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Technical Brief

Vertical Jump Height Estimation Algorithm Based on Takeoff and Landing Identification Via Foot-Worn Inertial Sensing

[+] Author and Article Information
Jianren Wang

State Key Laboratory of Mechanical System and Vibration,
School of Mechanical Engineering,
Shanghai Jiao Tong University,
800 Dongchuan Road,
Shanghai 200240, China
e-mail: wangxingyuan@sjtu.edu.cn

Junkai Xu

State Key Laboratory of Mechanical System and Vibration,
School of Mechanical Engineering,
Shanghai Jiao Tong University,
800 Dongchuan Road,
Shanghai 200240, China
e-mail: abcyxjk@sjtu.edu.cn

Peter B. Shull

State Key Laboratory of Mechanical System and Vibration,
School of Mechanical Engineering,
Shanghai Jiao Tong University,
800 Dongchuan Road,
Shanghai 200240, China
e-mail: pshull@sjtu.edu.cn

1Corresponding author.

Manuscript received December 10, 2016; final manuscript received November 19, 2017; published online January 23, 2018. Assoc. Editor: Paul Rullkoetter.

J Biomech Eng 140(3), 034502 (Jan 23, 2018) (7 pages) Paper No: BIO-16-1508; doi: 10.1115/1.4038740 History: Received December 10, 2016; Revised November 19, 2017

Vertical jump height is widely used for assessing motor development, functional ability, and motor capacity. Traditional methods for estimating vertical jump height rely on force plates or optical marker-based motion capture systems limiting assessment to people with access to specialized laboratories. Current wearable designs need to be attached to the skin or strapped to an appendage which can potentially be uncomfortable and inconvenient to use. This paper presents a novel algorithm for estimating vertical jump height based on foot-worn inertial sensors. Twenty healthy subjects performed countermovement jumping trials and maximum jump height was determined via inertial sensors located above the toe and under the heel and was compared with the gold standard maximum jump height estimation via optical marker-based motion capture. Average vertical jump height estimation errors from inertial sensing at the toe and heel were −2.2±2.1 cm and −0.4±3.8 cm, respectively. Vertical jump height estimation with the presented algorithm via inertial sensing showed excellent reliability at the toe (ICC(2,1)=0.98) and heel (ICC(2,1)=0.97). There was no significant bias in the inertial sensing at the toe, but proportional bias (b=1.22) and fixed bias (a=10.23cm) were detected in inertial sensing at the heel. These results indicate that the presented algorithm could be applied to foot-worn inertial sensors to estimate maximum jump height enabling assessment outside of traditional laboratory settings, and to avoid bias errors, the toe may be a more suitable location for inertial sensor placement than the heel.

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Copyright © 2018 by ASME
Topics: Algorithms , Sensors
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Figures

Grahic Jump Location
Fig. 1

(a) Toe theoretical vertical acceleration profile and (b) heel theoretical vertical acceleration profile for a vertical jump for a 75 kg healthy male subject based on the OpenSim Sky Higher: Dynamic Optimization of Maximum Jump Height [42]. Vertical upward position and acceleration are positive.

Grahic Jump Location
Fig. 2

Vertical jump height estimation algorithm. Peak detection is performed to find maximum acceleration values and, in turn, to determine takeoff and landing phases. Distinct acceleration characteristics within the takeoff and landing phases are used to estimate specific takeoff and landing times. Finally, vertical jump height is estimated via flight-time equations.

Grahic Jump Location
Fig. 3

Two electronic modules were used with a standard running shoe to capture foot acceleration profiles during vertical jumping. One module was taped to the top of the shoe above the head of the second metatarsal, and one module was inserted in the sole of the shoe under the heel. Each electronic module consisted of a microcontroller, nine-axis inertial measurement unit, 500 mAh lithium-ion battery, and microSD card.

Grahic Jump Location
Fig. 4

Linear regression of jump height estimation between inertial and optical motion capture sensing at the (left) toe and (right) heel. r is the correlation coefficient, a is the regression intercept, and b is the regression slope.

Grahic Jump Location
Fig. 5

(a) Typical trial for toe theoretical and actual vertical acceleration profiles. (b) Typical trial for heel theoretical and actual vertical acceleration profiles.

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