Cluster-based upper body marker models for three-dimensional kinematic analysis: Comparison with an anatomical model and reliability analysis

Boser, Quinn A.; Valevicius, Aïda M.; Lavoie, Ewen B.; Chapman, Craig S.; Pilarski, Patrick M.; Hebert, Jacqueline S.; Vette, Albert H.

doi:10.1016/j.jbiomech.2018.02.028

Cited by 36 publications

(56 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The angular kinematic measures revealed offsets in the wrist flexion/extension and ulnar/radial deviation measures of the repeated study participants, likely due to differences in the kinematic calibration pose across the two studies. Such calibration errors are known to be the main limitation of the Clusters Only model [13]. In addition, a large standard deviation in trunk flexion/extension was observed for repeated study participants, also likely attributable to errors in the kinematic calibration.…”

Section: Discussionmentioning

confidence: 99%

“…Gait studies commonly revealed that inconsistencies in motion capture marker placement were a large source of anatomical model errors [18]. The Clusters Only model used by GaMA attempts to address this issue as it does not require precise individual marker placement, and has been shown to be more reliable than anatomical models [13]; it does, however, introduce its own variability caused by calibration pose inconsistencies. Gait reliability research has also identified intrinsic participant-to-participant variation within a given population and trial-to-trial variation for a given participant [18], [19].…”

Section: Discussionmentioning

confidence: 99%

“…The equipment was set up in the repeated study as specified in the original study [7]–[9]. Rigid plates and a headband (each holding four retroreflective markers) were attached to the participant in accordance with Boser et al’s Clusters Only kinematic model [13]. To improve rigid body motion tracking in the repeated study, the hand plates were redesigned as shown in Fig 1.…”

Section: Methodsmentioning

confidence: 99%

“…To improve rigid body motion tracking in the repeated study, the hand plates were redesigned as shown in Fig 1. For both studies, markers were attached to the index finger (middle phalange) and thumb (distal phalange) [7]; a head-mounted eye tracker was placed on the participant and positioned in accordance with the manufacturer’s instructions; and a motion capture calibration pose was collected for each participant, as outlined by Boser et al [13].…”

Section: Methodsmentioning

confidence: 99%

See 3 more Smart Citations

Gaze and Movement Assessment (GaMA): Inter-site validation of a visuomotor upper limb functional protocol

Williams

Chapman

Pilarski

et al. 2019

Preprint

Self Cite

View full text Add to dashboard Cite

33 Background: Successful hand-object interactions require precise hand-eye coordination with 34 continual movement adjustments. Quantitative measurement of this visuomotor behaviour could 35 provide valuable insight into upper limb impairments. The Gaze and Movement Assessment 36 (GaMA) was developed to provide protocols for simultaneous motion capture and eye tracking 37 during the administration of two functional tasks, along with data analysis methods to generate 38 standard measures of visuomotor behaviour. The objective of this study was to investigate the 39 reproducibility of the GaMA protocol across two independent groups of non-disabled participants, 40 with different raters using different motion capture and eye tracking technology.41 Methods: Twenty non-disabled adults performed the Pasta Box Task and the Cup Transfer Task.42 Upper body and eye movements were recorded using motion capture and eye tracking, 43 respectively. Measures of hand movement, angular joint kinematics, and eye gaze were compared 44 to those from a different sample of twenty non-disabled adults who had previously performed the 45 same protocol with different technology, rater and site.46 Results: Participants took longer to perform the tasks versus those from the earlier study, although 47 the relative time of each movement phase was similar. Measures that were dissimilar between the 48 groups included hand distances travelled, hand trajectories, number of movement units, eye 49 latencies, and peak angular velocities. Similarities included all hand velocity and grip aperture 50 measures, eye fixations, and most peak joint angle and range of motion measures. 51 Discussion: The reproducibility of GaMA was confirmed by this study, despite a few differences 52 introduced by learning effects, task demonstration variation, and limitations of the kinematic 53 model. The findings from this study provide confidence in the reliability of normative results 54 obtained by GaMA, indicating it accurately quantifies the typical behaviours of a non-disabled 3 55 population. This work advances the consideration for use of GaMA in populations with upper limb 56 sensorimotor impairment. 57 4 58 Introduction59 Various sensorimotor impairments including stroke [1], amputation [2], and spinal cord 60 injury [3] lead to deficits in upper limb performance that can hamper activities of daily living 61 requiring precise hand-object interactions [4]. Various functional assessments are used to gauge 62 the functional impact of upper limb impairment and to monitor rehabilitative progress thereafter 63 [5], [6]. However, such assessments often do not precisely quantify hand and joint movements, 64 grip adjustments [7], [8], or hand-eye interaction, which is recognized as an important behaviour 65 during grasp control [9], [10]. Quantitative measurement of visuomotor behaviour collected during 66 the execution of functional tasks can enhance the understanding of these movement features. 67 Measurement technologies commonly used for this purpose include eye tra...

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

See 2 more Smart Citations

Gaze and Movement Assessment (GaMA): Inter-site validation of a visuomotor upper limb functional protocol

Williams

Chapman

Pilarski

et al. 2019

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…There is a growing body of research describing the use of objective multivariate classification methods that combine a wealth of corroborating and conflicting clinical and biomechanical evidence to assist clinical decision making (Beynon et al, 2006;Jones et al, 2006;Whatling et al, 2008). A range of complex multivariate statistical methods such as principal component analysis (PCA) and linear discriminative analysis (LDA) or cluster analysis (CA) techniques are increasingly used to analyse human movement (Boser et al, 2018;Clark et al, 2017;Witte et al, 2010). A novel Cardiff Dempster-Shafer Theory (DST) Classifier is an easy-to-operate method that is able to produce discriminatory models of function in healthy and pathological populations from objective analysis of clinical and biomechanical data (Beynon et al, 2006;Jones et al, 2006;Metcalfe et al, 2017;Whatling et al, 2008).…”

Section: Introductionmentioning

confidence: 99%

Identifying non-specific low back pain clinical subgroups from sitting and standing repositioning posture tasks using a novel Cardiff Dempster–Shafer Theory Classifier

Sheeran

Sparkes

Whatling

et al. 2019

Clinical Biomechanics

View full text Add to dashboard Cite

Background: Low back pain (LBP) classification systems are used to deliver targeted treatments matched to an individual profile, however, distinguishing between different subsets of LBP remains a clinical challenge. Methods: A novel application of the Cardiff Dempster-Shafer Theory Classifier was employed to identify clinical subgroups of LBP on the basis of repositioning accuracy for subjects performing a sitting and standing posture task. 87 LBP subjects, clinically subclassified into flexion (n = 50), passive extension (n = 14), and active extension (n = 23) motor control impairment subgroups and 31 subjects with no LBP were recruited. Thoracic, lumbar and pelvic repositioning errors were quantified. The Classifier then transformed the error variables from each subject into a set of three belief values: (i) consistent with no LBP, (ii) consistent with LBP, (iii) indicating either LBP or no LBP. Findings: In discriminating LBP from no LBP the Classifier accuracy was 96.61%. From no-LBP, subsets of flexion LBP, active extension and passive extension achieved 93.83, 98.15% and 97.62% accuracy, respectively. Classification accuracies of 96.8%, 87.7% and 70.27% were found when discriminating flexion from passive extension, flexion from active extension and active from passive extension subsets, respectively. Sitting lumbar error magnitude best discriminated LBP from no LBP (92.4% accuracy) and the flexion subset from no-LBP (90.1% accuracy). Standing lumbar error best discriminated active and passive extension from no LBP (94.4% and 95.2% accuracy, respectively). Interpretation: Using repositioning accuracy, the Cardiff Dempster-Shafer Theory Classifier distinguishes between subsets of LBP and could assist decision making for targeted exercise in LBP management.

show abstract

Quantitative Eye Gaze and Movement Differences in Visuomotor Adaptations to Varying Task Demands Among Upper-Extremity Prosthesis Users

et al. 2019

Self Cite

View full text Add to dashboard Cite

Key PointsQuestionIs task selection a factor in visuomotor adaptation strategies and therefore a complication in measuring outcomes for users of upper-extremity prostheses?FindingsIn this cross-sectional study of 8 prosthesis users and 16 participants with normal arm function, between tasks, prosthesis users changed their visuomotor compensatory strategies, and these strategies were different from those used by participants with normal arm function when performing the tasks. However, for a given task, prosthesis users demonstrated similar compensations despite varying amputation levels and technology.MeaningThis study suggests that prosthesis users have inherently different ways of functioning for object interaction tasks compared with individuals with normal arm function, and compensation strategies appear to vary depending on the task.

show abstract

Cluster-based upper body marker models for three-dimensional kinematic analysis: Comparison with an anatomical model and reliability analysis

Cited by 36 publications

References 17 publications

Gaze and Movement Assessment (GaMA): Inter-site validation of a visuomotor upper limb functional protocol

Gaze and Movement Assessment (GaMA): Inter-site validation of a visuomotor upper limb functional protocol

Identifying non-specific low back pain clinical subgroups from sitting and standing repositioning posture tasks using a novel Cardiff Dempster–Shafer Theory Classifier

Quantitative Eye Gaze and Movement Differences in Visuomotor Adaptations to Varying Task Demands Among Upper-Extremity Prosthesis Users

Contact Info

Product

Resources

About