127 results found
Fadhil A, Kanneganti R, Gupta L, et al., 2019, Fusion of enhanced and synthetic vision system images for runway and horizon detection, Sensors, Vol: 19, Pages: 1-17, ISSN: 1424-8220
Networked operation of unmanned air vehicles (UAVs) demands fusion of information from disparate sources for accurate flight control. In this investigation, a novel sensor fusion architecture for detecting aircraft runway and horizons as well as enhancing the awareness of surrounding terrain is introduced based on fusion of enhanced vision system (EVS) and synthetic vision system (SVS) images. EVS and SVS image fusion has yet to be implemented in real-world situations due to signal misalignment. We address this through a registration step to align EVS and SVS images. Four fusion rules combining discrete wavelet transform (DWT) sub-bands are formulated, implemented, and evaluated. The resulting procedure is tested on real EVS-SVS image pairs and pairs containing simulated turbulence. Evaluations reveal that runways and horizons can be detected accurately even in poor visibility. Furthermore, it is demonstrated that different aspects of EVS and SVS images can be emphasized by using different DWT fusion rules. The procedure is autonomous throughout landing, irrespective of weather. The fusion architecture developed in this study holds promise for incorporation into manned heads-up displays (HUDs) and UAV remote displays to assist pilots landing aircraft in poor lighting and varying weather. The algorithm also provides a basis for rule selection in other signal fusion applications.
Wilson S, Eberle H, Hayashi Y, et al., 2019, Formulation of a new gradient descent MARG orientation algorithm: Case study on robot teleoperation, Mechanical Systems and Signal Processing, Vol: 130, Pages: 183-200, ISSN: 0888-3270
We introduce a novel magnetic angular rate gravity (MARG) sensor fusion algorithm for inertial measurement. The new algorithm improves the popular gradient descent (ʻMadgwick’) algorithm increasing accuracy and robustness while preserving computational efficiency. Analytic and experimental results demonstrate faster convergence for multiple variations of the algorithm through changing magnetic inclination. Furthermore, decoupling of magnetic field variance from roll and pitch estimation is proven for enhanced robustness. The algorithm is validated in a human-machine interface (HMI) case study. The case study involves hardware implementation for wearable robot teleoperation in both Virtual Reality (VR) and in real-time on a 14 degree-of-freedom (DoF) humanoid robot. The experiment fuses inertial (movement) and mechanomyography (MMG) muscle sensing to control robot arm movement and grasp simultaneously, demonstrating algorithm efficacy and capacity to interface with other physiological sensors. To our knowledge, this is the first such formulation and the first fusion of inertial measurement and MMG in HMI. We believe the new algorithm holds the potential to impact a very wide range of inertial measurement applications where full orientation necessary. Physiological sensor synthesis and hardware interface further provides a foundation for robotic teleoperation systems with necessary robustness for use in the field.
Woodward R, Stokes M, Shefelbine S, et al., 2019, Segmenting mechanomyography measures of muscle activity phases using inertial data, Scientific Reports, Vol: 9, ISSN: 2045-2322
Electromyography (EMG) is the standard technology for monitoring muscle activity in laboratory environments, either using surface electrodes or fine wire electrodes inserted into the muscle. Due to limitations such as cost, complexity, and technical factors, including skin impedance with surface EMG and the invasive nature of fine wire electrodes, EMG is impractical for use outside of a laboratory environment. Mechanomyography (MMG) is an alternative to EMG, which shows promise in pervasive applications. The present study used an exerting squat-based task to induce muscle fatigue. MMG and EMG amplitude and frequency were compared before, during, and after the squatting task. Combining MMG with inertial measurement unit (IMU) data enabled segmentation of muscle activity at specific points: entering, holding, and exiting the squat. Results show MMG measures of muscle activity were similar to EMG in timing, duration, and magnitude during the fatigue task. The size, cost, unobtrusive nature, and usability of the MMG/IMU technology used, paired with the similar results compared to EMG, suggest that such a system could be suitable in uncontrolled natural environments such as within the home.
Formstone L, Pucek M, Wilson S, et al., 2019, Myographic Information Enables Hand Function Classification in Automated Fugl-Meyer Assessment, 9th IEEE/EMBS International Conference on Neural Engineering (NER), Publisher: IEEE, Pages: 239-242, ISSN: 1948-3546
Lai J, Nowlan NC, Vaidyanathan R, et al., 2019, The use of actograph in the assessment of fetal well-being, Journal of Maternal-Fetal and Neonatal Medicine, Pages: 1-6, ISSN: 1476-4954
PURPOSE: Third trimester maternal perception of fetal movements is often used to assess fetal well-being. However, its true clinical value is unknown, primarily because of the variability in subjective quantification. The actograph, a technology available on most cardiotocograph machines, quantifies movements, but has never previously been investigated in relation to fetal health and existing monitoring devices. The objective of this study was to quantify actograph output in healthy third trimester pregnancies and investigate this in relation to other methods of assessing fetal well-being. METHODS: Forty-two women between 24 and 34 weeks of gestation underwent ultrasound scan followed by a computerized cardiotocograph (CTG). Post capture analysis of the actograph recording was performed and expressed as a percentage of activity over time. The actograph output results were analyzed in relation to Doppler, ultrasound and CTG findings expressed as z-score normalized for gestation. RESULTS: There was a significant association between actograph output recording and estimated fetal weight Z-score (R = 0.546, p ≤ .005). This activity was not related to estimated fetal weight. Increased actograph activity was negatively correlated with umbilical artery pulsatility index Z-score (R = -0.306, p = .049) and middle cerebral artery pulsatility index Z-score (R = -0.390, p = .011). CONCLUSION: Fetal movements assessed by the actograph are associated both with fetal size in relation to gestation and fetoplacental Doppler parameters. It is not the case that larger babies move more, however, as the relationship with actograph output related only to estimated fetal weight z-score. These findings suggest a plausible link between the frequency of fetal movements and established markers of fetal health. RATIONALE The objective of this study was to quantify actograph output in healthy third trimester pregnancies and investigate this in relation to other methods of assess
Russell F, Vaidyanathan R, Ellison P, 2018, A kinematic model for the design of a bicondylar mechanical knee, 2018 7th IEEE International Conference on Biomedical Robotics and Biomechatronics (Biorob), Publisher: IEEE, Pages: 750-755
In this paper we present a design methodology for a bicondylar joint that mimics many of the physical mechanisms in the human knee. We replicate the elastic ligaments and sliding and rolling joint surfaces. As a result the centre of rotation and moment arm from the quadriceps changes as a function of flexion angle in a similar way to the human knee. This leads to a larger moment arm in the centre of motion, where it is most needed for high load tasks, and a smaller moment arm at the extremes, reducing the required actuator displacement. This is anticipated to improve performance:weight ratio in legged devices for tasks such as stair accent and sit-to-stand. In the design process ligament attachment positions, femur profile and ligament lengths were taken from cadaver studies. This information was then used as inputs to a simplified kinematic computer model in order to design a valid profile for a tibial condyle. A physical model was then tested on a custom built squatting robot. It was found that although ligament lengths deviated from the designed values the robot moment arm still matched the model to within 6.1% on average. This shows that the simplified model is an effective design tool for this type of joint. It is anticipated that this design, when employed in walking robots, prostheses or exoskeletons, will improve the high load task capability of these devices. In this paper we have outlined and validated a design method to begin to achieve this goal.
Caulcrick C, Russell F, Wilson S, et al., 2018, Unilateral Inertial and Muscle Activity Sensor Fusion for Gait Cycle Progress Estimation, Pages: 1151-1156, ISSN: 2155-1774
© 2018 IEEE. This paper introduces a method which uses feedforward neural networks (FNNs) for estimating gait cycle progress using data recorded from inertial and muscle activity sensors attached to one side of the lower body. Three-axis inertial measurement unit (IMU) readings from accelerometers and gyroscopes located above the outer ankle and knee were fused with mechanomyogram (MMG) sensor readings from across major muscle groups on the left leg. Validation was against ground truth gathered concurrently with VICON motion capture. The performance was characterised by rms error (Erms) and max error (Emax), averaged across four cross-validated trials, and enhanced by adjusting number of sliding window frames and hidden layer neurons. The final configuration estimated gait cycle progress with Erms of 1.6% and Emax of 6.8%. This demonstrates promise for such a method to be used for control of unilateral robotic prostheses and exoskeletons, providing state estimation of gait progress from low power sensors limited to one side of the lower body.
Needham APH, Paszkiewicz FP, Alias MFM, et al., 2018, Subject-independent data pooling in classification of gait intent using mechanomyography on a transtibial amputee, IEEE International Conference on Robotics and Automation (ICRA), Publisher: IEEE COMPUTER SOC, Pages: 1806-1811, ISSN: 1050-4729
In this paper we present a new bioinspired bicondylar knee joint that requires a smaller actuator size when compared to a constant moment arm joint. Unlike existing prosthetic joints, the proposed mechanism replicates the elastic, rolling and sliding elements of the human knee. As a result, the moment arm that the actuators can impart on the joint changes as function of the angle, producing the equivalent of a variable transmission. By employing a similar moment arm—angle profile as the human knee the peak actuator force for stair ascent can be reduced by 12% compared to a constant moment arm joint addressing critical impediments in weight and power for robotics limbs. Additionally, the knee employs mechanical 'ligaments' containing stretch sensors to replicate the neurosensory and compliant elements of the joint. We demonstrate experimentally how the ligament stretch can be used to estimate joint angle, therefore overcoming the difficulty of sensing position in a bicondylar joint.
Lai J, Woodward R, Alexandrov Y, et al., 2018, Performance of a wearable acoustic system for fetal movement discrimination, PLoS ONE, Vol: 13, ISSN: 1932-6203
Fetal movements (FM) are a key factor in clinical management of high-risk pregnancies such as fetal growth restriction. While maternal perception of reduced FM can trigger self-referral to obstetric services, maternal sensation is highly subjective. Objective, reliable monitoring of fetal movement patterns outside clinical environs is not currently possible. A wearable and non-transmitting system capable of sensing fetal movements over extended periods of time would be extremely valuable, not only for monitoring individual fetal health, but also for establishing normal levels of movement in the population at large. Wearable monitors based on accelerometers have previously been proposed as a means of tracking FM, but such systems have difficulty separating maternal and fetal activity and have not matured to the level of clinical use. We introduce a new wearable system based on a novel combination of accelerometers and bespoke acoustic sensors as well as an advanced signal processing architecture to identify and discriminate between types of fetal movements. We validate the system with concurrent ultrasound tests on a cohort of 44 pregnant women and demonstrate that the garment is capable of both detecting and discriminating the vigorous, whole-body ‘startle’ movements of a fetus. These results demonstrate the promise of multimodal sensing for the development of a low-cost, non-transmitting wearable monitor for fetal movements.
Admiraal M, Wilson S, Vaidyanathan R, 2017, Improved Formulation of the IMU and MARG Orientation Gradient Descent Algorithm for Motion Tracking in Human-Machine Interfaces, IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI), Publisher: IEEE, Pages: 403-410
Ma Y, Liu Y, Fin R, et al., 2017, Hand Gesture Recognition with Convolutional Neural Networks for the Multimodal UAV Control, Workshop on Research, Education and Development of Unmanned Aerial Systems (RED-UAS), Publisher: IEEE, Pages: 198-203
Martineau T, Vaidyanathan R, 2017, Studying the Implementation of Iterative Impedance Control for Assistive Hand Rehabilitation using an Exoskeleton, International Conference on Rehabilitation Robotics (ICORR), Publisher: IEEE, Pages: 1500-1505, ISSN: 1945-7898
Russell F, Gao L, Ellison P, et al., 2017, Challenges in using Compliant Ligaments for Position Estimation within Robotic Joints, 2017 International Conference on Rehabilitation Robotics (ICORR), Publisher: IEEE, ISSN: 1945-7901
The mechanical advantages of bio-inspired condylar robotic knee joints for use in prosthetics or rehabilitation has been argued extensively in literature. A common limitation of these designs is the difficulty of estimating joint angle and therefore accurately controlling the joint. Furthermore, the potential role of ligament-like structures in robotic knees is not very well established. In this work, we investigate the role of compliant stretch sensing ligaments and their integration into a condylar robotic knee. Simulations and experiments are executed out in order to establish whether measurement of stretch in these structures can be used to produce a new feedback controller for joint position. We report results from a computer model, as well as the design and construction of a robotic knee that show, for a chosen condyle shape, ligament stretch is a function of muscle force and joint velocity as well as joint angle. We have developed a genetic algorithm optimised controller incorporating ligament feedback that demonstrates improved performance for a desired joint angle in response to step inputs. The controller showed marginal improvement in response to a cyclic command signal and further investigation is required in order to use these measurements in robust control, nevertheless we believe these results demonstrate the that ligament-like structures have the potential to improve the performance of robotic knees for prosthetics and rehabilitation devices.
Wilson S, Vaidyanathan R, 2017, Upper-Limb Prosthetic Control using Wearable Multichannel Mechanomyography, International Conference on Rehabilitation Robotics (ICORR), Publisher: IEEE, Pages: 1293-1298, ISSN: 1945-7898
Woodward R, Shefelbine S, Vaidyanathan R, 2017, Pervasive monitoring of motion and muscle activation: inertial and mechanomyography fusion, IEEE/ASME Transactions on Mechatronics, Vol: 22, Pages: 2022-2033, ISSN: 1083-4435
Muscle activity and human motion are useful pa-rameters to map the diagnosis, treatment, and rehabilitation ofneurological and movement disorders. In laboratory and clinicalenvironments, electromyography (EMG) and motion capturesystems enable the collection of accurate, high resolution data onhuman movement and corresponding muscle activity. However,controlled surroundings limit both the length of time and thebreadth of activities that can be measured. Features of movement,critical to understanding patient progress, can change duringthe course of a day and daily activities may not correlate to thelimited motions examined in a laboratory. We introduce a systemto measure motion and muscle activity simultaneously over thecourse of a day in an uncontrolled environment with minimalpreparation time and ease of implementation that enables dailyusage. Our system combines a bespoke inertial measurement unit(IMU) and mechanomyography (MMG) sensor, which measuresthe mechanical signal of muscular activity. The IMU can collectdata continuously, and transmit wirelessly, for up to 10 hours.We describe the hardware design and validation and outline thedata analysis (including data processing and activity classificationalgorithms) for the sensing system. Furthermore, we presenttwo pilot studies to demonstrate utility of the system, includingactivity identification in six able-bodied subjects with an accuracyof 98%, and monitoring motion/muscle changes in a subjectwith cerebral palsy and of a single leg amputee over extendedperiods (∼5 hours). We believe these results provide a foundationfor mapping human muscle activity and corresponding motionchanges over time, providing a basis for a range of novelrehabilitation therapies.
Burridge JH, Lee ACW, Turk R, et al., 2017, Telehealth, Wearable Sensors, and the Internet: Will They Improve Stroke Outcomes Through Increased Intensity of Therapy, Motivation, and Adherence to Rehabilitation Programs?, JOURNAL OF NEUROLOGIC PHYSICAL THERAPY, Vol: 41, Pages: S32-S38, ISSN: 1557-0576
Angeles P, Tai Y, Pavese N, et al., 2017, Assessing Parkinson's disease motor symptoms using supervised learning algorithms, 21st International Congress of Parkinson's Disease and Movement Disorders, Publisher: WILEY, ISSN: 0885-3185
Angeles P, Tai Y, Pavese N, et al., Automated assessment of symptom severity changes during deep brain stimulation (DBS) therapy for Parkinson's disease., Publisher: Institute of Electrical and Electronics Engineers Inc., ISSN: 1945-7901
Wilson S, Vaidyanathan R, 2017, Gesture recognition through classification of acoustic muscle sensing for prosthetic control, Pages: 637-642, ISSN: 0302-9743
© Springer International Publishing AG 2017. In this paper we present the initial evaluation of a new upper limb prosthetic control system to be worn on the residual limb, which is capable of identifying hand gestures through muscle acoustic signatures (mechanomyography, or MMG) measured from the upper arm. We report the development of a complete system consisting of a bespoke inertial measurement unit (IMU) to monitor arm motion and a skin surface sensor capturing acoustic muscle activity associated with digit movement. The system fuses the orientation of the arm with the synchronized output of six MMG sensors, which capture the low frequency vibrations produced during muscle contraction, to determine which hand gesture the user is making. Twelve gestures split into two test categories were examined, achieving a preliminary average accuracy of 89% on the offline examination, and 68% in the real time tests.
The key determinant to a fetus maintaining its health is through adequate perfusion and oxygen transfer mediated by the functioning placenta. When this equilibrium is distorted, a number of physiological changes including reduced fetal growth occur to favour survival. Technologies have been developed to monitor these changes with a view to prolong intrauterine maturity whilst reducing the risks of stillbirth. Many of these strategies involve complex interpretation, for example Doppler ultrasound for fetal blood flow and computerisedcomputerized analysis of fetal heart rate changes. However, even with these modalities of fetal assessment to determine the optimal timing of delivery, fetal movements remain integral to clinical decision making. In high risk cohorts with fetal growth restriction, the manifestation of a reduction in perceived movements may warrant an expedited delivery. Despite this, there remains has been little evolution in the development of technologies to objectively define evaluate normal fetal movement behavior for behavior, and where there has, there has been no linkage to clinical useapplication. In tThis review we is an attempt to understand synthesize currently available literature on the value of fetal movement analysis as a method of assessing fetal wellbeing, and show how interdisciplinary developments in this area may aid in improvements to clinical outcomes.
Jameel ASMM, Mace M, Wang S, et al., 2016, Predicting Movement and Laterality From Deep Brain Local Field Potentials, 1st International Conference on Medical Engineering, Health Informatics and Technology (MediTec), Publisher: IEEE
Angeles P, Mace M, Admiraal M, et al., 2016, A Wearable Automated System to Quantify Parkinsonian Symptoms Enabling Closed Loop Deep Brain Stimulation, 17th Annual Conference on Towards Autonomous Robotic Systems (TAROS), Publisher: SPRINGER INT PUBLISHING AG, Pages: 8-19, ISSN: 0302-9743
Mamun KA, Mace M, Lutman ME, et al., 2015, Movement decoding using neural synchronisation and inter-hemispheric connectivity from deep brain local field potentials, Journal of Neural Engineering, Vol: 12, ISSN: 1741-2560
Objective. Correlating electrical activity within the human brain to movement is essential for developing and refining interventions (e.g. deep brain stimulation (DBS)) to treat central nervous system disorders. It also serves as a basis for next generation brain–machine interfaces (BMIs). This study highlights a new decoding strategy for capturing movement and its corresponding laterality from deep brain local field potentials (LFPs). Approach. LFPs were recorded with surgically implanted electrodes from the subthalamic nucleus or globus pallidus interna in twelve patients with Parkinson's disease or dystonia during a visually cued finger-clicking task. We introduce a method to extract frequency dependent neural synchronization and inter-hemispheric connectivity features based upon wavelet packet transform (WPT) and Granger causality approaches. A novel weighted sequential feature selection algorithm has been developed to select optimal feature subsets through a feature contribution measure. This is particularly useful when faced with limited trials of high dimensionality data as it enables estimation of feature importance during the decoding process. Main results. This novel approach was able to accurately and informatively decode movement related behaviours from the recorded LFP activity. An average accuracy of 99.8% was achieved for movement identification, whilst subsequent laterality classification was 81.5%. Feature contribution analysis highlighted stronger contralateral causal driving between the basal ganglia hemispheres compared to ipsilateral driving, with causality measures considerably improving laterality discrimination. Significance. These findings demonstrate optimally selected neural synchronization alongside causality measures related to inter-hemispheric connectivity can provide an effective control signal for augmenting adaptive BMIs. In the case of DBS patients, acquiring such signals requires no additional surgery whilst providing a rela
Hallett E, Woodward R, Schultz SR, et al., 2015, Rapid bicycle gear switching based on physiological cues, IEEE CASE 2015, Publisher: IEEE, Pages: 377-382
This paper discusses the merits of Mechanomyography (MMG) sensors in capturing and isolating muscle activity in high interference environs, with application to `hands free' gear shifting on a bicycle for users with limited extremity movement. MMG (acoustic) muscle sensing provides a simple and rugged alternative to physiological sensing for machine interface in the field, but suffers from interfering artifacts (in particular motion) which has limited its mainstream use. We introduce a system fusing MMG with a filter based on Inertial Measurement (IMU) to isolate muscle activity in the presence of interfering motion and vibrations. The system identifies user-initiated muscle trigger profiles during laboratory testing, allowing parameterization of MMG and IMU signals to identify purposeful muscle contractions (triggers) and to omit false triggers resulting from cycle/road vibration or rider movement. During laboratory testing the success rate of trigger identification was 88.5% while cycling with an average of 0.87 false triggers /min. During road testing the success rate was 72.5% and false triggers were more frequent at 3.7 /min. These results hold strong promise for alternative triggering mechanisms to the standard bar-end shifters used in current off-the-shelf cycling group sets, enabling amputees or people of reduced arm or hand dexterity to change gears while riding. Further testing will explore the use of signal filters on MMG data and further use of IMU data as feedback to increase false triggers rejection. Wider applications include a broad range of machine-interaction research.
Woodward R, Shefelbine S, Vaidyanathan R, 2015, Integrated Grip Switching and Grasp Control for Prosthetic Hands Using Fused Inertial and Mechanomyography Measurement, Swarm/Human Blended Intelligence Workshop (SHBI 2015), Publisher: IEEE
Morad S, Ulbricht C, Harkin P, et al., 2015, Modelling and control of a water jet cutting probe for flexible surgical robot, IEEE International Conference on Automation Science and Engineering (CASE), Publisher: IEEE, Pages: 1159-1164, ISSN: 2161-8070
Gardner M, Vaidyanathan R, Burdet E, et al., 2015, Motion-based Grasp Selection: Improving Traditional Control Strategies of Myoelectric Hand Prosthesis, 14th IEEE/RAS-EMBS International Conference on Rehabilitation Robotics (ICORR), Publisher: IEEE, Pages: 307-312, ISSN: 1945-7898
Mace M, Yousif N, Naushahi M, et al., 2014, An automated approach towards detecting complex behaviours in deep brain oscillations, JOURNAL OF NEUROSCIENCE METHODS, Vol: 224, Pages: 66-78, ISSN: 0165-0270
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