Research Papers

A Mobile Motion Capture System Based on Inertial Sensors and Smart Shoes

[+] Author and Article Information
Pyeong-Gook Jung

e-mail: pgjung@sogang.ac.kr

Sehoon Oh

Research Professor
e-mail: sehoon74@sogang.ac.kr
Department of Mechanical Engineering,
Sogang University,
Seoul 121-742, Korea

Gukchan Lim

Chief Research Engineer
Mobile Handset R&D Center,
LG Electronics,
Seoul 153-801, Korea
e-mail: gukchan.lim@lge.com

Kyoungchul Kong

Assistant Professor
Department of Mechanical Engineering,
Sogang University,
Seoul 121-742, Korea
e-mail: kckong@sogang.ac.kr

1Corresponding author.

Contributed by the Dynamic Systems Division of ASME for publication in the JOURNAL OF DYNAMIC SYSTEMS, MEASUREMENT, AND CONTROL. Manuscript received November 23, 2012; final manuscript received August 1, 2013; published online August 30, 2013. Assoc. Editor: Sergey Nersesov.

J. Dyn. Sys., Meas., Control 136(1), 011002 (Aug 30, 2013) (9 pages) Paper No: DS-12-1388; doi: 10.1115/1.4025207 History: Received November 23, 2012; Revised August 01, 2013

Motion capture systems play an important role in health-care and sport-training systems. In particular, there exists a great demand on a mobile motion capture system that enables people to monitor their health condition and to practice sport postures anywhere at any time. The motion capture systems with infrared or vision cameras, however, require a special setting, which hinders their application to a mobile system. In this paper, a mobile three-dimensional motion capture system is developed based on inertial sensors and smart shoes. Sensor signals are measured and processed by a mobile computer; thus, the proposed system enables the analysis and diagnosis of postures during outdoor sports, as well as indoor activities. The measured signals are transformed into quaternion to avoid the Gimbal lock effect. In order to improve the precision of the proposed motion capture system in an open and outdoor space, a frequency-adaptive sensor fusion method and a kinematic model are utilized to construct the whole body motion in real-time. The reference point is continuously updated by smart shoes that measure the ground reaction forces.

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Fig. 2

Position vectors for constructing the whole body motion; (a) a neutral posture, (b) an arbitrary posture. In (b), (k) is omitted for some vectors for the simplicity of representation

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Fig. 1

Orientation of a body segment; (a) quaternion, (b) roll (θ), pitch (ϕ), and yaw (ψ)

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Fig. 3

A set of an IMU and a data receiver; (a) an IMU, (b) a wireless data receiver. In (a), a quarter coin is shown for comparison of the size

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Fig. 4

Sensor installation position and orientation; (a) gyroscope and (b) accelerometer

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Fig. 5

The block diagram of sensor fusion

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Fig. 6

A cut-off frequency with respect to the acceleration magnitude

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Fig. 7

A smart shoe for detecting reference points; (a) a smart shoe, (b) the insole of the smart shoe

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Fig. 8

A scene of experiments; (a) a human subject and (b) a captured motion

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Fig. 10

Ground reaction forces and motion phases

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Fig. 11

Captured motions; (a) a sitting state, (b) sit-to-stand, and (c) walking

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Fig. 12

An experimental setup for performance verification

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Fig. 9

Sensor fusion results for one-dimensional test; (a) pitch angle and (b) acceleration magnitude

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Fig. 13

Experimental results; (a) slow motion and (b) fast motion

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Fig. 14

An evaluation methods of the proposed motion capture system



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