Discriminating spatiotemporal movement strategies during spine flexion-extension in healthy individuals

Beaudette, Shawn M.; Zwambag, Derek P.; Graham, Ryan B.; Brown, Stephen H.M.

doi:10.1016/j.spinee.2019.02.002

Cited by 13 publications

(22 citation statements)

References 42 publications

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“…Furthermore, these dynamics influence how a suitable, safe movement may look like (e.g., higher knee flexion in boxes with low handle). Research has shown heterogeneity in flexion-extension movements across healthy participants (Beaudette et al, 2019 ; Zwambag et al, 2019 ) and also that different lifting techniques are required to accomplish different lifting tasks in different contexts (van Dieën et al, 1999 ; Swinton et al, 2012 ). It follows then that the optimal lifting technique for all individuals (i.e., one size fits all) does not exist and cannot be taught in educational programs.…”

Section: Deliberate Practice Framework and Principles Of Motor Learning For Designing A Training Intervention: A Theoretical Perspectivementioning

confidence: 99%

Deliberate Practice and Motor Learning Principles to Underpin the Design of Training Interventions for Improving Lifting Movement in the Occupational Sector: A Perspective and a Pilot Study on the Role of Augmented Feedback

Oppici

Grütters

Garofolini

et al. 2021

Front. Sports Act. Living

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Spine posture during repetitive lifting is one of the main risk factors for low-back injuries in the occupational sector. It is thus critical to design appropriate intervention strategies for training workers to improve their posture, reducing load on the spine during lifting. The main approach to train safe lifting to workers has been educational; however, systematic reviews and meta-analyses have shown that this approach does not improve lifting movement nor reduces the risk of low back injury. One of the main limitations of this approach lies in the amount, quality and context of practice of the lifting movement. In this article, first we argue for integrating psychologically-grounded perspectives of practice design in the development of training interventions for safe lifting. Principles from deliberate practice and motor learning are combined and integrated. Given the complexity of lifting, a training intervention should occur in the workplace and invite workers to repeatedly practice/perform the lifting movement with the clear goal of improving their lifting-related body posture. Augmented feedback has a central role in creating the suitable condition for achieving such intervention. Second, we focus on spine bending as risk factor and present a pilot study examining the benefits and boundary conditions of different feedback modalities for reducing bending during lifting. The results showed how feedback modalities meet differently key requirements of deliberate practice conditions, i.e., feedback has to be informative, individualized and actionable. Following the proposed approach, psychology will gain an active role in the development of training interventions, contributing to finding solutions for a reduction of risk factors for workers.

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Section: Deliberate Practice Framework and Principles Of Motor Learning For Designing A Training Intervention: A Theoretical Perspectivementioning

confidence: 99%

Deliberate Practice and Motor Learning Principles to Underpin the Design of Training Interventions for Improving Lifting Movement in the Occupational Sector: A Perspective and a Pilot Study on the Role of Augmented Feedback

Oppici

Grütters

Garofolini

et al. 2021

Front. Sports Act. Living

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“…To determine the optimal k (number of clusters), we used the Bayesian information criterion (BIC), where for k = 1-10 a GMM was fit to the dataset and the minimum BIC identified the best k. An optimal k was determined for each movement: DS, RHS and LHS, respectively. A GMM for each movement was applied to each data set respectively, running 100 repetitions to increase the likelihood of the data converging to an optimum (Beaudette et al, 2019). Following the application of the GMM to each movement dataset, centroid scores from each cluster were determined along with the clustering assignments from each individual trial, where hard clustering was performed such that each trial was assigned to only 1 phenotype.…”

Section: Classificationmentioning

confidence: 99%

“…With the distribution, soft clustering-based method, GMM, the clusters can represent different ellipsoid shapes, overlap or be relatively close to one another which can skew results determined by a method such as a silhouette analysis. Silhouette analysis measures the separability of the clusters based on how close each point in one cluster is to points in the neighboring clusters (Beaudette et al, 2019). As an alternative, the BIC is a criterion for model selection among a finite set of models partly based on the likelihood function.…”

Section: Knee Ankle Separation Ratiomentioning

confidence: 99%

“…In the biomechanical analysis of human movement data, clustering has proven useful for grouping participants with similar patterns (Sawacha et al, 2010;Bennetts et al, 2013;Gilles and Wild, 2018) and gait waveforms (Watelain et al, 2000;Toro et al, 2007;Roche et al, 2014). Previously, PCA and clustering techniques have been combined to identify and group distinct spine spatiotemporal movement strategies (Beaudette et al, 2019), which support that a combination of these methods may have utility in objectively identifying movement phenotypes in a movement screening context. However, such application of pattern recognition and clustering to identify naturally occurring movement phenotypes within the movement screening context remains a novel endeavor.…”

Section: Introductionmentioning

confidence: 98%

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Exploring the Application of Pattern Recognition and Machine Learning for Identifying Movement Phenotypes During Deep Squat and Hurdle Step Movements

Remedios

Armstrong

Graham

et al. 2020

Front. Bioeng. Biotechnol.

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Background: Movement screens are increasingly used in sport and rehabilitation to evaluate movement competency. However, common screens are often evaluated using subjective visual detection of a priori prescribed discrete movement features (e.g., spine angle at maximum squat depth) and may not account for whole-body movement coordination, or associations between different discrete features. Objective: To apply pattern recognition and machine learning techniques to identify whole-body movement pattern phenotypes during the performance of exemplar functional movement screening tasks; the deep squat and hurdle step. Additionally, we also aimed to compare how discrete kinematic measures, commonly used to score movement competency, differed between emergent groups identified via pattern recognition and machine learning. Methods: Principal component analysis (PCA) was applied to 3-dimensional (3D) trajectory data from participant's deep squat (DS) and hurdle step performance, identifying emerging features that describe orthogonal modes of inter-trial variance in the data. A gaussian mixture model (GMM) was fit and used to cluster the principal component scores as an unsupervised machine learning approach to identify emergent movement phenotypes. Between group features were analyzed using a oneway ANOVA to determine if the objective classifications were significantly different from one another. Results: Three clusters (i.e., phenotypes) emerged for the DS and right hurdle step (RHS) and 4 phenotypes emerged for the left hurdle step (LHS). Selected discrete points commonly used to score DS and hurdle step movements were different between emergent groups. In regard to the select discrete kinematic measures, 4 out of 5, 7 out of 7 and 4 out of 7, demonstrated a main effect (p < 0.05) between phenotypes for the DS, RHS, and LHS respectively.

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“…CRP is used to investigate movement coordination and is quantified as the difference in phase angle between two adjacent segments in oscillation, derived from the phase plane of the segments [24]. CRP has been employed extensively in spine control research because it can differentiate between normal and abnormal spine movement [25][26][27], detect individual spine movement subtypes [28], reflect changes in muscle fatigue status [29,30], and be measured reliably using wearable IMUs [31]. In this study, 10 variables in the sagittal plane were selected to comprise a spine motion composite index (SMCI) for their known association with muscle fatigue and/or low computational processing cost: peak value of the thoraco-pelvic CRP waveform; repetition time; and IMU (pelvis and T8 vertebrae) orientation range, peak orientation, angular velocity, and angular acceleration.…”

Section: Introductionmentioning

confidence: 99%

A Subject-Specific Approach to Detect Fatigue-Related Changes in Spine Motion Using Wearable Sensors

Chan

Beaudette

Smale

et al. 2020

Sensors

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An objective method to detect muscle fatigue-related kinematic changes may reduce workplace injuries. However, heterogeneous responses to muscle fatigue suggest that subject-specific analyses are necessary. The objectives of this study were to: (1) determine if wearable inertial measurement units (IMUs) could be used in conjunction with a spine motion composite index (SMCI) to quantify subject-specific changes in spine kinematics during a repetitive spine flexion-extension (FE) task; and (2) determine if the SMCI was correlated with measures of global trunk muscle fatigue. Spine kinematics were measured using wearable IMUs in 10 healthy adults during a baseline set followed by 10 sets of 50 spine FE repetitions. After each set, two fatigue measures were collected: perceived level of fatigue using a visual analogue scale (VAS), and maximal lift strength. SMCIs incorporating 10 kinematic variables from 2 IMUs (pelvis and T8 vertebrae) were calculated and used to quantify subject-specific changes in movement. A main effect of set was observed (F (1.7, 15.32) = 10.42, p = 0.002), where the SMCI became significantly greater than set 1 starting at set 4. Significant correlations were observed between the SMCI and both fatigue VAS and maximal lift strength at the individual and study level. These findings support the use of wearable IMUs to detect subject-specific changes in spine motion associated with muscle fatigue.

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Discriminating spatiotemporal movement strategies during spine flexion-extension in healthy individuals

Cited by 13 publications

References 42 publications

Deliberate Practice and Motor Learning Principles to Underpin the Design of Training Interventions for Improving Lifting Movement in the Occupational Sector: A Perspective and a Pilot Study on the Role of Augmented Feedback

Deliberate Practice and Motor Learning Principles to Underpin the Design of Training Interventions for Improving Lifting Movement in the Occupational Sector: A Perspective and a Pilot Study on the Role of Augmented Feedback

Exploring the Application of Pattern Recognition and Machine Learning for Identifying Movement Phenotypes During Deep Squat and Hurdle Step Movements

A Subject-Specific Approach to Detect Fatigue-Related Changes in Spine Motion Using Wearable Sensors

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