Kevin Thomas SweeneyPMD Solutions
· Post Doctoral Research Fellow
Currently employed as a postdoctoral research fellow in University College Dublin. The project examines the use of IMU sensors to classify the movement of the lower limb during rehabilitation exercises following both hip and knee replacements. A second project examines data collected from professional rugby players and looks to accurately classify the various epochs of play during a game.
Skills and Expertise
Research Items (22)
Background: The drop vertical jump (DVJ) task has previously been used to identify movement patterns associated with a number of injury types. However, no current research exists evaluating participants with chronic ankle instability (CAI) compared to lateral ankle sprain (LAS) copers during this task. Objective: This study aims to identify the coping movement and motor control patterns of LAS copers in comparison to individuals with CAI during a DVJ task. Design: Case-control study METHODS: Seventy individuals were recruited at convenience within 2-weeks of sustaining a first-time acute LAS injury. One year following recruitment these individuals were stratified into two groups: twenty-eight with CAI and forty-two LAS copers. They attended the testing laboratory to complete a DVJ task. 3D kinematic and sagittal plane kinetic profiles were plotted for the lower extremity joints of both limbs for the drop jump (phase 1) and drop landing (phase 2) phases of the DVJ. The rate of impact modulation relative to bodyweight (BW) during both phases of the DVJ was also determined. Results: Compared with LAS copers, CAI participants displayed significant increases in hip flexion on their 'involved' limb during phase 1 of the DVJ (23° vs 18°), and bilaterally during phase 2 (15° vs 10°). These movement patterns coincided with altered moment-of-force patterns at the hip on the 'uninvolved' limb. Limitations: It is unknown whether these movement and motor control patterns preceded or occurred as a result of the initial LAS. Conclusions: Participants with CAI display hip-centred changes in movement and motor control patterns during a DVJ task compared to LAS copers. These findings may give an indication of the coping mechanism underlying outcome following initial LAS.
To evaluate the adaptive movement and motor control patterns of a group with a 6-month history of first-time lateral ankle sprain (LAS) injury during a drop vertical jump (DVJ) task. Fifty-one participants with a 6-month history of first-time acute LAS injury and twenty controls performed a DVJ task. 3D kinematic and sagittal plane kinetic profiles were plotted for the lower extremity joints of both limbs for the drop jump (phase 1) and drop landing (phase 2) phases of the DVJ. Inter-limb symmetry and the rate of impact modulation (RIM) relative to bodyweight (BW) during both phases of the DVJ were also determined. LAS participants displayed bilateral increases in knee flexion and an increase in inversion during phases 1 and 2, respectively. They also displayed reduced ankle plantar-flexion on their injured limb during both phases of the DVJ (p < 0.05); increased inter-limb asymmetry of RIM was noted for both phases of the DVJ, while the moment-of-force profile exhibited bilaterally greater hip extensor dominance during phase 1. Participants with a 6-month history of LAS display some movement patterns consistent with those observed in chronic ankle instability populations during similar tasks. This article is protected by copyright. All rights reserved. This article is protected by copyright. All rights reserved.
This investigation combined measures of inter-joint coordination and stabilometry to evaluate eyes-open (condition 1) and eyes-closed (condition 2) static unilateral stance performance in a group of participants with an acute, first-time lateral ankle sprain injury in comparison to a control group. Sixty-six participants with an acute first-time lateral ankle sprain and 19 non-injured controls completed three 20-second unilateral stance task trials in conditions 1 and 2. An adjusted coefficient of multiple determination statistic was used to compare stance limb 3-D kinematic data for similarity in the aim of establishing patterns of inter-joint coordination for these groups. Between-group analyses revealed significant differences in stance limb inter-joint coordination strategies for conditions 1 and 2. Injured participants displayed increases in ankle-hip linked coordination compared to controls in condition 1 (sagittal/frontal plane: 0.12 [0.09] vs 0.06 [0.04]; η(2)=.16) and condition 2 (sagittal/frontal plane: 0.18 [0.13] vs 0.08 [0.06]; η(2)=0.37). Participants with acute first-time lateral ankle sprain exhibit a hip-dominant coordination strategy for static unilateral stance compared to non-injured controls. Copyright © 2015 Elsevier Ltd. All rights reserved.
Longitudinal analyses of participants with a history of lateral ankle sprain are lacking. This investigation combined measures of lower limb inter-joint coordination and stabilometry to evaluate static unipedal stance with eyes-open (condition 1) and eyes-closed (condition 2) in a group of participants with chronic ankle instability compared to ankle sprain 'copers' (both recruited 12-months after sustaining an acute first-time lateral ankle sprain) and a group of non-injured controls. Twenty-eight participants with chronic ankle instability, forty-two lateral ankle sprain 'copers' and twenty non-injured controls completed three 20-second single-limb stance trials in conditions 1 and 2. An adjusted coefficient of multiple determination statistic was used to compare stance limb 3-dimensional kinematic data for similarity in the aim of establishing patterns of inter-joint coordination. The fractal dimension of the stance limb center of pressure path was also calculated. Between-group analyses revealed that participants with chronic ankle instability displayed notable increases in ankle-hip linked coordination compared to both 'copers' (0.52 [1.05] vs -0.28 [0.9] p = 0.007) and controls (0.52 [1.05] vs -0.63 [0.64] p = 0.006) in condition 1 and to controls (0.62 [1.92] vs 0.1 [1.0]) in condition 2. Participants with chronic ankle instability also exhibited a decrease in the fractal dimension of the center-of-pressure path during condition 2 compared to both controls and 'copers'. Participants with chronic ankle instability present with a hip-dominant strategy of eyes-open and eyes-closed static unipedal stance. This coincided with reduced complexity of the stance-limb center of pressure path in the eyes-closed condition.
Longitudinal analyses of participants with a history of lateral ankle sprain are lacking. This investigation combined measures of inter-joint coordination and stabilometry to evaluate eyes-open (condition 1) and eyes-closed (condition 2) static unilateral stance performance in a group of participants, 6-months after they sustained an acute, first-time lateral ankle sprain in comparison to a control group. Sixty-nine participants with a 6-month history of first-time lateral ankle sprain and 20 non-injured controls completed three 20-second unilateral stance task trials in conditions 1 and 2. An adjusted coefficient of multiple determination statistic was used to compare stance limb 3-dimensional kinematic data for similarity in the aim of establishing patterns of lower-limb inter-joint coordination. The fractal dimension of the stance limb centre of pressure path was also calculated. Between-group analyses revealed significant differences in stance limb inter-joint coordination strategies for conditions 1 and 2, and in the fractal dimension of the centre-of-pressure path for condition 2 only. Injured participants displayed increases in ankle-hip linked coordination compared to controls in condition 1 (sagittal/frontal plane: 0.15 [0.14] vs 0.06 [0.04]; η(2)=.19; sagittal/transverse plane: 0.14 [0.11] vs 0.09 [0.05]; η(2)=0.14) and condition 2 (sagittal/frontal plane: 0.15 [0.12] vs 0.08 [0.06]; η(2)=0.23), with an associated decrease in the fractal dimension of the centre-of-pressure path (injured limb: 1.23 [0.13] vs 1.36 [0.13]; η(2)=0.20). Participants with a 6-month history of first-time lateral ankle sprain exhibit a hip-dominant coordination strategy for static unilateral stance compared to non-injured controls. Copyright © 2014 Elsevier Ltd. All rights reserved.
Accurate assessments of adherence and exercise performance are required in order to ensure that patients adhere to and perform their rehabilitation exercises correctly within the home environment. Inertial sensors have previously been advocated as a means of achieving these requirements, by using them as an input to an exercise biofeedback system. This research sought to investigate whether inertial sensors, and in particular a single sensor, can accurately classify exercise performance in patients performing lower limb exercises for rehabilitation purposes. Fifty-eight participants (19 male, 39 female, age: 53.9 +/- 8.5 years, height: 1.69 +/- 0.08 m, weight: 74.3 +/- 13.0 kg) performed ten repetitions of seven lower limb exercises (hip abduction, hip flexion, hip extension, knee extension, heel slide, straight leg raise, and inner range quadriceps). Three inertial sensor units, secured to the thigh, shin and foot of the leg being exercised, were used to acquire data during each exercise. Machine learning classification methods were applied to quantify the acquired data. The classification methods achieved relatively high accuracy at distinguishing between correct and incorrect performance of an exercise using three, two, or one sensor while moderate efficacy scores were also achieved by the classifier when attempting to classify the particular error in exercise performance. Results also illustrated that a reduction in the number of inertial sensor units employed has little effect on the overall efficacy results. The results revealed that it is possible to classify lower limb exercise performance using inertial sensors with satisfactory levels of accuracy and reducing the number of sensors employed does not reduce the accuracy of the method.
Purpose: Evaluate the potentially adaptive movement patterns associated with acute lateral ankle sprain (LAS) using biomechanical analyzes. Methods: Thirty participants with acute LAS and nineteen controls performed a drop vertical jump (DVJ) task. 3D kinematic and sagittal plane kinetic profiles were plotted for the hip, knee and ankle joints of both limbs for the drop jump (phase 1) and drop landing (phase 2) phases of the DVJ. Inter-limb symmetry and the rate of force development (RFD) relative to bodyweight (BW) during both phases of the DVJ were also determined. Results: The LAS group displayed reduced ankle plantar-flexion on their injured limb during phase 2 of the DVJ, with greater associated inter-limb asymmetry for this movement (p<.05). The LAS group also displayed altered kinetic profiles, with increased inter-limb hip asymmetry for both phases of the DVJ (p<.05). This was associated with a decrease in the LAS participants' injured limb RFD during phase 2 of the DVJ when compared with that of controls (11.76±3.43BW/s vs 14.60±3.20BW/s; p=.01, η(2)=0.14). Conclusion: Participants with LAS display potentially aberrant coordination strategies during a DVJ as evidenced by an increased dependence on the non-injured limb.
The benefits of exercise in rehabilitation after orthopaedic surgery or following a musculoskeletal injury has been widely established. Within a hospital or clinical environment, adherence levels to rehabilitation exercise programs are high due to the supervision of the patient during the rehabilitation process. However, adherence levels drop significantly when patients are asked to perform the program at home. This paper describes the use of simple inertial sensors for the purpose of developing a biofeedback system to monitor adherence to rehabilitation programs. The results show that a single sensor can accurately distinguish between seven commonly prescribed rehabilitation exercises with accuracies between 93% and 95%. Results also show that the use of multiple sensor units does not significantly improve results therefore suggesting that a single sensor unit can be used as an input to an exercise biofeedback system.
The use of inertial sensors to characterize pathological gait has traditionally been based on the calculation of temporal and spatial gait variables from inertial sensor data. This approach has proved successful in the identification of gait deviations in populations where substantial differences from normal gait patterns exist; such as in Parkinsonian gait. However, it is not currently clear if this approach could identify more subtle gait deviations, such as those associated with musculoskeletal injury. This study investigates whether additional analysis of inertial sensor data, based on quantification of gyroscope features of interest, would provide further discriminant capability in this regard. The tested cohort consisted of a group of anterior cruciate ligament reconstructed (ACL-R) females and a group of non-injured female controls, each performed ten walking trials. Gait performance was measured simultaneously using inertial sensors and an optoelectronic marker based system. The ACL-R group displayed kinematic and kinetic deviations from the control group, but no temporal or spatial deviations. This study demonstrates that quantification of gyroscope features can successfully identify changes associated with ACL-R gait, which was not possible using spatial or temporal variables. This finding may also have a role in other clinical applications where small gait deviations exist.
Observation of a patient's respiration signal can provide a clinician with the required information necessary to analyse a subject's wellbeing. Due to an increase in population number and the aging population demographic there is an increasing stress being placed on current healthcare systems. There is therefore a requirement for more of the rudimentary patient testing to be performed outside of the hospital environment. However due to the ambulatory nature of these recordings there is also a desire for a reduction in the number of sensors required to perform the required recording in order to be unobtrusive to the wearer, and also to use textile based systems for comfort. The extraction of a proxy for the respiration signal from a recorded electrocardiogram (ECG) signal has therefore received considerable interest from previous researchers. To allow for accurate measurements, currently employed methods rely on the availability of a clean artifact free ECG signal from which to extract the desired respiration signal. However, ambulatory recordings, made outside of the hospital-centric environment, are often corrupted with contaminating artifacts, the most degrading of which are due to subject motion. This paper presents the use of the ensemble empirical mode decomposition (EEMD) algorithm to aid in the extraction of the desired respiration signal. Two separate techniques are examined; 1) Extraction of the respiration signal directly from the noisy ECG 2) Removal of the artifact components relating to the subject movement allowing for the use of currently available respiration signal detection techniques. Results presented illustrate that the two proposed techniques provide significant improvements in the accuracy of the breaths per minute (BPM) metric when compared to the available true respiration signal. The error reduced from ± 5.9 BPM prior to the use of the two techniques to ± 2.9 and ± 3.3 BPM post processing using the EEMD algorithm techniques.
Sleep apnea is a common sleep disorder in which patient sleep patterns are disrupted due to recurrent pauses in breathing or by instances of abnormally low breathing. Current gold standard tests for the detection of apnea events are costly and have the addition of long waiting times. This paper investigates the use of cheap and easy to use sensors for the identification of sleep apnea events. Combinations of respiration, electrocardiography (ECG) and acceleration signals were analysed. Results show that using features, formed using the discrete wavelet transform (DWT), from the ECG and acceleration signals provided the highest classification accuracy, with an F1 score of 0.914. However, the novel employment of just the accelerometer signal during classification provided a comparable F1 score of 0.879. By employing one or a combination of the analysed sensors a preliminary test for sleep apnea, prior to the requirement for gold standard testing, can be performed.
The combination of reducing birth rate and increasing life expectancy continues to drive the demographic shift toward an ageing population and this is placing an ever-increasing burden on our healthcare systems. The urgent need to address this so called healthcare \time bomb" has led to a rapid growth in research into ubiquitous, pervasive and distributed healthcare technologies where recent advances in signal acquisition, data storage and communication are helping such systems become a reality. However, similar to recordings performed in the hospital environment, artifacts continue to be a major issue for these systems. The magnitude and frequency of artifacts can vary signi�cantly depending on the recording environment with one of the major contributions due to the motion of the subject or the recording transducer. As such, this thesis addresses the challenges of the removal of this motion artifact removal from various physiological signals. The preliminary investigations focus on artifact identi�cation and the tagging of physiological signals streams with measures of signal quality. A new method for quantifying signal quality is developed based on the use of inexpensive accelerometers which facilitates the appropriate use of artifact processing methods as needed. These artifact processing methods are thoroughly examined as part of a comprehensive review of the most commonly applicable methods. This review forms the basis for the comparative studies subsequently presented. Then, a simple but novel experimental methodology for the comparison of artifact processing techniques is proposed, designed and tested for algorithm evaluation. The method is demonstrated to be highly e�ective for the type of artifact challenges common in a connected health setting, particularly those concerned with brain activity monitoring. This research primarily focuses on applying the techniques to functional near infrared spectroscopy (fNIRS) and electroencephalography (EEG) data due to their high susceptibility to contamination by subject motion related artifact. Using the novel experimental methodology, complemented with simulated data, a comprehensive comparison of a range of artifact processing methods is conducted, allowing the identi�cation of the set of the best performing methods. A novel artifact removal technique is also developed, namely ensemble empirical mode decomposition with canonical correlation analysis (EEMD-CCA), which provides the best results when applied on fNIRS data under particular conditions. Four of the best performing techniques were then tested on real ambulatory EEG data contaminated with movement artifacts comparable to those observed during in-home monitoring. It was determined that when analysing EEG data, the Wiener �lter is consistently the best performing artifact removal technique. However, when employing the fNIRS data, the best technique depends on a number of factors including: 1) the availability of a reference signal and 2) whether or not the form of the artifact is known. It is envisaged that the use of physiological signal monitoring for patient healthcare will grow signi�cantly over the next number of decades and it is hoped that this thesis will aid in the progression and development of artifact removal techniques capable of supporting this growth.
Biosignal measurement and processing is increasingly being deployed in ambulatory situations particularly in connected health applications. Such an environment dramatically increases the likelihood of artifacts which can occlude features of interest and reduce the quality of information available in the signal. If multichannel recordings are available for a given signal source then there are currently a considerable range of methods which can suppress or in some cases remove the distorting effect of such artifacts. There are however considerably fewer techniques available if only a single channel measurement is available and yet single channel measurements are important where minimal instrumentation complexity is required. This paper describes a novel artifact removal technique for use in such a context. The technique known as ensemble empirical mode decomposition with canonical correlation analysis (EEMD-CCA) is capable of operating on single channel measurements. The EEMD technique is first used to decompose the single channel signal into a multi-dimensional signal. The CCA technique is then employed to isolate the artifact components from the underlying signal using second order statistics. The new technique is tested against the currently available Wavelet denoising and EEMDICA techniques using both electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) data and is shown to produce significantly improved results.
Artifact removal from physiological signals is an essential component of the biosignal processing pipeline. The need for powerful and robust methods for this process has become particularly acute as healthcare technology deployment undergoes transition from the current hospital-centric setting toward a wearable and ubiquitous monitoring environment. Currently, determining the relative efficacy and performance of the multiple artifact removal techniques available on real world data can be problematic, due to incomplete information on the uncorrupted desired signal. The majority of techniques are presently evaluated using simulated data, and therefore, the quality of the conclusions is contingent on the fidelity of the model used. Consequently, in the biomedical signal processing community, there is considerable focus on the generation and validation of appropriate signal models for use in artifact suppression. Most approaches rely on mathematical models which capture suitable approximations to the signal dynamics or underlying physiology and, therefore, introduce some uncertainty to subsequent predictions of algorithm performance. This paper describes a more empirical approach to the modeling of the desired signal that we demonstrate for functional brain monitoring tasks which allows for the procurement of a "ground truth" signal which is highly correlated to a true desired signal that has been contaminated with artifacts. The availability of this "ground truth," together with the corrupted signal, can then aid in determining the efficacy of selected artifact removal techniques. A number of commonly implemented artifact removal techniques were evaluated using the described methodology to validate the proposed novel test platform.
The combination of reducing birth rate and increasing life expectancy continues to drive the demographic shift toward an aging population. This, in turn, places an ever-increasing burden on healthcare due to the increasing prevalence of patients with chronic illnesses and the reducing income-generating population base needed to sustain them. The need to urgently address this healthcare "time bomb" has accelerated the growth in ubiquitous, pervasive, distributed healthcare technologies. The current move from hospital-centric healthcare toward in-home health assessment is aimed at alleviating the burden on healthcare professionals, the health care system and caregivers. This shift will also further increase the comfort for the patient. Advances in signal acquisition, data storage and communication provide for the collection of reliable and useful in-home physiological data. Artifacts, arising from environmental, experimental and physiological factors, degrade signal quality and render the affected part of the signal useless. The magnitude and frequency of these artifacts significantly increases when data collection is moved from the clinic into the home. Signal processing advances have brought about significant improvement in artifact removal over the past few years. This paper reviews the physiological signals most likely to be recorded in the home, documenting the artifacts which occur most frequently and which have the largest degrading effect. A detailed analysis of current artifact removal techniques will then be presented. An evaluation of the advantages and disadvantages of each of the proposed artifact detection and removal techniques, with particular application to the personal healthcare domain, is provided.
fNIRS recordings are increasingly utilized to monitor brain activity in both clinical and connected health settings. These optical recordings provide a convenient measurement of cerebral hemodynamic changes which can be linked to motor and cognitive performance. Such measurements are of clinical utility in a broad range of conditions ranging from dementia to movement rehabilitation therapy. For such applications fNIRS is increasingly deployed outside the clinic for patient monitoring in the home. However, such a measurement environment is poorly controlled and motion, in particular, is a major source of artifacts in the signal, leading to poor signal quality for subsequent clinical interpretation. Artifact removal techniques are increasingly being employed with an aim of reducing the effect of the noise in the desired signal. Currently no methodology is available to accurately determine the efficacy of a given artifact removal technique due to the lack of a true reference for the uncontaminated signal. In this paper we propose a novel methodology for fNIRS data collection allowing for effective validation of artifact removal techniques. This methodology describes the use of two fNIRS channels in close proximity allowing them to sample the same measurement location; allowing for the introducing of motion artifact to only one channel while having the other free of contamination. Through use of this methodology, for each motion artifact epoch, a true reference for the uncontaminated signal becomes available for use in the development and performance evaluation of signal processing strategies. The advantage of the described methodology is demonstrated using a simple artifact removal technique with an accelerometer based reference.
Accurately modelled computer-generated data can be used in place of real-world signals for the design, test and validation of signal processing techniques in situations where real data is difficult to obtain. Bio-signal processing researchers interested in working with fNIRS data are restricted due to the lack of freely available fNIRS data and by the prohibitively expensive cost of fNIRS systems. We present a simplified mathematical description and associated MATLAB implementation of model-based synthetic fNIRS data which could be used by researchers to develop fNIRS signal processing techniques. The software, which is freely available, allows users to generate fNIRS data with control over a wide range of parameters and allows for fine-tuning of the synthetic data. We demonstrate how the model can be used to generate raw fNIRS data similar to recorded fNIRS signals. Signal processing steps were then applied to both the real and synthetic data. Visual comparisons between the temporal and spectral properties of the real and synthetic data show similarity. This paper demonstrates that our model for generating synthetic fNIRS data can replicate real fNIRS recordings.
There has been significant growth in the area of ubiquitous, pervasive, distributed healthcare technologies due to the increasing burden on the healthcare system and the impending demographic shift towards an aging population. The move from a hospital-centric healthcare system towards in-home health assessment is aimed to alleviate the burden on healthcare professionals, the health care system and caregivers. Advances in signal acquisition, data storage and communication channels provide for the collection of reliable and useful in-home physiological data. Artifacts, arising from environmental, experimental and physiological factors, degrade signal quality and reduce the utility of the affected part of the signal. The degrading effect of the artifacts significantly increases when data collection is moved from the clinic into the home. Advances in signal processing have brought about significant improvement in artifact removal over the last number of years. This paper reviews the most common physiological and location-indicative signals recorded in the home and documents the artifacts which occur most often. A discussion of some of the most common artifact removal techniques is then provided. An evaluation of the advantages and disadvantages of each is given with reference to the assisted living environment.
Connected health represents an increasingly important model for health-care delivery. The concept is heavily reliant on technology and in particular remote physiological monitoring. One of the principal challenges is the maintenance of high quality data streams which must be collected with minimally intrusive, inexpensive sensor systems operating in difficult conditions. Ambulatory monitoring represents one of the most challenging signal acquisition challenges of all in that data is collected as the patient engages in normal activities of everyday living. Data thus collected suffers from considerable corruption as a result of artifact, much of it induced by motion and this has a bearing on its utility for diagnostic purposes. We propose a model for ambulatory signal recording in which the data collected is accompanied by labeling indicating the quality of the collected signal. As motion is such an important source of artifact we demonstrate the concept in this case with a quality of signal measure derived from motion sensing technology viz. accelerometers. We further demonstrate how different types of artifact might be tagged to inform artifact reduction signal processing elements during subsequent signal analysis. This is demonstrated through the use of multiple accelerometers which allow the algorithm to distinguish between disturbance of the sensor relative to the underlying tissue and movement of this tissue. A brain monitoring experiment utilizing EEG and fNIRS is used to illustrate the concept.
Electrocardiography (ECG) is a test that measures the electrical activity of the heart. The use of ECG for recording in ambulatory settings is becoming more prominent due to an increase in in-home monitoring. By virtue of the ambulatory nature of the recordings, artifacts have a large effect on the signals, with the most significant artifact a result of motion. This paper describes an accelerometer system used to detect differential movement between the recording electrodes on the body. This system is then used to determine a Quality of Signal (QOS) metric for the ECG signal. The results show that the use of differential movement of the recording electrodes with respect to one another is a better representative of the motion artifact, then overall body movement. This simple Signal Quality metric is used to more accurately flag the appropriate noisy ECG data which can be rejected from the signal. The simplicity of this system also allows it to be easily embedded into any in-home monitoring system.