Imperial College London

DrRaviVaidyanathan

Faculty of EngineeringDepartment of Mechanical Engineering

Senior Lecturer in Bio-Mechatronics
 
 
 
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Contact

 

+44 (0)20 7594 7020r.vaidyanathan CV

 
 
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Location

 

717City and Guilds BuildingSouth Kensington Campus

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Summary

 

Publications

Publication Type
Year
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101 results found

Wilson S, Eberle H, Hayashi Y, Madgwick SOH, McGregor A, Jing X, Vaidyanathan Ret 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 (Ê»Madgwick’) 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.

Journal article

Woodward R, Stokes M, Shefelbine S, Vaidyanathan Ret 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.

Journal article

Lai J, Nowlan NC, Vaidyanathan R, Visser GHA, Lees CCet 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

Journal article

Formstone L, Pucek M, Wilson S, Bentley P, McGregor A, Vaidyanathan Ret 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

Conference paper

Needham APH, Paszkiewicz FP, Alias MFM, Wilson S, Dehghani-Sanij AA, Khoo BC, Vaidyanathan Ret 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

Conference paper

Russell F, Zhu Y, Hey W, Vaidyanathan R, Ellison Pet al., A biomimicking design for mechanical knee joints, Bioinspiration and Biomimetics, Vol: 13, ISSN: 1748-3182

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.

Journal article

Lai J, Woodward R, Alexandrov Y, Munnee QA, Lees CC, Vaidyanathan R, Nowlan NCet 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.

Journal article

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

Conference paper

Ma Y, Liu Y, Fin R, Yuan X, Sekha R, Wilson S, Vaidyanathan Ret 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

Conference paper

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

Conference paper

Russell F, Gao L, Ellison P, Vaidyanathan Ret 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.

Conference paper

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

Conference paper

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.

Journal article

Burridge JH, Lee ACW, Turk R, Stokes M, Whitall J, Vaidyanathan R, Clatworthy P, Hughes A-M, Meagher C, Franco E, Yardley Let 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

Journal article

Angeles P, Tai Y, Pavese N, Vaidyanathan Ret 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

Conference paper

Angeles P, Tai Y, Pavese N, Wilson S, vaidyanathan Ret 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

Conference paper

Lai J, Nowlan N, Vaidyanathan R, Shaw C, Lees Cet al., 2016, Fetal movements as a predictor of health, Acta Obstetricia et Gynecologica Scandinavica, Vol: 95, Pages: 968-975, ISSN: 1600-0412

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.

Journal article

Jameel ASMM, Mace M, Wang S, Vaidyanathan R, Al Mamun KAet 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

Conference paper

Angeles P, Mace M, Admiraal M, Burdet E, Pavese N, Vaidyanathan Ret 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

Conference paper

Low KH, Mohammed S, Hu T, Seipel J, Vaidyanathan R, Solis Jet al., 2015, Biorobotics with Hybrid and Multimodal Locomotion, IEEE ROBOTICS & AUTOMATION MAGAZINE, Vol: 22, Pages: 29-+, ISSN: 1070-9932

Journal article

Mamun KA, Mace M, Lutman ME, Stein J, Liu X, Aziz T, Vaidyanathan R, Wang Set 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

Journal article

Hallett E, Woodward R, Schultz SR, Vaidyanathan Ret 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.

Conference paper

Morad S, Ulbricht C, Harkin P, Chan J, Parker K, Vaidyanathan Ret 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

Conference paper

Gardner M, Vaidyanathan R, Burdet E, Khoo BCet 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

Conference paper

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

Conference paper

Mace M, Yousif N, Naushahi M, Abdullah-Al-Mamun K, Wang S, Nandi D, Vaidyanathan Ret 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

Journal article

Lock RJ, Burgess SC, Vaidyanathan R, 2014, Multi-modal locomotion: from animal to application, BIOINSPIRATION & BIOMIMETICS, Vol: 9, ISSN: 1748-3182

Journal article

Lock RJ, Vaidyanathan R, Burgess SC, 2014, Impact of Marine Locomotion Constraints on a Bio-inspired Aerial-Aquatic Wing: Experimental Performance Verification, JOURNAL OF MECHANISMS AND ROBOTICS-TRANSACTIONS OF THE ASME, Vol: 6, ISSN: 1942-4302

Journal article

Gardner M, Woodward R, Vaidyanathan R, Burdet E, Khoo BCet al., 2014, An Unobtrusive Vision System to Reduce the Cognitive Burden of Hand Prosthesis Control, 13th International Conference on Control Automation Robotics & Vision (ICARCV), Publisher: IEEE, Pages: 1279-1284, ISSN: 2474-2953

Conference paper

Evins R, Vaidyanathan R, Burgess S, 2014, Multi-material Compositional Pattern-Producing Networks for Form Optimisation, 17th European Conference on Applications of Evolutionary Computation (EvpApplications), Publisher: SPRINGER-VERLAG BERLIN, Pages: 189-200, ISSN: 0302-9743

Conference paper

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