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Research Papers

Shoe–Floor Interactions in Human Walking With Slips: Modeling and Experiments

[+] Author and Article Information
Mitja Trkov

Department of Mechanical and
Aerospace Engineering,
Rutgers University,
Piscataway, NJ 08854
e-mail: m.trkov@utah.edu;
mitja.trkov@rutgers.edu

Jingang Yi

Fellow ASME
Department of Mechanical and
Aerospace Engineering,
Rutgers University,
Piscataway, NJ 08854
e-mail: jgyi@rutgers.edu

Tao Liu

School of Mechanical Engineering,
Zhejiang University Hangzhou,
Zhejiang 310027, China
e-mail: liutao@zju.edu.cn

Kang Li

Department of Industrial and
Systems Engineering,
Rutgers University,
Piscataway, NJ 08854
e-mail: kl419@rci.rutgers.edu

1Present address: Department of Mechanical Engineering, University of Utah, Salt Lake City, UT 84112.

2Corresponding author.

Manuscript received April 21, 2017; final manuscript received September 4, 2017; published online January 17, 2018. Assoc. Editor: Tammy L. Haut Donahue.

J Biomech Eng 140(3), 031005 (Jan 17, 2018) (11 pages) Paper No: BIO-17-1164; doi: 10.1115/1.4038251 History: Received April 21, 2017; Revised September 04, 2017

Shoe–floor interactions play a crucial role in determining the possibility of potential slip and fall during human walking. Biomechanical and tribological parameters influence the friction characteristics between the shoe sole and the floor and the existing work mainly focus on experimental studies. In this paper, we present modeling, analysis, and experiments to understand slip and force distributions between the shoe sole and floor surface during human walking. We present results for both soft and hard sole material. The computational approaches for slip and friction force distributions are presented using a spring-beam networks model. The model predictions match the experimentally observed sole deformations with large soft sole deformation at the beginning and the end stages of the stance, which indicates the increased risk for slip. The experiments confirm that both the previously reported required coefficient of friction (RCOF) and the deformation measurements in this study can be used to predict slip occurrence. Moreover, the deformation and force distribution results reported in this study provide further understanding and knowledge of slip initiation and termination under various biomechanical conditions.

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Figures

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Fig. 3

A schematic of the shoe–floor contact and force distributions

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Fig. 2

(a) Laser-based contour footprint setup. Validation results of laser-based contour measurements by a (b) spherical regular object and (c) concave irregular object. (d) The outcomes of the contact contour, sole surface dots (squares ◻) and landmarks (diamonds ⋄) detection on a snapshot during self-selected walking gait.

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Fig. 1

(a) The slip and fall experimental setup with various sensor suites and (b) instrumented shoe kinematics/kinetics/forces distribution sensing suite

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Fig. 7

Comparison results of the COP trajectory during the normal walking gait by various sensor measurements and the normal force model. (a) Evolution of the COP in the x-axis direction versus the percentage of stance. (b) Evolution of the COP in the y-axis direction versus the percentage of stance. (c) The COP trajectory comparisons in the shoe frame.

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Fig. 4

Schematic of the lateral force distribution calculation: (a) flexible PSECR sensor array and calculation configuration and (b) cross section view of the normal force measurements along section Cx

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Fig. 5

A schematic of the hybrid beam-spring network model to capture the shoe sole–floor interactions

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Fig. 6

Evolution of the laser-based contact contour and detected dots inside the contour (a) 10% of the stance, (b) 25% of the stance, (c) 50% of the stance, (d) 75% of the stance, and (e) 90% of the stance during subject's regular walking gait

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Fig. 8

Experimental (a)–(c) and simulation (d)–(f) results of the distributions of the normal load Fn. Experimental (g)–(i) and simulation (j)–(l) results of a hard rubber deformations δ magnified by 20×, and experimental (m)–(o) and simulation (p)–(r) results of a soft rubber deformations δ magnified by 10×. Results are presented for 12%, 46%, and 92% of stance (S). Units of the normal load distributions are N/mm2.

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Fig. 10

Measured deformations at (a) 50 ms and (b) 60 ms from the beginning of stance, until slip happens at (c) 70 ms after initial contact. All deformations are magnified by 20 times.

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Fig. 11

Results right before slip occurs: (a) computed deformation distribution, (b) measured normal load fn(x, y), (c) computed longitudinal friction force distribution fx(x, y), and (d) computed lateral friction force distribution fy(x, y). Results right after slip occurs: (e) computed deformation distribution, (f) measured normal load fn(x, y), (g) computed longitudinal friction force distribution fx(x, y), and (h) computed lateral friction force distribution fy(x, y). The unit for force distributions is N/mm2.

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Fig. 12

(a) Forces during regular walking gait and walking with foot slip gait plotted with respect to the regular walking stance (S). GRFs from the experiments and model predictions for (b) regular no slip walking and (c) slip-and-stop walking gaits. The curves of “Fn Exp” and “Fn Sim” coincide with each other since we intentionally use the normal forces measured in experiments in the computation.

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Fig. 9

(a) RCOF from the smart shoe sensors versus the stance for normal walking, delayed-slip and slip-and-stop gaits. (b) ROCF for the delayed-slip over the whole step. (c) Comparison of the RCOF for the delayed-slip, slip-and-stop and the estimated RCOF for the slip-and-stop gait if using the values of the normal force from nonslip walking gait. Marked are instances of the slip initiation for the delayed-slip gait and the slip termination during the slip-and-stop gait.

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