High-density surface electromyography allows to identify risk conditions and people with and without low back pain during fatiguing frequency-dependent lifting activities.

Low back pain (LBP) is a leading cause of disability in the workplace, often caused by manually lifting of heavy loads. Instrumental-based assessment tools are used to quantitatively assess the biomechanical risk of lifting activities. This study aims to verify that, during the execution of fatiguing frequency-dependent lifting, high-density surface electromyography (HDsEMG) allows the discrimination of healthy controls (HC) versus people with LBP and biomechanical risk levels. Fifteen HC and eight people with LBP performed three lifting tasks with a progressively increasing lifting index, each lasting 15 min. Erector spinae (ES) activity was recorded using HDsEMG and amplitude parameters were calculated to characterize the spatial distribution of muscle activity. LBP group showed a less ES activity than HC (lower root mean square across the grid and of the activation region) and an involvement of the same muscular area across the task (lower coefficient of variation of the center of gravity of muscle activity). The results indicate the usefulness of HDsEMG parameters to classify risk levels for both HC and LBP groups and to determine differences between them. The findings suggest that the use of HDsEMG could expand the capabilities of existing instrumental-based tools for biomechanical risk classification during lifting activities.

Gait Analysis in Neurorehabilitation: From Research to Clinical Practice.

When brain damage occurs, gait and balance are often impaired. Evaluation of the gait cycle, therefore, has a pivotal role during the rehabilitation path of subjects who suffer from neurological disorders. Gait analysis can be performed through laboratory systems, non-wearable sensors (NWS), and/or wearable sensors (WS). Using these tools, physiotherapists and neurologists have more objective measures of motion function and can plan tailored and specific gait and balance training early to achieve better outcomes and improve patients' quality of life. However, most of these innovative tools are used for research purposes (especially the laboratory systems and NWS), although they deserve more attention in the rehabilitation field, considering their potential in improving clinical practice. In this narrative review, we aimed to summarize the most used gait analysis systems in neurological patients, shedding some light on their clinical value and implications for neurorehabilitation practice.

Exergames to Limit Weight Gain and to Fight Sedentarism in Children and Adolescents with Obesity.

Exergames are defined as digital games that require bodily movements to play, stimulating an active gaming experience to function as a form of physical activity (PA). The players interact with the game through whole-body movements improving energy expenditure. Exergames may be effective in improving physical and psychological aspects of children and adolescents with obesity. In this narrative review, we synthesized the current evidence regarding the role of exergames in modifying body composition and weight and in promoting changes in sedentary behavior to define the benefits of active video games as useful tools for fighting sedentarism and to outline the future directions of exergaming as a supplementation exercise rather than a replacement in educational programs for pediatric obesity. Data from the literature indicate that exergames may offer an interesting impact on childhood obesity and may be considered a potential strategy for controlling weight gain and body composition, promote PA, and decrease time spent on sedentary behavior in children and adolescents with obesity. However, exergame use also has some limits, such as children's poor self-regulation and poor structuring of exergame use. Therefore, a prudent approach should be maintained, and additional high-quality research is needed to determine if exergames can be effectively used in the treatment of childhood obesity and if new digital media, as a supplementation of exercise rather than a replacement, could be considered to combat sedentary behavior in educational programs for pediatric obesity prevention.

Innovative Technologies in the Neurorehabilitation of Traumatic Brain Injury: A Systematic Review.

Motor and cognitive rehabilitation in individuals with traumatic brain injury (TBI) is a growing field of clinical and research interest. In fact, novel rehabilitative approaches allow a very early verticalization and gait training through robotic devices and other innovative tools boosting neuroplasticity, thanks to the high-intensity, repetitive and task-oriented training. In the same way, cognitive rehabilitation is also evolving towards advanced interventions using virtual reality (VR), computer-based approaches, telerehabilitation and neuromodulation devices. This review aimed to systematically investigate the existing evidence concerning the role of innovative technologies in the motor and cognitive neurorehabilitation of TBI patients. We searched and reviewed the studies published in the Cochrane Library, PEDro, PubMed and Scopus between January 2012 and September 2022. After an accurate screening, only 29 papers were included in this review. This systematic review has demonstrated the beneficial role of innovative technologies when applied to cognitive rehabilitation in patients with TBI, while evidence of their effect on motor rehabilitation in this patient population is poor and still controversial.

Training-Induced Muscle Fatigue with a Powered Lower-Limb Exoskeleton: A Preliminary Study on Healthy Subjects.

Powered lower-limb exoskeletons represent a promising technology for helping the upright stance and gait of people with lower-body paralysis or severe paresis from spinal cord injury. The powered lower-limb exoskeleton assistance can reduce the development of lower-limb muscular fatigue as a risk factor for spasticity. Therefore, measuring powered lower-limb exoskeleton training-induced fatigue is relevant to guiding and improving such technology's development. In this preliminary study, thirty healthy subjects (age 23.2 ± 2.7 years) performed three motor tasks: (i) walking overground (WO), (ii) treadmill walking (WT), (iii) standing and sitting (STS) in three separate exoskeleton-based training sessions of 60 min each. The changes in the production of lower-limb maximal voluntary isometric contraction (MVIC) were assessed for knee and ankle dorsiflexion and extension before and after the three exoskeleton-based trained motor tasks. The MVIC forces decreased significantly after the three trained motor tasks except for the ankle dorsiflexion. However, no significant interaction was found between time (before-, and after-training) and the training sessions except for the knee flexion, where significant fatigue was induced by WO and WT trained motor tasks. The results of this study pose the basis to generate data useful for a better approach to the exoskeleton-based training. The STS task leads to a lower level of muscular fatigue, especially for the knee flexor muscles.

Centre of pressure parameters for the assessment of biomechanical risk in fatiguing frequency-dependent lifting activities.

Lifting tasks, among manual material handling activities, are those mainly associated with low back pain. In recent years, several instrumental-based tools were developed to quantitatively assess the biomechanical risk during lifting activities. In this study, parameters related to balance and extracted from the Centre of Pressure (CoP) data series are studied in fatiguing frequency-dependent lifting activities to: i) explore the possibility of classifying people with LBP and asymptomatic people during the execution of task; ii) examine the assessment of the risk levels associated with repetitive lifting activities, iii) enhance current understanding of postural control strategies during lifting tasks. Data were recorded from 14 asymptomatic participants and 7 participants with low back pain. The participants performed lifting tasks in three different lifting conditions (with increasing lifting frequency and risk levels) and kinetic and surface electromyography (sEMG) data were acquired. Kinetic data were used to calculated the CoP and parameters extracted from the latter show a discriminant capacity for the groups and the risk levels. Furthermore, sEMG parameters show a trend compatible with myoelectric manifestations of muscular fatigue. Correlation results between sEMG and CoP velocity parameters revealed a positive correlation between amplitude sEMG parameters and CoP velocity in both groups and a negative correlation between frequency sEMG parameters and CoP velocity. The current findings suggest that it is possible to quantitatively assess the risk level when monitoring fatiguing lifting tasks by using CoP parameters as well as identify different motor strategies between people with and without LBP.

Trunk Muscle Coactivation in People with and without Low Back Pain during Fatiguing Frequency-Dependent Lifting Activities.

Lifting tasks are manual material-handling activities and are commonly associated with work-related low back disorders. Instrument-based assessment tools are used to quantitatively assess the biomechanical risk associated with lifting activities. This study aims at highlighting different motor strategies in people with and without low back pain (LBP) during fatiguing frequency-dependent lifting tasks by using parameters of muscle coactivation. A total of 15 healthy controls (HC) and eight people with LBP performed three lifting tasks with a progressively increasing lifting index (LI), each lasting 15 min. Bilaterally erector spinae longissimus (ESL) activity and rectus abdominis superior (RAS) were recorded using bipolar surface electromyography systems (sEMG), and the time-varying multi-muscle coactivation function (TMCf) was computed. The TMCf can significantly discriminate each pair of LI and it is higher in LBP than HC. Collectively, our findings suggest that it is possible to identify different motor strategies between people with and without LBP. The main finding shows that LBP, to counteract pain, coactivates the trunk muscles more than HC, thereby adopting a strategy that is stiffer and more fatiguing.

Bipolar versus high-density surface electromyography for evaluating risk in fatiguing frequency-dependent lifting activities.

Workers often develop low back pain due to manually lifting heavy loads. Instrumental-based assessment tools are used to quantitatively assess the biomechanical risk in lifting activities. This study aims to verify the hypothesis that high-density surface electromyography (HDsEMG) allows an optimized discrimination of risk levels associated with different fatiguing lifting conditions compared to traditional bipolar sEMG. 15 participants performed three lifting tasks with a progressively increasing lifting index (LI) each lasting 15 min. Erector spinae (ES) activity was recorded using both bipolar and HDsEMG systems. The amplitude of both bipolar and HDsEMG can significantly discriminate each pair of LI. HDsEMG data could discriminate across the different LIs starting from the fourth minute of the task while bipolar sEMG could only do so towards the end. The higher discriminative power of HDsEMG data across the lifting tasks makes such methodology a valuable tool to be used to monitor fatigue while lifting and could extend the possibilities offered by currently available instrumental-based tools.

A novel metric of reliability in pressure pain threshold measurement.

The inter-session Intraclass Correlation Coefficient (ICC) is a commonly investigated and clinically important metric of reliability for pressure pain threshold (PPT) measurement. However, current investigations do not account for inter-repetition variability when calculating inter-session ICC, even though a PPT measurement taken at different sessions must also imply different repetitions. The primary aim was to evaluate and report a novel metric of reliability in PPT measurement: the inter-session-repetition ICC. One rater recorded ten repetitions of PPT measurement over the lumbar region bilaterally at two sessions in twenty healthy adults using a pressure algometer. Variance components were computed using linear mixed-models and used to construct ICCs; most notably inter-session ICC and inter-session-repetition ICC. At 70.1% of the total variance, the source of greatest variability was between subjects ([Formula: see text] = 222.28 N2), whereas the source of least variability (1.5% total variance) was between sessions ([Formula: see text] = 4.83 N2). Derived inter-session and inter-session-repetition ICCs were 0.88 (95%CI: 0.77 to 0.94) and 0.73 (95%CI: 0.53 to 0.84) respectively. Inter-session-repetition ICC provides a more conservative estimate of reliability than inter-session ICC, with the magnitude of difference being clinically meaningful. Quantifying individual sources of variability enables ICC construction to be reflective of individual testing protocols.

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

People with chronic neck pain (CNP) often present with altered gait kinematics. This paper investigates, combines, and compares the kinematic features from linear and nonlinear walking trajectories to design supervised machine learning models which differentiate asymptomatic individuals from those with CNP. For this, 126 features were extracted from seven body segments of 20 asymptomatic subjects and 20 individuals with non-specific CNP. Neighbourhood Component Analysis (NCA) was used to identify body segments and the corresponding significant features which have the maximum discriminative power for conducting classification. We assessed the efficacy of NCA combined with K- Nearest Neighbour (K-NN), Support Vector Machine and Linear Discriminant Analysis. By applying NCA, all classifiers increased their performance for both linear and nonlinear walking trajectories. Notably, features selected by NCA which magnify the classification power of the computational model were solely from the head, trunk and pelvis kinematics. Our results revealed that the nonlinear trajectory provides the best classification performance through the NCA-K-NN algorithms with an accuracy of 90%, specificity of 100% and sensitivity of 83.3%. The selected features by NCA are introduced as key biomarkers of gait kinematics for classifying non-specific CNP. This paper provides insight into changes in gait kinematics which are present in people with non-specific CNP which can be exploited for classification purposes. The result highlights the importance of curvilinear gait kinematic features which potentially could be utilized in future research to predict recurrent episodes of neck pain.

Controlled manual loading of body tissues: towards the next generation of pressure algometer.

Assessing the responses of body tissue subjected to mechanical load is a fundamental component of the clinical examination, psychophysical assessments and bioengineering research. The forces applied during such assessments are usually generated manually, via the hands of the tester, and aimed at discreet tissue sites. It is therefore desirable to objectively quantify and optimise the control of manually applied force. However, current laboratory-grade manual devices and commercial software packages, in particular pressure algometer systems, are generally inflexible and expensive. This paper introduces and discusses several principles that should be implemented as design goals within a flexible, generic software application, given currently available force measurement hardware. We also discuss pitfalls that clinicians and researchers might face when using current pressure algometer systems and provide examples of these. Finally, we present our implementation of a pressure algometer system that achieves these goals in an efficient and affordable way for researchers and clinicians. As part of this effort, we will be sharing our configurable software application via a software repository.

Kinematic Biomarkers of Chronic Neck Pain During Curvilinear Walking: A Data-driven Differential Diagnosis Approach.

Chronic Neck Pain (CNP) can be associated with biomechanical changes. This paper investigates the changes in patterns of walking kinematics along a curvilinear trajectory and uses a specially designed feature space, coupled with a machine learning framework to conduct a data-driven differential diagnosis, between asymptomatic individuals and those with CNP. For this, 126 kinematic features were collected from seven body segments of 40 participants (20 asymptomatic, 20 individuals with CNP). The features space was processed through a Neighbourhood Component Analysis (NCA) algorithm to systematically select the most significant features which have the maximum discriminative power for conducting the differential diagnosis. The selected features were then processed by a K-Nearest Neighbors (K-NN) classifier to conduct the task. Our results show that, through a systematic selection of feature space, we can significantly increase the classification accuracy. In this regard, a 35% increase is reported after applying the NCA. Thus, we have shown that using only 13 features (of which 61% belong to kinematic features and 39% to statistical features) from five body segments (Head, Trunk, Pelvic, Hip and Knee) we can achieve an accuracy, sensitivity and specificity of 82.50%, 80.95% and 84.21% respectively. This promising result highlights the importance of curvilinear kinematic features through the proposed information processing pipeline for conducting differential diagnosis and could be tested in future studies to predict the likelihood of people developing recurrent neck pain.

Influence of low back pain and its remission on motor abundance in a low-load lifting task.

Having an abundance of motor solutions during movement may be advantageous for the health of musculoskeletal tissues, given greater load distribution between tissues. The aim of the present study was to understand whether motor abundance differs between people with and without low back pain (LBP) during a low-load lifting task. Motion capture with electromyography (EMG) assessment of 15 muscles was performed on 48 participants [healthy control (con) = 16, remission LBP (rLBP) = 16, current LBP (cLBP) = 16], during lifting. Non-negative matrix factorization and uncontrolled manifold analysis were performed to decompose inter-repetition variability in the temporal activity of muscle modes into goal equivalent (GEV) and non-goal equivalent (NGEV) variabilities in the control of the pelvis and trunk linear displacements. Motor abundance occurs when the ratio of GEV to NGEV exceeds zero. There were significant group differences in the temporal activity of muscle modes, such that both cLBP and rLBP individuals demonstrated greater activity of muscle modes that reflected lumbopelvic coactivation during the lifting phase compared to controls. For motor abundance, there were no significant differences between groups. Individuals with LBP, including those in remission, had similar overall motor abundance, but use different activation profiles of muscle modes than asymptomatic people during lifting.

Classifying individuals with and without patellofemoral pain syndrome using ground force profiles - Development of a method using functional data boosting.

BACKGROUND: Predictors of recovery in patellofemoral pain syndrome (PFPS) currently used in prognostic models are scalar in nature, despite many physiological measures originally lying on the functional scale. Traditional modelling techniques cannot harness the potential predictive value of functional physiological variables. RESEARCH QUESTION: What is the classification performance of PFPS status of a statistical model when using functional ground reaction force (GRF) time-series? METHODS: Thirty-one individuals (control = 17, PFPS = 14) performed maximal countermovement jumps, on two force plates. The three-dimensional components of the GRF profiles were time-normalized between the start of the eccentric phase and take-off, and used as functional predictors. A statistical model was developed using functional data boosting (FDboost), for binary classification of PFPS statuses (control vs PFPS). The area under the Receiver Operating Characteristic curve (AUC) was used to quantify the model's ability to discriminate the two groups. RESULTS: The three predictors of GRF waveform achieved an average out-of-bag AUC of 93.7 %. A 1 % increase in applied medial force reduced the log odds of being in the PFPS group by 0.68 at 87 % of jump cycle. In the AP direction, a 1 % reduction in applied posterior force increased the log odds of being classified as PFPS by 1.10 at 70 % jump cycle. For the vertical GRF, a 1 % increase in applied force reduced the log odds of being classified in the PFPS group by 0.12 at 44 % of the jump cycle. SIGNIFICANCE: Using simple functional GRF variables collected during functionally relevant task, in conjunction with FDboost, produced clinically interpretable models that retain excellent classification performance in individuals with PFPS. FDboost may be an invaluable tool to be used in longitudinal cohort prognostic studies, especially when scalar and functional predictors are collected.

Individuals with patellofemoral pain syndrome have altered inter-leg force coordination.

BACKGROUND: Patellofemoral pain syndrome (PFPS) is one of the most common musculoskeletal disorders. Pain may be further exacerbated by atypical motor coordination strategies. It has been thought that low coordination variability may concentrate loads onto painful knee tissues. RESEARCH QUESTION: To investigate if inter-limb force coordination is altered between individuals with and without PFPS. METHODS: 31 individuals (control = 17, PFPS = 14) performed bilateral vertical hopping, on two force plates at three frequencies (2.2, 2.6, 3.0 Hz). Uncontrolled manifold analysis (UCM) was used to provide an index of motor abundance (IMA) in the coordination of inter-limb forces to stabilize the two-limb's total force. UCM was applied to the study of forces in each plane (medial-lateral (ML), anterior-posterior (AP), vertical). Bayesian Functional Data Analysis was used for statistical inference. We calculated the mean (u) with 95 % credible interval (CrI) of the difference ΔIMAcon>PFPS between the two groups. We also calculated the probability PΔIMAcon>PFPS>0data). RESULTS: Individuals with PFPS had the greatest significant decrement from controls at 6% of stance hopping at 2.6 Hz by a mean difference of -0.23 for ML GRF; at 19 % of stance hopping at 2.2 Hz by a mean difference of -0.14 for AP GRF; and 52 % of stance hopping at 2.6 Hz by a mean difference of -0.14 for vertical GRF. For vertical GRF, there was a > 0.95 probability that controls had greater IMA than individuals with PFPS hopping between 12-13% of stance at 2.2 Hz, and between 48-55% at 2.6 Hz. SIGNIFICANCE: Individuals with PFPS have reduced inter-leg force coordination for impact force attenuation and body support, compared to asymptomatic controls. The present study provides insights into a plausible mechanism underpinning persistent knee pain which could be used in the development of novel rehabilitative approaches for individuals with PFPS.

Interpretable machine learning models for classifying low back pain status using functional physiological variables.

PURPOSE: To evaluate the predictive performance of statistical models which distinguishes different low back pain (LBP) sub-types and healthy controls, using as input predictors the time-varying signals of electromyographic and kinematic variables, collected during low-load lifting. METHODS: Motion capture with electromyography (EMG) assessment was performed on 49 participants [healthy control (con) = 16, remission LBP (rmLBP) = 16, current LBP (LBP) = 17], whilst performing a low-load lifting task, to extract a total of 40 predictors (kinematic and electromyographic variables). Three statistical models were developed using functional data boosting (FDboost), for binary classification of LBP statuses (model 1: con vs. LBP; model 2: con vs. rmLBP; model 3: rmLBP vs. LBP). After removing collinear predictors (i.e. a correlation of > 0.7 with other predictors) and inclusion of the covariate sex, 31 predictors were included for fitting model 1, 31 predictors for model 2, and 32 predictors for model 3. RESULTS: Seven EMG predictors were selected in model 1 (area under the receiver operator curve [AUC] of 90.4%), nine predictors in model 2 (AUC of 91.2%), and seven predictors in model 3 (AUC of 96.7%). The most influential predictor was the biceps femoris muscle (peak [Formula: see text] = 0.047) in model 1, the deltoid muscle (peak [Formula: see text] = 0.052) in model 2, and the iliocostalis muscle (peak [Formula: see text] =  0.16) in model 3. CONCLUSION: The ability to transform time-varying physiological differences into clinical differences could be used in future prospective prognostic research to identify the dominant movement impairments that drive the increased risk. These slides can be retrieved under Electronic Supplementary Material.

Variability of neck and trunk movement during single- and dual-task gait in people with chronic neck pain.

BACKGROUND: Previous findings reported that people with chronic neck pain walk with reduced range trunk rotation, especially when walking in more challenging conditions. Quantification of the quality of neck and trunk movement during gait could provide further insight into biomechanical changes that occur in people with neck pain. This study uniquely compared the variability of trunk and neck rotation during single-task and dual-task gait in people with chronic neck pain and asymptomatic individuals. METHODS: An observational case-control study was conducted on 20 asymptomatic individuals and 24 people with chronic neck pain of idiopathic or traumatic origin. Participants performed rectilinear walking whilst keeping the head in a neutral position (single-task) and whilst rotating the head at a natural speed (dual-task). Trunk and head rotation angles were averaged across gait cycles for the task trials. The data were normalised in time, and the average variability of angular distribution along the normalised cycle was extracted. The Tampa Scale for Kinesiophobia was used to assess fear of movement. FINDINGS: During single-task gait, there were no group differences for the variability of trunk (p = 0.862) or neck (p = 0.427) rotation. For dual-task gait, there was no difference between groups for the variability of neck rotation (p = 0.636), however, the participants with neck pain displayed reduced variability of trunk rotation (p = 0.021). The neck pain group also walked at a significantly slower speed during dual-task gait (p = 0.043) compared to asymptomatic individuals and the speed of their gait was associated with the extent of fear of movement. INTERPRETATION: The strategy observed in participants with chronic neck pain likely reflects adaptive behaviour when faced with more challenging conditions for postural control.

Classifying neck pain status using scalar and functional biomechanical variables - development of a method using functional data boosting.

BACKGROUND: Individuals with neck pain have different movement and muscular activation (collectively termed as biomechanical variables) patterns compared to healthy individuals. Incorporating biomechanical variables as covariates into prognostic models is challenging due to the high dimensionality of the data. RESEARCH QUESTION: What is the classification performance of neck pain status of a statistical model which uses both scalar and functional biomechanical covariates? METHODS: Motion capture with electromyography assessment on the sternocleidomastoid, splenius cervicis, erector spinae, was performed on 21 healthy and 26 individuals with neck pain during walking over three gait conditions (rectilinear, curvilinear clockwise (CW) and counterclockwise (CCW)). After removing highly collinear variables, 94 covariates across the three conditions were used to classify neck pain status using functional data boosting (FDboost). RESULTS: Two functional covariates trunk lateral flexion angle during CCW gait, and trunk flexion angle during CW gait; and a scalar covariate, hip jerk index during CCW gait were selected. The model achieved an estimated AUC of 80.8 %. For hip jerk index, an increase in hip jerk index by one unit increased the log odds of being in the neck pain group by 0.37. A 1° increase in trunk lateral flexion angle throughout gait alone reduced the probability of being in the neck pain group from 0.5 to 0.15. A 1° increase in trunk flexion angle throughout gait alone increased the probability of being in the neck pain group from 0.5 to 0.9. SIGNIFICANCE: Interpreting the physiological significance of the extracted covariates, with other biomechanical variables, suggests that individuals with neck pain performed curvilinear walking using a stiffer strategy, compared to controls; and this increased the risk of being in the neck pain group. FDboost can produce clinically interpretable models with complex high dimensional data and could be used in future prognostic modelling studies in neck pain research.

Variability of the helical axis during active cervical movements in people with chronic neck pain.

BACKGROUND: Recent work described parameters of the helical axis in asymptomatic people with potential for investigating kinematic changes in the cervical region. This approach could provide novel information on movement variability in people with neck pain, however this has never been investigated. This study aimed to investigate movement variability during active neck movements performed at different speeds in people with and without chronic neck pain. METHODS: This observational case-control study examined 18 participants with chronic neck pain of either idiopathic or traumatic origin and 18 gender-matched asymptomatic participants. Cervical kinematics were captured with 3D motion capture as people with and without chronic neck pain performed flexion-extension, bilateral lateral flexion and bilateral rotation at different speeds (natural, slow, and fast). The mean distance and mean angle parameters of the helical axis were extracted to describe 3D motion and quantify movement variability. FINDINGS: A smaller mean distance was observed in those with neck pain compared to the asymptomatic participants during flexion-extension (P = 0.019) and rotation movements (P = 0.007). The neck pain group displayed smaller values for the mean angle during rotation movements with different speeds (P = 0.01). These findings indicate less variable movement for those with neck pain relative to the asymptomatic participants. No difference in the mean angle was observed between groups for flexion-extension and lateral flexion. INTERPRETATION: The findings reiterate the importance of data derived from kinematic measures, and its potential for providing clinicians with further insight into the quality of active neck movements in people with chronic neck pain.