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Research Papers

Local Dynamic Joint Stability During Human Treadmill Walking in Response to Lower Limb Segmental Loading Perturbations

[+] Author and Article Information
Shawn M. Beaudette

Department of Human Health
and Nutritional Sciences,
University of Guelph,
50 Stone Road East,
Guelph, ON N1G 2W1, Canada
e-mail: sbeaudet@uoguelph.ca

Timothy A. Worden

Department of Human Health
and Nutritional Sciences,
University of Guelph,
50 Stone Road East,
Guelph, ON N1G 2W1, Canada
e-mail: tworden@uoguelph.ca

Megan Kamphuis

Department of Human Health
and Nutritional Sciences,
University of Guelph,
50 Stone Road East,
Guelph, ON N1G 2W1, Canada
e-mail: kamphuim@mail.uoguelph.ca

Lori Ann Vallis

Department of Human Health
and Nutritional Sciences,
University of Guelph,
50 Stone Road East,
Guelph, ON N1G 2W1, Canada
e-mail: lvallis@uoguelph.ca

Stephen H. M. Brown

Department of Human Health
and Nutritional Sciences,
University of Guelph,
50 Stone Road East,
Guelph, ON N1G 2W1, Canada
e-mail: shmbrown@uoguelph.ca

1Corresponding author.

Manuscript received January 20, 2015; final manuscript received June 15, 2015; published online July 14, 2015. Assoc. Editor: Tammy L. Haut Donahue.

J Biomech Eng 137(9), 091006 (Sep 01, 2015) (7 pages) Paper No: BIO-15-1026; doi: 10.1115/1.4030944 History: Received January 20, 2015; Revised June 15, 2015; Online July 14, 2015

Our purpose was to quantify changes in local dynamic stability (LDS) of the lumbar spine, hip, knee, and ankle in response to changes in lower limb segment mass, as well as to quantify temporal adaptations to segment loading during treadmill walking. Results demonstrate that increased mass distal to a joint yields either the maintenance of, or increased stabilization of, that particular joint relative to the unloaded condition. Increased mass proximal to a particular joint resulted in joint destabilization. The hip and ankle LDS were observed to change temporally, independent of segment loading condition, suggesting adaptation to walking on a treadmill interface.

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Figures

Grahic Jump Location
Fig. 2

The spatial effect of each segment inertial loading condition during treadmill walking on estimates of (a) lumbar spine, (b) hip, (c) knee, and (d) ankle LDS (λmax-m). All plots show arithmetic mean ± SEM. Note that higher LDS values indicate lower stability. Asterisks show statistical significance (p < 0.05).

Grahic Jump Location
Fig. 3

The temporal effect of each segment inertial loading condition during treadmill walking on estimates of (a) lumbar spine, (b) hip, (c) knee, and (d) ankle LDS (λmax-m). All plots show mean (±SEM) calculated across all participants for each loading condition. Note that higher LDS values indicate lower stability. Asterisks show statistical significance (p < 0.05) between E1 versus E2–E5 (hip) and E1 versus E4 (ankle).

Grahic Jump Location
Fig. 1

Schematic depiction of the angular joint time series data processing steps used in the estimation of lumbar spine, hip (shown), knee, and ankle LDS. Steps outline the acquisition of angular joint time series: (a) the shift of each time series (α, β, and γ) into positive Cartesian space, (b) the calculation of the Euclidean norm (N) from each 3D joint dataset, (c) the reconstruction into multidimensional state space using time-delays (TD) (five embedding dimensions (dE) were used for the hip, however, only three shown here), and (d) creation of an average maximal logarithmic divergence ({lndj(i)}) curve for each respective joint to attain estimates of λmax-s, λmax-m, and λmax-l.

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