Bernard X. W. Liew

University of Essex

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Deborah Falla

University of Birmingham

Susan Morris

Curtin University

Kevin Netto

Curtin University

David Rügamer

Ludwig-Maximilians-Universität München

Xiaojun Zhai

University of Essex

David Evans

University of Birmingham

Marco Scutari

Dalle Molle Institute for Artificial Intelligence Research

César Fernández‐de‐las‐Peñas

Universidad Rey Juan Carlos

Alessandro Marco De Nunzio

University of Luxembourg

Jason Moran

University of Essex

Cooperative Institutions

University of Essex

University of Birmingham

Universidad Rey Juan Carlos

Physiotherapy New Zealand

Johns Hopkins University

Curtin University

Vrije Universiteit Brussel

University of Maryland, Baltimore

Harvard University

Aalborg University

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A Kalman Filter based Approach for Markerless Pose Tracking and Assessment

2022 27th International Conference on Automation and Computing (ICAC) (2022)

Xuqi Zhu Issam Boukhennoufa Bernard X. W. Liew Klaus D. McDonald-Maier Xiaojun Zhai

The assessment and treatment of diseases that causes movement impairments typically rely upon clinical information obtained from self-reported rating scales and clinical observation from health professionals. Currently, objective clinical gait analysis requires the use of expensive cameras or wearable sensors, which may be too time-consuming for routine clinical usage. Therefore, an assessment system that can provide non-invasive gait analysis tool is desired. In this paper, we propose a cost-efficient assessment system that combines computer vision and artificial intelligence technology to analyse human gait, which can provide a basic clinical report for clinicians to evaluate the patients' recovery to facilitate clinical decision making. And a series of experiments is taken to improve this assessment system performance. Those experiments showed that when the visibility threshold (VT) is set to a relatively high level (VT =0.4), the postprocessing part, which includes a Kalman filter and a FDF, can improve the human pose detection model (BlazePose)'s joint angle prediction accuracy by 10%. This post-processing method can be applied to other human body detection models to achieve filtering and feature extraction from joint angle signals for clinical gait analysis.

Tracking (education)

10.1109/icac55051.2022.9911152

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Citations (4)

Motion Analysis in Neurological Rehabilitation: From the Lab to the Clinic

Springer eBooks (2024)

Renato Baptista Bernard X. W. Liew Serena Pizzocaro Xiaojun Zhai Svonko Galasso

Neurological Rehabilitation

10.1007/978-3-031-63604-2_7

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A cross-sectional investigation of back pain beliefs and fear in physiotherapy and sport undergraduate students

PLoS ONE (2023)

Cameron Black Adrian Mallows Sally Waterworth Paul B. Freeman Edward Hope

Background Although low back pain (LBP) beliefs have been well investigated in mainstream healthcare discipline students, the beliefs within sports-related study students, such as Sport and Exercise Science (SES), Sports Therapy (ST), and Sport Performance and Coaching (SPC) programmes have yet to be explored. This study aims to understand any differences in the beliefs and fear associated with movement in students enrolled in four undergraduate study programmes–physiotherapy (PT), ST, SES, and SPC. Method 136 undergraduate students completed an online survey. All participants completed the Tampa Scale of Kinesiophobia (TSK) and Back Beliefs Questionnaire (BBQ). Two sets of two-way between-subjects Analysis of Variance (ANOVA) were conducted for each outcome of TSK and BBQ, with the independent variables of the study programme, study year (1st, 2nd, 3rd), and their interaction. Results There was a significant interaction between study programme and year for TSK (F(6, 124) = 4.90, P < 0.001) and BBQ (F(6, 124) = 8.18, P < 0.001). Post-hoc analysis revealed that both PT and ST students had lower TSK and higher BBQ scores than SES and SPC students particularly in the 3rd year. Conclusions The beliefs of clinicians and trainers managing LBP are known to transfer to patients, and more negative beliefs have been associated with greater disability. This is the first study to understand the beliefs about back pain in various sports study programmes, which is timely, given that the management of injured athletes typically involves a multidisciplinary team.

Sports Science

Sports medicine

Cross-sectional study

Post-hoc analysis

10.1371/journal.pone.0284754

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Clinical predictive modelling of post-surgical recovery in individuals with cervical radiculopathy: a machine learning approach

Scientific Reports (2020)

Bernard X. W. Liew Anneli Peolsson David Rügamer Johanna Wibault Håkan Löfgren

Abstract Prognostic models play an important role in the clinical management of cervical radiculopathy (CR). No study has compared the performance of modern machine learning techniques, against more traditional stepwise regression techniques, when developing prognostic models in individuals with CR. We analysed a prospective cohort dataset of 201 individuals with CR. Four modelling techniques (stepwise regression, least absolute shrinkage and selection operator [LASSO], boosting, and multivariate adaptive regression splines [MuARS]) were each used to form a prognostic model for each of four outcomes obtained at a 12 month follow-up (disability—neck disability index [NDI]), quality of life (EQ5D), present neck pain intensity, and present arm pain intensity). For all four outcomes, the differences in mean performance between all four models were small (difference of NDI < 1 point; EQ5D < 0.1 point; neck and arm pain < 2 points). Given that the predictive accuracy of all four modelling methods were clinically similar, the optimal modelling method may be selected based on the parsimony of predictors. Some of the most parsimonious models were achieved using MuARS, a non-linear technique. Modern machine learning methods may be used to probe relationships along different regions of the predictor space.

Stepwise regression

Lasso

Neck pain

Predictive modelling

Boosting

10.1038/s41598-020-73740-7

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Citations (17)

Increase in Leg Stiffness Reduces Joint Work During Backpack Carriage Running at Slow Velocities

Journal of Applied Biomechanics (2017)

Bernard X. W. Liew Kevin Netto Susan Morris

Optimal tuning of leg stiffness has been associated with better running economy. Running with a load is energetically expensive, which could have a significant impact on athletic performance where backpack carriage is involved. The purpose of this study was to investigate the impact of load magnitude and velocity on leg stiffness. We also explored the relationship between leg stiffness and running joint work. Thirty-one healthy participants ran overground at 3 velocities (3.0, 4.0, 5.0 m·s-1), whilst carrying 3 load magnitudes (0%, 10%, 20% weight). Leg stiffness was derived using the direct kinetic-kinematic method. Joint work data was previously reported in a separate study. Linear models were used to establish relationships between leg stiffness and load magnitude, velocity, and joint work. Our results found that leg stiffness did not increase with load magnitude. Increased leg stiffness was associated with reduced total joint work at 3.0 m·s-1, but not at faster velocities. The association between leg stiffness and joint work at slower velocities could be due to an optimal covariation between skeletal and muscular components of leg stiffness, and limb attack angle. When running at a relatively comfortable velocity, greater leg stiffness may reflect a more energy efficient running pattern.

Backpack

Joint stiffness

10.1123/jab.2016-0226

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Citations (13)

Quantifying lumbar mobility using a single tri-axial accelerometer

Heliyon (2024)

David Evans Ian T.Y. Wong Hoi Kam Leung Hanyun Yang Bernard X. W. Liew

BackgroundLumbar mobility is regarded as important for assessing and managing low back pain (LBP). Inertial Measurement Units (IMUs) are currently the most feasible technology for quantifying lumbar mobility in clinical and research settings. However, their gyroscopes are susceptible to drift errors, limiting their use for long-term remote monitoring.Research QuestionCan a single tri-axial accelerometer provide an accurate and feasible alternative to a multi-sensor IMU for quantifying lumbar flexion mobility and velocity?MethodsIn this cross-sectional study, 18 healthy adults performed nine repetitions of full spinal flexion movements. Lumbar flexion mobility and velocity were quantified using a multi-sensor IMU and just the tri-axial accelerometer within the IMU. Correlations between the two methods were assessed for each percentile of the lumbar flexion movement cycle, and differences in measurements were modelled using a Generalised Additive Model (GAM).ResultsVery high correlations (r > 0.90) in flexion angles and velocities were found between the two methods for most of the movement cycle. However, the accelerometer overestimated lumbar flexion angle at the start (-4.7° [95% CI -7.6° to -1.8°]) and end (-4.8° [95% CI -7.7° to -1.9°]) of movement cycles, but underestimated angles (maximal difference of 4.3° [95% CI 1.4° to 7.2°]) between 7% and 92% of the movement cycle. For flexion velocity, the accelerometer underestimated at the start (16.6°/s [95%CI 16.0 to 17.2°/s]) and overestimated (-12.3°/s [95%CI -12.9 to -11.7°/s]) at the end of the movement, compared to the IMU.SignificanceDespite the observed differences, the study suggests that a single tri-axial accelerometer could be a feasible tool for continuous remote monitoring of lumbar mobility and velocity. This finding has potential implications for the management of LBP, enabling more accessible and cost-effective monitoring of lumbar mobility in both clinical and research settings.

10.1016/j.heliyon.2024.e32544

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A network analysis reveals the interaction between fear and physical features in people with neck pain

Scientific Reports (2022)

Valter Devecchi Ahmed Alalawi Bernard X. W. Liew Deborah Falla

Abstract Although neck pain is known to be a complex and multifactorial condition characterised by the interplay between physical and psychological domains, a comprehensive investigation examining the interactions across multiple features is still lacking. In this study, we aimed to unravel the structure of associations between physical measures of neuromuscular function and fear of movement in people with a history of neck pain. One hundred participants (mean age 33.3 ± 9.4) were assessed for this cross-sectional study, and the neuromuscular and kinematic features investigated were the range of motion, velocity of neck movement, smoothness of neck movement, neck proprioception (measured as the joint reposition error), and neck flexion and extension strength. The Tampa Scale for Kinesiophobia was used to assess fear of movement. A network analysis was conducted to estimate the associations across features, as well as the role of each feature in the network. The estimated network revealed that fear of movement and neuromuscular/kinematic features were conditionally dependent. Higher fear of movement was associated with a lower range of motion, velocity, smoothness of neck movement, neck muscle strength, and proprioception (partial correlations between − 0.05 and − 0.12). Strong interactions were also found between kinematics features, with partial correlations of 0.39 and 0.58 between the range of motion and velocity, and between velocity and smoothness, respectively. The velocity of neck movement was the most important feature in the network since it showed the highest strength value. Using a novel approach to analysis, this study revealed that fear of movement can be associated with a spectrum of neuromuscular/kinematic adaptations in people with a history of neck pain.

Proprioception

Smoothness

Neck pain

10.1038/s41598-022-14696-8

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Citations (20)

Joint-level energetics differentiate isoinertial from speed-power resistance training — a Bayesian analysis

Bernard X. W. Liew Christopher Drovandi Samuel Clifford Justin Keogh Susan Morris

Background: There is convincing evidence for the benefits of resistance training on vertical jump improvements, but little evidence to guide optimal training prescription. The inability to detect small between modality effects may partially reflect the use of ANOVA statistics. This study represents the results of a sub-study from a larger project investigating the effects of two resistance training methods on load carriage running energetics. Bayesian statistics were used to compare the effectiveness of isoinertial resistance against speed-power training to change countermovement jump (CMJ) and squat jump (SJ) height, and joint energetics. Methods: Active adults were randomly allocated to either a six-week isoinertial (n = 16; calf raises, leg press, and lunge), or a speed-power training program (n = 14; countermovement jumps, hopping, with hip flexor training to target pre-swing running energetics). Primary outcome variables included jump height and joint power. Bayesian mixed modelling and Functional Data Analysis were used, where significance was determined by a non-zero crossing of the 95% Bayesian Credible Interval (CrI). Results: The gain in CMJ height after isoinertial training was 1.95 cm (95% CrI [0.85–3.04] cm) greater than the gain after speed-power training, but the gain in SJ height was similar between groups. In the CMJ, isoinertial training produced a larger increase in power absorption at the hip by a mean 0.018% (equivalent to 35 W) (95% CrI [0.007–0.03]), knee by 0.014% (equivalent to 27 W) (95% CrI [0.006–0.02]) and foot by 0.011% (equivalent to 21 W) (95% CrI [0.005–0.02]) compared to speed-power training. Discussion: Short-term isoinertial training improved CMJ height more than speed-power training. The principle adaptive difference between training modalities was at the level of hip, knee and foot power absorption.

Plyometrics

Countermovement

Squat

Source

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Citations (1)

A comparison and update of direct kinematic-kinetic models of leg stiffness in human running

Journal of Biomechanics (2017)

Bernard X. W. Liew Susan Morris Ashleigh Masters Kevin Netto

Ground reaction force

10.1016/j.jbiomech.2017.09.028

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Citations (22)

Association between fat and fat-free body mass indices on shock attenuation during running

Journal of Biomechanics (2024)

Bernard X. W. Liew Xuqi Zhu Xiaojun Zhai Stuart A. McErlain‐Naylor Christopher McManus

High amplitudes of shock during running have been thought to be associated with an increased injury risk. This study aimed to quantify the association between dual-energy X-ray absorptiometry (DEXA) quantified body composition, and shock attenuation across the time and frequency domains. Twenty-four active adults participated. A DEXA scan was performed to quantify the fat and fat-free mass of the whole-body, trunk, dominant leg, and viscera. Linear accelerations at the tibia, pelvis, and head were collected whilst participants ran on a treadmill at a fixed dimensionless speed 1.00 Fr. Shock attenuation indices in the time- and frequency-domain (lower frequencies: 3-8 Hz; higher frequencies: 9-20 Hz) were calculated. Pearson correlation analysis was performed for all combinations of DEXA and attenuation indices. Regularised regression was performed to predict shock attenuation indices using DEXA variables. A greater power attenuation between the head and pelvis within the higher frequency range was associated with a greater trunk fat-free mass (r = 0.411, p = 0.046), leg fat-free mass (r = 0.524, p = 0.009), and whole-body fat-free mass (r = 0.480, p = 0.018). For power attenuation of the high-frequency component between the pelvis and head, the strongest predictor was visceral fat mass (β = 48.79). Passive and active tissues could represent important anatomical factors aiding in shock attenuation during running. Depending on the type and location of these masses, an increase in mass may benefit injury risk reduction. Also, our findings could implicate the injury risk potential during weight loss programs.

10.1016/j.jbiomech.2024.112025

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Citations (1)