Prediction of ground reaction forces in level and incline/decline walking from a multistage analysis of plantar pressure data

• Published in: Journal of biomechanics 2019-02, Vol.84, p.46-51
• Main Authors: Wei, Feng, Crechiolo, Andrew, Haut, Roger C.
• Format: Article
• Language: English
• Subjects: Accuracy; Adult; Biomechanical engineering; Biomechanical Phenomena; Biomechanics; Calibration; Data collection; Female; Foot - physiology; Footwear; Gait; Ground reaction force; Humans; Inclination; Incline/decline walking; Injury analysis; Injury prevention; Insoles; Laboratories; Male; Mechanical Phenomena; Methods; Motion capture; Multistage; Plantar pressure; Predictions; Pressure; Pressure sensors; Prostheses; Regression analysis; Regression model; Regression models; Root-mean-square errors; Sensors; Shoes; Shoes & boots; Walking; Walkways; Weight-Bearing; Young Adult
• ISSN: 0021-9290; 1873-2380
• DOI: 10.1016/j.jbiomech.2018.12.015

Knowing the ground reaction forces (GRFs) during walking has various biomechanical applications in injury prevention, gait analysis, as well as prosthetic and footwear design. The current study presents a method for predicting the GRFs in level and incline/decline walking that may be used in various outdoor biomechanics studies geared towards the above applications. The method was developed to predict the complete set of GRFs at walking inclinations of 0°, ±5°, ±10°, ±15°, and ±20°. Plantar pressure insoles were used to obtain inclination-specific, linear regression models based on three periods of gait stance phase, and the model-determined GRFs were compared with those measured from a forceplate. The three periods were determined based on the observed shifting of load-bearing insole sensors from heel to forefoot during walking, i.e., heel-strike, midstance, and toe-off. Six subjects wearing minimalist shoes fitted with plantar pressure insoles containing 99 pressure sensors performed ten walking trials at each of the aforementioned inclinations on an adjustable ramp with an embedded forceplate. Data from contact of the instrumented shoes with the forceplate were used to create linear regressions to transform insole pressure data into a complete set of GRFs. The root mean square error (RMSE) over peak recorded values were on average 10%, 3%, 21% for level walking and 11%, 4%, 23% for ramp walking in the respective anteroposterior, vertical, and mediolateral directions. The multistage linear regression model developed in the current study may be an acceptable option for estimating GRFs during walking in various environments without the restraint of a forceplate.