Simultaneous estimation of effects of gender, age and walking speed on kinematic gait data

Røislien, Jo; Skare, Øivind; Gustavsen, Marit; Broch, Nana Lise; Rennie, Linda; Opheim, Arve

doi:10.1016/j.gaitpost.2009.07.002

Cited by 76 publications

(78 citation statements)

References 15 publications

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“…Functional data analysis is an emerging topic in statistics. It has been successfully used in the medical field for the classification of temporal gene expression data [26,27] and for the analysis of normal and pathological kinematic gait data [28][29][30].…”

Section: Introductionmentioning

confidence: 99%

Multilevel Analysis of Trunk Surface Measurements for Noninvasive Assessment of Scoliosis Deformities

Séoud

Dansereau

Cheriet

2012

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Study design: Reliability study. Objectives:To assess between-acquisition reliability of new multi-levels trunk cross-sections measurements, in order to define what is a real change when comparing two trunk surface acquisitions of a same patient, before and after surgery or throughout the clinical monitoring.Summary of background data: Several cross-sectional surface measurements have been proposed in the literature for non-invasive assessment of trunk deformity in patients with adolescent idiopathic scoliosis (AIS). However, only the maximum values along the trunk are evaluated and used for monitoring progression and assessing treatment outcome. Methods:Back surface rotation (BSR), trunk rotation (TR) and coronal and sagittal trunk deviation are computed on 300 cross-sections of the trunk. Each set of 300 measures is represented as a single functional data using a set of basis functions. To evaluate betweenacquisition variability at all trunk levels, a test-retest reliability study is conducted on 35 AIS patients. A functional correlation analysis is also carried out to evaluate any redundancy between the measurements.Results: Each set of 300 measures was successfully described using only 10 basis functions.The test-retest reliability of the functional measurements is good to very good all over the trunk, except above the shoulders levels. The typical errors of measurement are between 1.20 and 2.2 for the rotational measures and between 2 to 6 mm for deviation measures. There is a very strong correlation between BSR and TR all over the trunk, a moderate correlation between coronal trunk deviation and both BSR and TR and no correlation between sagittal trunk deviation and any other measurement. Conclusions:This novel representation of trunk surface measurements allows for a global assessment of trunk surface deformity. Multilevel trunk measurements provide a broader perspective of the trunk deformity and allow a reliable multi-level monitoring during clinical follow-up of patients with AIS and a reliable assessment of the esthetic outcome after surgery. KEY WORDS:Scoliosis, multi-level trunk surface measurement, functional data analysis, reliability, correlation. KEY POINTS:1) Trunk cross-sections measurements are reliable all along the trunk, except at the shoulders level.2) The maximum value of a measurement along the trunk overlooks the extent of the hump above and below the apex of the curve. MINI ABSTRACT / PRÉCIS:This study assesses the reliability of multi-levels trunk cross-sections measurements and defines the difference needed between trunk surface acquisitions to detect a real progression and/or surgical correction of trunk surface deformity at all trunk levels.

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Section: Introductionmentioning

confidence: 99%

Multilevel Analysis of Trunk Surface Measurements for Noninvasive Assessment of Scoliosis Deformities

Séoud

Dansereau

Cheriet

2012

Spine

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“…Although representing motion data as a set of functions is not new to biomechanics analyses (Chester & Wrigley, 2008;Deluzio & Astephen, 2007;Landry et al, 2007;Lee et al, 2009;Sadeghi et al, 2002;2000), statistical methods developed specifically for analyzing these functions have not been available. More recently, FDA methods have been used within biomechanics to study mastication (Crane et al, 2010), back pain (Page et al, 2006), as well as age, gender, and speed effects on walking (Røislien et al, 2009). Given the interest in and need for treating motion data as functions, it is important that methods for analyzing a set a functions using emerging statistical methods are brought to the attention of those in the biomechanics community.…”

Section: Introductionmentioning

confidence: 99%

Functional Data Analysis for Biomechanics

Crane¹,

Childers²,

Gerstner³

et al. 2011

Theoretical Biomechanics

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“…[1][2][3][4][5][6] However, internationally, there is a dearth of reference data pertaining to normal, injury-free population groups.…”

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confidence: 99%

Dynamic plantar pressure proles of South African university students

2015

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Background. Footscan technology allows for assessment of injury risk and walking mechanics, yet there is a dearth of normative data pertaining to the normal, injury-free foot in a South African (SA) context. Objective. To generate normative tables from plantar pressure pro le data gathered from students at an SA university. Methods. e RS Footscan (RSscan, Belgium) system was used to measure the plantar pressure values of the foot. Ten anatomical landmarks of the foot were analysed: the hallux, lateral toes, ve metatarsal heads, midfoot, and medial and lateral heel. ese ten areas were grouped into one of three regions: forefoot, midfoot and heel. A total of 180 participants were subdivided as follows: gender (males, n=90; females, n=90); race (black, n=60; white, n=60; coloured, n=60). Each race group comprised 30 males and 30 females. Results. Of the ten individual plantar pressure areas, the second and third metatarsal heads demonstrated the highest mean peak plantar pressure values. Of the three regions, the heel region was ascribed with the largest plantar pressure values. Black females, coloured males and coloured females yielded the highest pressure values, especially under the midfoot region of the foot. Black and white males and white females exhibited the lowest pressure under the foot, especially under the midfoot region. Conclusion. e plantar pressure pro le data generated in this study could serve to provide clinicians with a frame of reference when evaluating participants within the age range of 18 -30 years.

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Simultaneous estimation of effects of gender, age and walking speed on kinematic gait data

Cited by 76 publications

References 15 publications

Multilevel Analysis of Trunk Surface Measurements for Noninvasive Assessment of Scoliosis Deformities

Multilevel Analysis of Trunk Surface Measurements for Noninvasive Assessment of Scoliosis Deformities

Functional Data Analysis for Biomechanics

Dynamic plantar pressure proles of South African university students

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