Predicting vertical and shear ground reaction forces during walking and jogging using wearable plantar pressure insoles

Maryam Hajizadeh; Allison L Clouthier; Marshall Kendall; Ryan B Graham

doi:10.1016/j.gaitpost.2023.06.006

Predicting vertical and shear ground reaction forces during walking and jogging using wearable plantar pressure insoles

Gait Posture. 2023 Jul:104:90-96. doi: 10.1016/j.gaitpost.2023.06.006. Epub 2023 Jun 9.

Authors

Maryam Hajizadeh¹, Allison L Clouthier², Marshall Kendall³, Ryan B Graham²

Affiliations

¹ Spine & Movement Biomechanics Lab, Faculty of Health Sciences, University of Ottawa, Ottawa, ON, Canada. Electronic address: maryam.hajizadeh44@gmail.com.
² Spine & Movement Biomechanics Lab, Faculty of Health Sciences, University of Ottawa, Ottawa, ON, Canada.
³ Independent researcher, Ottawa, ON, Canada.

PMID: 37348185
DOI: 10.1016/j.gaitpost.2023.06.006

Abstract

Background: The development of plantar pressure insoles has made them a potential replacement for force plates. These wearable devices can measure multiple steps and might be used outside of the lab environment for rehabilitation and evaluation of sport performance. However, they can only measure the vertical force which does not completely represent the vertical ground reaction force. In addition, they are not able to measure shear forces which play an import role in the dynamic performance of individuals. Indirect approaches might be implemented to improve the accuracy of the force estimated by plantar pressure systems.

Research question: The aim of this study was to predict the vertical and shear components of ground reaction force from plantar pressure data using recurrent neural networks.

Methods: Ground reaction force and plantar pressure data were collected from 16 healthy individuals during 10 trials of walking and five trials of jogging using Bertec force plates at 1000 Hz and FScan plantar pressure insoles at 100 Hz. A long short-term memory neural network was built to consider the time dependency of pressure and force data in predictions. The data were split into three subsets of train, to train the model, evaluate, to optimize the model hyperparameters, and test sets, to assess the accuracy of the model predictions.

Results: The results of this study showed that our long short-term memory model could accurately predict the shear and vertical force components during walking and jogging. The predictions were more accurate during walking compared to jogging. In addition, the predictions of mediolateral force had higher error and lower correlation compared to vertical and anteroposterior components.

Significance: The long short-term memory model developed in this study may be an acceptable option for accurate estimation of ground reaction force during outdoor activities which can have significant impacts in rehabilitation, sport performance, and gaming.

Keywords: Force; Long short-term memory neural network; Plantar pressure insoles; Wearable devices.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Biomechanical Phenomena
Gait
Humans
Jogging*
Pressure
Shoes
Walking
Wearable Electronic Devices*