Deep Learning Approach to on-Node Sensor Data Analytics for Mobile or Wearable Devices, IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS, Vol: 21, Pages: 56-64, ISSN: 2168-2194
Berthelot M, Yang G-Z, Lo B, 2017, Preliminary Study for Hemodynamics Monitoring using a Wearable Device Network, 14th Annual IEEE International Conference on Wearable and Implantable Body Sensor Networks (BSN), Publisher: IEEE, Pages: 115-118, ISSN: 2376-8886
Wijayasingha LNS, Lo B, 2016, A wearable sensing framework for improving personal and oral hygiene for people with developmental disabilities, Pages: 7-13
© 2016 IEEE. People with developmental disabilities often face difficulties in coping with daily activities and many require constant support. One of the major health issues for people with developmental disabilities is personal hygiene. Many lack the ability, poor memory or lack of attention to carry out normal daily activities like brushing teeth and washing hands. Poor personal hygiene may result in increased susceptibility to infection and other health issues. To enable independent living and improve the quality of care for people with developmental abilities, this paper proposes a new wearable sensing framework to monitoring personal hygiene. Based on a smartwatch, this framework is designed as a pervasive monitoring and learning tool to provide detailed evaluation and feedback to the user on hand washing and tooth brushing. A preliminary study was conducted to assess the performance of the approach, and the results showed the reliability and robustness of the framework in quantifying and assessing hand washing and tooth brushing activities.
Ding X-R, Zhao N, Yang G-Z, et al., 2016, Continuous Blood Pressure Measurement From Invasive to Unobtrusive: Celebration of 200th Birth Anniversary of Carl Ludwig, IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS, Vol: 20, Pages: 1455-1465, ISSN: 2168-2194
Chen S, Lach J, Lo B, et al., 2016, Toward Pervasive Gait Analysis With Wearable Sensors: A Systematic Review, IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS, Vol: 20, Pages: 1521-1537, ISSN: 2168-2194
Jarchi D, Lo B, Wong C, et al., 2016, Gait Analysis From a Single Ear-Worn Sensor: Reliability and Clinical Evaluation for Orthopaedic Patients, IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING, Vol: 24, Pages: 882-892, ISSN: 1534-4320
Lo BPL, Yang GZ, Merrifield R, 2016, A SENSING SYSTEM
The present disclosure relates to a system for detecting challenging behaviors, enabling a customizable learning experience, and providing context-aware, intelligent daily assistance for people with learning disabilities.In addition to general health problems, people with learning disabilities are known to have higher incidents of dementia, respiratory diseases, gastrointestinal cancer, ADHD/hyperkinesis and conduct disorders, epilepsy, physical and sensory impairments, dysphagia, poor oral health, and tend to be prone to injuries, accidents and falls. Often due to the lack of expressive skills, people with learning disabilities are more likely to have undiagnosed long-term conditions and which leads to high risk of premature death. With the aim of improving the care of people with learning disabilities, a new wearable sensing system is provided which comprises a new miniaturized wearable sensor, for example that may be worn either as a wrist worn or ear worn sensor, and a seamlessly integrated mobile app that adapts to individual care needs.
Akay M, Coatrieux G, Hao Y, et al., 2016, Guest Editorial MobiHealth 2014, IEEE HealthCom 2014, and IEEE BHI 2014, IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS, Vol: 20, Pages: 731-732, ISSN: 2168-2194
Ravi D, Wong C, Lo B, et al., 2016, Deep Learning for Human Activity Recognition: A Resource Efficient Implementation on Low-Power Devices, 13th IEEE International Conference on Wearable and Implantable Body Sensor Networks (BSN), Publisher: IEEE, Pages: 71-76, ISSN: 2376-8886
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