Working towards an objective segmental assessment of trunk control in children with cerebral palsy

Sánchez, María Beatriz González; Loram, Ian D.; Holmes, Paul; Darby, John; Butler, Penelope B

doi:10.1016/j.gaitpost.2018.06.176

Cited by 9 publications

(27 citation statements)

References 15 publications

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“…To generate results comparable to the existing literature, segment angles were computed according to [7], [9]. Results for the relevant segments (head, neck, UT, MT, LT, UL, LL, pelvis) for our fully automated method recorded comparable performance, 6.4° (MAE), to the current benchmark semiautomated method, 3.8° (MAE) (table 1).…”

Section: B Segment Anglesmentioning

confidence: 75%

“…Data were collected in a previous study [7] from 12 children (9 males, 3 females, mean age 4.52 years ±2.4, mean height 0.97m ±0.1, and weight 16.15kg ±7.5). All children had a diagnosis of CP and attended a specialist physical therapy centre.…”

Section: B Participants Procedures and Measurementsmentioning

confidence: 99%

“…The SATCo was conducted according to the published guidelines [8], assessing control at six trunk segmental levels (head, upper-thoracic (UT), mid-thoracic (MT), lower-thoracic (LT), upper-lumbar (UL), lower-lumbar (LL)), and Free Sitting, or as far as each child's neuromuscular control permitted, within a single session [7].…”

Section: B Participants Procedures and Measurementsmentioning

confidence: 99%

“…This view allowed recording of sagittal plane movements of the head and trunk and arm. Markers (small 2 cm 3 coloured bocks) were placed on specific landmarks of the head, trunk and pelvis, to improve the lateral visualisation and tracking of the back landmarks following the model previously developed (figure 1) [7].…”

Section: B Participants Procedures and Measurementsmentioning

confidence: 99%

“…Previous work [7], [9] has demonstrated that the two components of 'alignment' and 'contact' can be accurately classified from a clinical assessment and quantified using video recordings. However, the methods used previously were semiautomated requiring manual initialisation of postural point features and necessitating significant human intervention to compensate for tracking drift and occlusion [7], [9].…”

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confidence: 99%

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Fully automated image-based estimation of postural point-features in children with cerebral palsy using deep learning

Cunningham¹,

Sánchez²,

Butler³

et al. 2018

Preprint

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Objective: To automate identification of postural point-features from colour videos of children with neuromotor disability, during clinical assessment. The automatic identification of 13 points of interest (2, 6, 2, 3 points on the head, trunk, pelvis, arm respectively) is required to estimate the location and orientation of head, trunk, and arm segments, from videos of the clinical test "Segmental Assessment of Trunk Control" (SATCo) which is a test of seated postural control. Methods: Three expert operators manually annotated 13 point-features in every fourth image of 177 short (5-10 second) videos (25 Hz) showing 12 children with cerebral palsy (ages: 4.52 ±2.4, male: 9), participating in SATCo testing. Linear interpolation for the remaining images resulted in 30,825 annotated images. Meanpooling and max-pooling convolutional neural networks were trained with cross-validation, giving held-out test results for all children. Results: The point-features were estimated with error 4.40 ±3.75 pixels (mean-pooling), and 4.49 ±4.45 pixels (maxpooling), at approximately 100 images per second. Trunk segment angles (head, neck, 6 thoraco-lumbar-pelvic segments) were estimated with error 6.4° ±2.8° allowing accurate classification (F1 > 80%) of deviation from a reference posture at thresholds up to 3°, 3°, 2° respectively. Contact between arm point features (elbow, wrist) and supporting surface was classified at F1 = 80.5%. Conclusion and Significance: This study demonstrates, for the first time, a technical solution to automate identification of i) a sitting segmental-posture including individual trunk segments, ii) changes away from that posture, and iii) support from the upper limb, required for the clinical SATCo.

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Section: B Segment Anglesmentioning

confidence: 75%

Section: B Participants Procedures and Measurementsmentioning

confidence: 99%

Section: B Participants Procedures and Measurementsmentioning

confidence: 99%

Section: B Participants Procedures and Measurementsmentioning

confidence: 99%

mentioning

confidence: 99%

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Fully automated image-based estimation of postural point-features in children with cerebral palsy using deep learning

Cunningham¹,

Sánchez²,

Butler³

et al. 2018

Preprint

Self Cite

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The feasibility of automated detection of the aligned trunk in sitting directly from raw video using a depth camera

et al. 2020

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Introduction Maintenance of an upright head and trunk ('trunk') posture is mandatory for effective performance of everyday activities. Poor trunk control is commonly seen in children with neuromotor or neuromuscular disorders, (e.g. cerebral palsy or muscular dystrophy), and in adults with neurological conditions (e.g. stroke): this leads to functional limitations, such as compromised ability to sit independently [1-3]. Detailed and reliable evaluation of trunk control status is essential when planning therapeutic interventions for these patients [2-5]. The Segmental Assessment of Trunk Control (SATCo) [4] evaluates sitting control at seven separate trunk segments, judging each segment's position in space relative to a defined, neutral aligned posture. SATCo is a validated test that is in regular clinical and research use but it remains a subjective assessment. Generating objective, automated tools that are feasible for use in a clinical environment, will positively impact the planning and delivery of therapeutic interventions for both children and adults. Research Question This study tests the feasibility of deep convolutional neural network (DCNN) analysis of raw high definition and depth (HD+D) video to identify those video frames containing the aligned, reference trunk posture.

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Cluster analysis of impairment measures to inform an evidence-based classification structure in RaceRunning, a new World Para Athletics event for athletes with hypertonia, ataxia or athetosis

Linden

Corrigan

Tennant³

et al. 2020

Journal of Sports Sciences

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RaceRunning enables athletes with limited or no walking ability to propel themselves independently using a three-wheeled frame that has a saddle, handle bars and a chest plate. For RaceRunning to be included as a para athletics event, an evidence-based classification system is required. This study assessed the impact of trunk control and lower limb impairment measures on RaceRunning performance and evaluated whether clusters analysis of these impairment measures produce a valid classification structure for RaceRunning.The Trunk Control Measurement Scale (TCMS), Selective Control Assessment of the Lower Extremity (SCALE), the Australian Spasticity Assessment Scale (ASAS), and knee extension were recorded for 26 RaceRunning athletes. Thirteen male and 13 female athletes aged 24 (SD=7) years participated. All impairment measures were significantly correlated with performance (rho=0.55-0.74). Using ASAS, SCALE, TCMS and knee extension as cluster variables in a two-step cluster analysis resulted in two clusters of athlete. Race speed and the impairment measures were significantly different between the clusters (p<0.001). The findings of this study provide evidence for the utility of the selected impairment measures in an evidence-based classification system for RaceRunning athletes.

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Working towards an objective segmental assessment of trunk control in children with cerebral palsy

Cited by 9 publications

References 15 publications

Fully automated image-based estimation of postural point-features in children with cerebral palsy using deep learning

Fully automated image-based estimation of postural point-features in children with cerebral palsy using deep learning

The feasibility of automated detection of the aligned trunk in sitting directly from raw video using a depth camera

Cluster analysis of impairment measures to inform an evidence-based classification structure in RaceRunning, a new World Para Athletics event for athletes with hypertonia, ataxia or athetosis

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