221 results found
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
Berthelot M, Chen C-M, Yang G-Z, et al., 2016, Wireless wearable self-calibrated sensor for perfusion assessment of myocutaneous tissue, 13th IEEE International Conference on Wearable and Implantable Body Sensor Networks (BSN), Publisher: IEEE, Pages: 171-176, ISSN: 2376-8886
Abstract:Blood flow and perfusion monitoring are critical appraisal to ensure survival of tissue flap after reconstructive surgery. Many techniques have been developed over the years: from optical to chemical, invasive or not, they all have limitations in their price, risks and adaptiveness to the patient. A wireless wearable self-calibrated device, based on near infrared spectroscopy (NIRS) was developed for blood flow and perfusion monitoring contingent on tissue oxygen saturation (StO2). The use of such device is particularly relevant in the case of free flap myocutaneous reconstructive surgery; postoperative monitoring of the flap is crucial for a prompt intervention in case of thrombosis. Although failure rate is low, the rate of additional surgery following anastomosis problem is about 50%. NIRS has shown promising results for the monitoring of free flap, however lack of adaptation to its environment (ambient light) and users (body mass index (BMI), skin tone, alcohol and smoking habits or physical activity level) hinders the practical use of this technique. To overcome those limitations, a self-calibrated approach is introduced. Tested with is chaemia and cold water experiments on healthy subjects of different skin tones, its ability to personalize its calibration is demonstrated. Furthermore, using a vascular phantom, it is also able to detect pulses, differentiate venous and arterial coloured-like fluids with distinct clusters and detect significant changes in simulated partial venous occlusion. Placed in the trained classifier, partial occlusion data showed similar results between predicted and true classification. Further analysis from partial occlusion data showed that distinct clusters for 75% and 100% occlusion emerged.
Huen D, Liu J, Lo B, 2016, An integrated wearable robot for tremor suppression with context aware sensing, 13th IEEE International Conference on Wearable and Implantable Body Sensor Networks (BSN), Publisher: IEEE, Pages: 312-317, ISSN: 2376-8886
Abstract:Tremor is a neurological disorder which can significantly impede the daily functions of patients. The available treatments for patients with tremor are mainly pharmacotherapy and neurosurgery, but these treatments often have side effects. A wearable exoskeleton can potentially provide the assistance needed for patients with Parkinsonian or essential tremor to carry out daily activities and enable independent living. This paper presents the design and development of a 3D printed lightweight tremor suppression wearable exoskeleton. One of the major technical challenges for wearable robot is to maintain long battery life meanwhile miniature in size for practical use. This paper proposes an integrated approach where context aware Body Sensor Networks (BSN) sensors are incorporated to characterize voluntary and tremor movement, and detect activities of daily life (ADL). With the contextual information, the system can determine the intention of the user, optimize its control and minimize its power consumption by providing the necessary suppression only when needed. The preliminary result has shown that the wearable robot prototype can reduce the amplitude of simulated tremor by around 77%, and accurately identify different ADL with accuracy above 70%.
Ravi D, Wong C, Lo B, et al., 2016, Deep learning for human activity recognition: A resource efficient implementation on low-power devices, 2016 IEEE 13th International Conference on Wearable and Implantable Body Sensor Networks, Publisher: IEEE, Pages: 71-76, ISSN: 2376-8894
Human Activity Recognition provides valuable contextual information for wellbeing, healthcare, and sport applications. Over the past decades, many machine learning approaches have been proposed to identify activities from inertial sensor data for specific applications. Most methods, however, are designed for offline processing rather than processing on the sensor node. In this paper, a human activity recognition technique based on a deep learning methodology is designed to enable accurate and real-time classification for low-power wearable devices. To obtain invariance against changes in sensor orientation, sensor placement, and in sensor acquisition rates, we design a feature generation process that is applied to the spectral domain of the inertial data. Specifically, the proposed method uses sums of temporal convolutions of the transformed input. Accuracy of the proposed approach is evaluated against the current state-of-the-art methods using both laboratory and real world activity datasets. A systematic analysis of the feature generation parameters and a comparison of activity recognition computation times on mobile devices and sensor nodes are also presented.
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: 2168-2208
The papers in this special section were presented at three well-known conferences organized in 2014: EAI Mobihealth, IEEE HealthCom, and IEEE Biomedical and Health Informatics. EAI Mobihealth is an annually organized conference, which started in 2010, to address the demands of the rapidly evolving disciplines of wireless communications, mobile computing, and sensing technologies in healthcare. The IEEE-Healthcom is held every year since 1999 in different countries in Asia, Europe, and in America. It aims at bringing together interested parties working in the field of healthcare to exchange ideas, discuss innovative and emerging solutions, and develop collaborations. The IEEE Biomedical Health Informatics Conference started in 2013 and is organized every year providing the forum to showcase enabling technologies of computing, devices, imaging, sensors, and systems that optimize the acquisition, transmission, processing, storage, retrieval, visualization, and analysis of medical data. The aim of this special section is to present an overview of recent advances in sensing technologies, monitoring of patients, security and privacy of data transfer, provision of collaborative environments, data gathering and analysis from various sources, and predictive models, which all finally target the best strategy for patient monitoring and treatment.
Lo BPL, Ip H, Yang G-Z, 2016, Transforming health care: body sensor networks, wearables, and the Internet of Things, IEEE Pulse, Vol: 7, Pages: 4-8, ISSN: 2154-2287
This paper talks about body sensor networks, wearables, and the Internet of Things.
Jarchi D, Peters A, Lo B, et al., 2016, Assessment of the e-AR sensor for gait analysis of Parkinson;s Disease patients, 2015 IEEE 12th International Conference on Wearable and Implantable Body Sensor Networks (BSN), Publisher: IEEE, Pages: 1-6, ISSN: 2376-8886
This paper analyses gait patterns of patients with Parkinson;s Disease (PD) based on the acceleration data given by an e-AR sensor. Ten PD patients wearing the e-AR sensor walked along a 7m walkway and each session contained 16 repeated trials. An iterative algorithm has been proposed to produce robust estimations in the case of measurement noise and short-duration of gait signals. Step-frequency as a gait parameter derived from the estimated heel-contacts is calculated and validated using the CODA motion-capture system. Intersession variability of step-frequency for each patient and the overall variability across patients demonstrate a good agreement between estimations from the e-AR and CODA systems.
Jarchi D, Lo B, Wong C, et al., 2015, 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: 1558-0210
Objective assessment of detailed gait patterns after orthopaedic surgery is important for post-surgical follow-up and rehabilitation. The purpose of this paper is to assess the use of a single ear-worn sensor for clinical gait analysis. A reliability measure is devised for indicating the confidence level of the estimated gait events, allowing it to be used in free-walking environments and for facilitating clinical assessment of orthopaedic patients after surgery. Patient groups prior to or following anterior cruciate ligament (ACL) reconstruction and knee replacement were recruited to assess the proposed method. The ability of the sensor for detailed longitudinal analysis is demonstrated with a group of patients after lower limb reconstruction by considering parameters such as temporal and force-related gait asymmetry derived from gait events. The results suggest that the ear-worn sensor can be used for objective gait assessments of orthopaedic patients without the requirement and expense of an elaborate laboratory setup for gait analysis. It significantly simplifies the monitoring protocol and opens the possibilities for home-based remote patient assessment.
Cola G, Avvenuti M, Vecchio A, et al., 2015, An on-node processing approach for anomaly detection in gait, IEEE Sensors Journal, Vol: 15, Pages: 6640-6649, ISSN: 1558-1748
Kwasnicki RM, Hettiaratchy S, Okogbaa J, et al., 2015, Return of functional mobility after an open tibial fracture a sensor-based longitudinal cohort study using the Hamlyn Mobility Score, Bone and Joint Journal, Vol: 97B, Pages: 1118-1125, ISSN: 2049-4394
In this study we quantified and characterised the return of functional mobility following open tibial fracture using the Hamlyn Mobility Score. A total of 20 patients who had undergone reconstruction following this fracture were reviewed at three-month intervals for one year. An ear-worn movement sensor was used to assess their mobility and gait. The Hamlyn Mobility Score and its constituent kinematic features were calculated longitudinally, allowing analysis of mobility during recovery and between patients with varying grades of fracture. The mean score improved throughout the study period. Patients with more severe fractures recovered at a slower rate; those with a grade I Gustilo-Anderson fracture completing most of their recovery within three months, those with a grade II fracture within six months and those with a grade III fracture within nine months.Analysis of gait showed that the quality of walking continued to improve up to 12 months post-operatively, whereas the capacity to walk, as measured by the six-minute walking test, plateaued after six months.Late complications occurred in two patients, in whom the trajectory of recovery deviated by > 0.5 standard deviations below that of the remaining patients. This is the first objective, longitudinal assessment of functional recovery in patients with an open tibial fracture, providing some clarification of the differences in prognosis and recovery associated with different grades of fracture.
Kirby GSJ, Guyver P, Strickland L, et al., 2015, Assessing arthroscopic skills using wireless elbow-worn motion sensors, Journal of Bone and Joint Surgery-American Volume, Vol: 97A, Pages: 1119-1127, ISSN: 1535-1386
Background: Assessment of surgical skill is a critical component of surgical training. Approaches to assessment remain predominantly subjective, although more objective measures such as Global Rating Scales are in use. This study aimed to validate the use of elbow-worn, wireless, miniaturized motion sensors to assess the technical skill of trainees performing arthroscopic procedures in a simulated environment.Methods: Thirty participants were divided into three groups on the basis of their surgical experience: novices (n = 15), intermediates (n = 10), and experts (n = 5). All participants performed three standardized tasks on an arthroscopic virtual reality simulator while wearing wireless wrist and elbow motion sensors. Video output was recorded and a validated Global Rating Scale was used to assess performance; dexterity metrics were recorded from the simulator. Finally, live motion data were recorded via Bluetooth from the wireless wrist and elbow motion sensors and custom algorithms produced an arthroscopic performance score.Results: Construct validity was demonstrated for all tasks, with Global Rating Scale scores and virtual reality output metrics showing significant differences between novices, intermediates, and experts (p < 0.001). The correlation of the virtual reality path length to the number of hand movements calculated from the wireless sensors was very high (p < 0.001). A comparison of the arthroscopic performance score levels with virtual reality output metrics also showed highly significant differences (p < 0.01). Comparisons of the arthroscopic performance score levels with the Global Rating Scale scores showed strong and highly significant correlations (p < 0.001) for both sensor locations, but those of the elbow-worn sensors were stronger and more significant (p < 0.001) than those of the wrist-worn sensors.Conclusions: A new wireless assessment of surgical performance system for objective assessment of surgical skills has proven v
Gaglione A, Chen S, Lo B, et al., 2015, A Low-Power Opportunistic Communication Protocol for Wearable Applications, 12th IEEE International Conference on Wearable and Implantable Body Sensor Networks (BSN), Publisher: To appear
Recent trends in wearable applications demandflexible architectures being able to monitor people while theymove in free-living environments. Current solutions use eitherstore-download-offline processing or simple communicationschemes with real-time streaming of sensor data. This limits theapplicability of wearable applications to controlled environments(e.g, clinics, homes, or laboratories), because they need tomaintain connectivity with the base station throughout themonitoring process. In this paper, we present the design andimplementation of an opportunistic communication frameworkthat simplifies the general use of wearable devices in free-livingenvironments. It relies on a low-power data collection protocolthat allows the end user to opportunistically, yet seamlesslymanage the transmission of sensor data. We validate thefeasibility of the framework by demonstrating its use forswimming, where the normal wireless communication isconstantly interfered by the environment.
Lo BPL, Chen CM, Yang GZ, 2015, A Multiple PPG sensing platform
Teachasrisaksakul K, Zhang Z-Q, Yang G-Z, et al., 2015, Imitation of Dynamic Walking With BSN for Humanoid Robot, IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS, Vol: 19, Pages: 794-802, ISSN: 2168-2194
Ravi D, Lo B, Yang G, 2015, Real-time food intake classification and energy expenditure estimation on a mobile device, BSN 2015, Publisher: IEEE
Assessment of food intake has a wide range ofapplications in public health and life-style related chronic dis-ease management. In this paper, we propose a real-time foodrecognition platform combined with daily activity and energyexpenditure estimation. In the proposed method, food recognitionis based on hierarchical classification using multiple visual cues,supported by efficient software implementation suitable for real-time mobile device execution. A Fischer Vector representationtogether with a set of linear classifiers are used to categorizefood intake. Daily energy expenditure estimation is achieved byusing the built-in inertial motion sensors of the mobile device.The performance of the vision-based food recognition algorithmis compared to the current state-of-the-art, showing improvedaccuracy and high computational efficiency suitable for real-time feedback. Detailed user studies have also been performed todemonstrate the practical value of the software environment.
Abstract:Understanding the solid biomechanics of the human body is important to the study of structure and function of the body, which can have a range of applications in health care, sport, well-being, and workflow analysis. Conventional laboratory-based biomechanical analysis systems and observation-based tests are designed only to capture brief snapshots of the mechanics of movement. With recent developments in wearable sensing technologies, biomechanical analysis can be conducted in less-constrained environments, thus allowing continuous monitoring and analysis beyond laboratory settings. In this paper, we review the current research in wearable sensing technologies for biomechanical analysis, focusing on sensing and analytics that enable continuous, long-term monitoring of kinematics and kinetics in a free-living environment. The main technical challenges, including measurement drift, external interferences, nonlinear sensor properties, sensor placement, and muscle variations, that can affect the accuracy and robustness of existing methods and different methods for reducing the impact of these sources of errors are described in this paper. Recent developments in motion estimation in kinematics, mobile force sensing in kinematics, sensor reduction for electromyography, and the future direction of sensing for biomechanics are also discussed.
Nikita K, Bourbakis N, Lo B, et al., 2015, 4th international conference on wireless mobile communication and healthcare - MOBIHEALTH 2014, Pages: xii-xv
Cola G, Avvenuti M, Vecchio A, et al., 2015, An Unsupervised Approach for Gait-based Authentication, IEEE 12th International Conference on Wearable and Implantable Body Sensor Networks (BSN), Publisher: IEEE
Chen C-M, Onyenso K, Yang G-Z, et 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
Poon CCY, Lo BPL, Yuce MR, et 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.
Wong C, Zhang Z, Lo B, et al., 2014, Markerless motion capture using appearance and inertial data, Pages: 6907-6910
Zheng Y-L, Ding X-R, Poon CCY, et al., 2014, Unobtrusive Sensing and Wearable Devices for Health Informatics, IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, Vol: 61, Pages: 1538-1554, ISSN: 0018-9294
Atallah L, Wiik A, Lo B, et al., 2014, Gait asymmetry detection in older adults using a light ear-worn sensor, PHYSIOLOGICAL MEASUREMENT, Vol: 35, Pages: N29-N40, ISSN: 0967-3334
Chen CM, Kwasnicki R, Lo B, et 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
Jarchi D, Lo B, Ieong E, et 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
Li L, Atallah L, Lo B, et 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
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
Kirby GSJ, Kwasnicki RM, Hargrove C, et 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.
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