Estimation of vertical ground reaction force during running using neural network model and uniaxial accelerometer

Wearable technology has been viewed as one of the plausible alternatives to capture human motion in an unconstrained environment, especially during running. However, existing methods require kinematic and kinetic measurements of human body segments and can be complicated. This paper investigates the...

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Bibliographic Details
Published in:Journal of biomechanics 2018-07, Vol.76, p.269-273
Main Authors: Ngoh, Kieron Jie-Han, Gouwanda, Darwin, Gopalai, Alpha A., Chong, Yu Zheng
Format: Article
Language:English
Subjects:
Accelerometer
Accelerometers
Accelerometry
Adult
Algorithms
Artificial intelligence
Biomechanical Phenomena
Body weight
Computer applications
Correlation coefficients
Exercise Test
Fitness equipment
Ground Reaction Force
Human motion
Humans
Impact loads
Kinematics
Magnetic fields
Male
Neural Network
Neural networks
Neural Networks (Computer)
Running
Running - physiology
Running Gait
Sensors
Vertical forces
Walking
Wearable Electronic Devices
Wearable technology
Young Adult
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Summary:Wearable technology has been viewed as one of the plausible alternatives to capture human motion in an unconstrained environment, especially during running. However, existing methods require kinematic and kinetic measurements of human body segments and can be complicated. This paper investigates the use of neural network model (NN) and accelerometer to estimate vertical ground reaction force (VGRF). An experimental study was conducted to collect sufficient samples for training, validation and testing. The estimated results were compared with VGRF measured using an instrumented treadmill. The estimates yielded an average root mean square error of less than 0.017 of the body weight (BW) and a cross-correlation coefficient greater than 0.99. The results also demonstrated that NN could estimate impact force and active force with average errors ranging between 0.10 and 0.18 of BW at different running speeds. Using NN and uniaxial accelerometer can (1) simplify the estimation of VGRF, (2) reduce the computational requirement and (3) reduce the necessity of multiple wearable sensors to obtain relevant parameters.
ISSN:0021-9290
1873-2380
DOI:10.1016/j.jbiomech.2018.06.006