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Research Papers

Inexpensive Vision-Based System for the Direct Measurement of Ankle Stiffness During Quiet Standing

[+] Author and Article Information
Raul Chavez-Romero

Unidad Académica de Ingeniería I,
Programa de Ingeniería Mecánica,
Universidad Autónoma de Zacatecas,
Jardín Juárez #147,
Zacatecas 98000, México
e-mail: rchavez@uaz.edu.mx

Antonio Cardenas

Facultad de Ingeniería,
Centro de Investigación y Estudios de Posgrado,
Universidad Autónoma de San Luis Potosí,
Avenue Dr. Manuel Nava #9,
San Luis Potosí 78290, México
e-mail: antonio.cardenas@uaslp.mx

Juan Manuel Rendon-Mancha

Departamento de Computación,
Universidad Autónoma del Estado de Morelos,
Avenue Universidad #1001,
Cuernavaca, Morelos 62209, México
e-mail: rendon@uaem.mx

Karinna M. Vernaza

Department of Mechanical Engineering,
Gannon University,
109 University Square,
Erie, PA 16541-0001
e-mail: vernaza001@gannon.edu

Davide Piovesan

Biomedical Engineering Program,
Department of Mechanical Engineering,
Gannon University,
109 University Square, PMB 3251,
Erie, PA 16541-0001
e-mail: piovesan001@gannon.edu

1Corresponding author.

Manuscript received October 10, 2014; final manuscript received July 9, 2015; published online August 12, 2015. Assoc. Editor: Venketesh Dubey.

J. Med. Devices 9(4), 041011 (Aug 12, 2015) (8 pages) Paper No: MED-14-1250; doi: 10.1115/1.4031060 History: Received October 10, 2014

We created a sensor-fusion suite for the acquisition of biometric information that can be used for the estimation of human control strategy in a variety of everyday tasks. This work focuses on the experimental validation of the integrated motion capture subsystem based on raster images. Understanding human control strategies utilized in everyday activity requires measurement of several variables that can be grouped as kinematic, dynamic, and biological-feedback variables. Hence, there is a strong need for the acquisition, analysis, and synchronization of the information measured by a variety of transducers. Our system was able to capture the complex dynamics of a flexible robot by means of two inexpensive web cameras without compromising accuracy. After validating the vision system by means of the robotic device, a direct measure of the center of gravity (COG) position during the recovery from a fall was performed on two groups of human subjects separated by age. The instrumental setup was used to estimate how ankle operational stiffness changes as function of age. The results indicate a statistical increase of stiffness for the older group.

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Figures

Grahic Jump Location
Fig. 1

Settings for calibrations pattern for estimation of vision parameters

Grahic Jump Location
Fig. 2

(a) Segmental model of human standing, (b) analytical schematic of robot, and (c) human and robotic device proportions

Grahic Jump Location
Fig. 3

Phase A—quiet standing, phase B—hold, phase C—release with consequence acceleration of the center of mass forward, and phase D—recoil where fall is prevented and quiet posture is regained as in condition A

Grahic Jump Location
Fig. 4

Block diagram of positional step response from phase D back to phase A

Grahic Jump Location
Fig. 5

Tracking angle from vision system (gray) and tracked angle of a pendulum using the robot's encoder (dashed)

Grahic Jump Location
Fig. 6

Torque trajectory at the ankle and angular displacement of the COG for the experiment on one unimpaired individual. (Top panel) measurement from force plate of the total torque at the ground. (Bottom panel) position of the COG as tracked by the vision system. Shaded area indicates phases B and C from Fig 3. Nonshaded area represents the transient in the recovery from fall between phase D and phase A in Fig. 3. Td is the period of oscillation of the COG before system stabilization; θmax is the maximum angular deviation during phase D; θf is the desired angle at equilibrium in phase A; and θ0 is the position at the beginning of phase D.

Grahic Jump Location
Fig. 7

Comparison of ankle stiffness in two age groups

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