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Research Papers

The Effects of Aging and Dual Tasking on Human Gait Complexity During Treadmill Walking: A Comparative Study Using Quantized Dynamical Entropy and Sample Entropy

[+] Author and Article Information
Samira Ahmadi

Department of Mechanical Engineering,
University of Manitoba,
Room E1-451 EITC,
15 Gillson Street,
Winnipeg, MB R3T 5V6, Canada
e-mail: ahmadis3@myumanitoba.ca

Christine Wu

Fellow ASME
Department of Mechanical Engineering,
University of Manitoba,
Room E2-327 Engineering Information
and Technology Complex,
75A Chancellors Circle,
Winnipeg, MB R3T 5V6, Canada
e-mail: christine.wu@umanitoba.ca

Nariman Sepehri

Fellow ASME
Department of Mechanical Engineering,
University of Manitoba,
Room E2-327 Engineering Information
and Technology Complex,
75A Chancellors Circle,
Winnipeg, MB R3T 5V6, Canada
e-mail: nariman.sepehri@umanitoba.ca

Anuprita Kantikar

College of Rehabilitation Sciences,
University of Manitoba,
R106-771 McDermot Avenue,
Winnipeg, MB R3E 0T6, Canada
e-mail: anuprita.kan@gmail.com

Mayur Nankar

College of Rehabilitation Sciences,
University of Manitoba,
R106-771 McDermot Avenue,
Winnipeg, MB R3E 0T6, Canada
e-mail: mayur5522@gmail.com

Tony Szturm

Department of Physical Therapy,
College of Rehabilitation Sciences,
University of Manitoba,
R106-771 McDermot Avenue,
Winnipeg, MB R3E 0T6, Canada
e-mail: tony.szturm@umanitoba.ca

1Corresponding author.

Manuscript received December 13, 2016; final manuscript received September 13, 2017; published online October 31, 2017. Assoc. Editor: Kenneth Fischer.

J Biomech Eng 140(1), 011006 (Oct 31, 2017) (10 pages) Paper No: BIO-16-1514; doi: 10.1115/1.4037945 History: Received December 13, 2016; Revised September 13, 2017

Quantized dynamical entropy (QDE) has recently been proposed as a new measure to quantify the complexity of dynamical systems with the purpose of offering a better computational efficiency. This paper further investigates the viability of this method using five different human gait signals. These signals are recorded while normal walking and while performing secondary tasks among two age groups (young and older age groups). The results are compared with the outcomes of previously established sample entropy (SampEn) measure for the same signals. We also study how analyzing segmented and spatially and temporally normalized signal differs from analyzing whole data. Our findings show that human gait signals become more complex as people age and while they are cognitively loaded. Center of pressure (COP) displacement in mediolateral direction is the best signal for showing the gait changes. Moreover, the results suggest that by segmenting data, more information about intrastride dynamical features are obtained. Most importantly, QDE is shown to be a reliable measure for human gait complexity analysis.

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Figures

Grahic Jump Location
Fig. 1

Segmentation and normalization of: (a) AP COP-D; (b) ML COP-D

Grahic Jump Location
Fig. 2

Segmentation and normalization of: (a) ML Shank-AV; (b) ML Trunk-LA; (c) AP Trunk-LA

Grahic Jump Location
Fig. 3

Data length convergence test for various range of template sizes m; ML Trunk-LA of older age group was used

Grahic Jump Location
Fig. 4

Group means and SEM of SampEn (unitless) of whole data along with the results of statistical analysis (F-statistics and p-value) representing age and dual task effects: (a) ML COP-D, (b) AP COP-D, (c) ML Trunk-LA, (d) AP Trunk-LA, and (e) ML shank-AV

Grahic Jump Location
Fig. 5

Group means and SEM of QDE (unitless) of whole data along with the results of statistical analysis (F-statistics and p-value) representing age and dual task effects: (a) ML COP-D, (b) AP COP-D, (c) ML Trunk-LA, (d) AP Trunk-LA, and (e) ML shank-AV

Grahic Jump Location
Fig. 6

Group means and SEM of SampEn (unitless) of segmented data along with the results of statistical analysis (F-statistics and p-value) representing age and dual task effects: (a) ML COP-D, (b) AP COP-D, (c) ML Trunk-LA, (d) AP Trunk-LA, and (e) ML shank-AV

Grahic Jump Location
Fig. 7

Group means and SEM of QDE (unitless) of segmented data along with the results of statistical analysis (F-statistics and p-value) representing age and dual task effects: (a) ML COP-D, (b) AP COP-D, (c) ML Trunk-LA, (d) AP Trunk-LA, and (e) ML shank-AV

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