Kinematic biomarkers of chronic neck pain measured during gait: A data-driven classification approach

Jiménez-Grande, David; Atashzar, S. Farokh; Martinez‐Valdes, Eduardo; Nunzio, Alessandro Marco De; Falla, Deborah

doi:10.1016/j.jbiomech.2020.110190

Cited by 14 publications

(4 citation statements)

References 48 publications

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“…The altered variability of head and neck movements found during the Butterfly test in our study is related to body sway velocity and frequency in the AP direction. This is partially in line with other studies, where cervical spine injury was associated with altered body stiffness in postural and locomotion tasks [ 37 , 38 ]. In general, postural and locomotor tasks are dependent on a closed-loop mechanism, where movement corrections are based on proprioceptive feedback [ 39 ]; however in neck pain patients, this closed-loop system could be hampered due to commonly observed sensory mismatch.…”

Section: Discussionsupporting

confidence: 93%

Relationship between Cervicocephalic Kinesthetic Sensibility Measured during Dynamic Unpredictable Head Movements and Eye Movement Control or Postural Balance in Neck Pain Patients

Rosker

Vodičar

Kristjansson

2022

IJERPH

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Cervical afferent input is believed to affect postural balance and oculomotor control in neck pain patients, but its relationship to cervicocephalic kinesthesia, describing movement sense, has not yet been studied. The aim of this study was to analyze the relationship of two aspects of cervicocephalic kinesthesia to postural balance and oculomotor control in neck torsion positions. Forty-three idiopathic neck pain patients referred from orthopedic outpatient clinics and forty-two asymptomatic controls were enrolled in the study. A force plate was used to measure center-of-pressure movements during parallel stances under neutral and neck torsion maneuvers. Video-oculography was used to assess eye movements during smooth pursuit neck torsion test (SPNTT), while kinesthetic awareness was measured using the Butterfly test and head-to-neutral relocation test. Multiple regression was used to describe relationships between tests. Body sway in the anterior–posterior direction was related to Butterfly parameters but less to the head-to-neutral test. A medium relationship between Butterfly parameters and gain during SPNTT, with less SPNT-difference, was observed, but not for the head-to-neutral test. It can be concluded that specific aspect of neck kinesthetic functions (i.e., movement sense) importantly contributes towards oculomotor and balance control, which is more evident under neck torsion positions in neck pain patients, but is less pronounced in asymptomatic individuals.

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Section: Discussionsupporting

confidence: 93%

Relationship between Cervicocephalic Kinesthetic Sensibility Measured during Dynamic Unpredictable Head Movements and Eye Movement Control or Postural Balance in Neck Pain Patients

Rosker

Vodičar

Kristjansson

2022

IJERPH

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“…In the majority of the state-of-the-art works spatiotemporal gait features have been used for gait pattern classification, accompanied by 3D gait joint angles and kinetics features [9]. Previous studies applied supervised learning algorithms driven by kinematic data acquired during gait to differentiate subjects with chronic neck pain and healthy ones [28], predict the severity of Parkinson's disease [29], categorize gait patterns in CP subjects [12,30] and link toe-walking gait patterns to their clinical cause [31]. In the present contribution, in contrast to previous works adopting only kinematic or only sEMG features, both types of features were combined in different ways in order to fully explore their classification potential.…”

Section: Discussionmentioning

confidence: 99%

A Supervised Classification of Children with Fragile X Syndrome and Controls Based on Kinematic and sEMG Parameters

et al. 2022

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Fragile X syndrome (FXS) is caused by pathologic expansions of the CGG repeat polymorphic region of the FMR1 gene. There are two main categories of FMR1 mutations, “premutation” and “full mutation”, that are associated with different clinical phenotypes, and somatic mosaicism can represent a strong FXS phenotype modulator. FXS is the leading cause of inherited intellectual disability and autism, and it is characterized by musculoskeletal manifestations such as flexible flat feet, joint laxity and hypotonia. The former have been associated with altered joint kinematics and muscle activity during gait. The aim of this study was to use gait analysis parameters to classify FXS children from healthy controls and, within FXS children with full mutation, to classify children with mosaicism. Seven supervised machine learning algorithms were applied to a dataset of joint kinematics and surface electromyographic signals collected on twenty FXS children and sixteen controls. Results showed that the k-NN algorithm outperformed in terms of accuracy (100%) in classifying FXS children from controls, while CN2 rule induction obtained the best accuracy (97%) in classifying FXS children with mosaicism. The proposed pipeline might be used for developing assisted decision-making systems aiming at identifying and treating the musculoskeletal alterations associated with FXS.

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“…NCA is a nonparametric dimensionality reduction technique which learns the Mahalanobis distance used in the k-nearest neighbourhood classification algorithm [ 48 ]. NCA has been applied on kinematics, kinetics and physiological signals [ 49 , 50 , 51 , 52 , 53 ] and has been shown to outperform other conventional algorithms such as principle component analysis and reliefF [ 50 , 53 ]. This approach optimises the feature weights by minimising the objective function that measures the leave-one-out prediction loss over the training data.…”

Section: Methodsmentioning

confidence: 99%

Joint Torque Prediction via Hybrid Neuromusculoskeletal Modelling during Gait Using Statistical Ground Reaction Estimates: An Exploratory Study

Lam¹,

Vujaklija²

2021

Sensors

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Joint torques of lower extremity are important clinical indicators of gait capability. This parameter can be quantified via hybrid neuromusculoskeletal modelling that combines electromyography-driven modelling and static optimisation. The simulations rely on kinematics and external force measurements, for example, ground reaction forces (GRF) and the corresponding centres of pressure (COP), which are conventionally acquired using force plates. This bulky equipment, however, hinders gait analysis in real-world environments. While this portability issue could potentially be solved by estimating the parameters through machine learning, the effect of the estimation errors on joint torque prediction with biomechanical models remains to be investigated. This study first estimated GRF and COP through feedforward artificial neural networks, and then leveraged them to predict lower-limb sagittal joint torques via (i) inverse dynamics and (ii) hybrid modelling. The approach was evaluated on five healthy subjects, individually. The predicted torques were validated with the measured torques, showing that hip was the most sensitive whereas ankle was the most resistive to the GRF/COP estimates for both models, with average metrics values being 0.70 < R2 < 0.97 and 0.069 < RMSE < 0.15 (Nm/kg). This study demonstrated the feasibility of torque prediction based on personalised (neuro)musculoskeletal modelling using statistical ground reaction estimates, thus providing insights into potential real-world mobile joint torque quantification.

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Kinematic biomarkers of chronic neck pain measured during gait: A data-driven classification approach

Cited by 14 publications

References 48 publications

Relationship between Cervicocephalic Kinesthetic Sensibility Measured during Dynamic Unpredictable Head Movements and Eye Movement Control or Postural Balance in Neck Pain Patients

Relationship between Cervicocephalic Kinesthetic Sensibility Measured during Dynamic Unpredictable Head Movements and Eye Movement Control or Postural Balance in Neck Pain Patients

A Supervised Classification of Children with Fragile X Syndrome and Controls Based on Kinematic and sEMG Parameters

Joint Torque Prediction via Hybrid Neuromusculoskeletal Modelling during Gait Using Statistical Ground Reaction Estimates: An Exploratory Study

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