Gait and Dynamic Balance Sensing Using Wearable Foot Sensors

• Published in: IEEE transactions on neural systems and rehabilitation engineering Vol. 27; no. 2; pp. 218 - 227
• Main Authors: Mohamed Refai, Mohamed Irfan, van Beijnum, Bert-Jan F., Buurke, Jaap H., Veltink, Peter H.
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.02.2019; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adult; Algorithms; Ambulatory sensing; Balance; Biomechanical Phenomena; dynamic balance; Dynamic stability; Estimation; extrapolated center of mass; Feet; Female; Foot; Foot - physiology; Gait; Geriatrics; Healthy Volunteers; Humans; Inertial platforms; Inertial sensing devices; Legged locomotion; Male; margin of stability; Multiple sclerosis; Older people; Postural Balance; Pressure; Pressure sensors; Regression analysis; Regression models; Remote monitoring; Remote sensing; Remote sensors; Reproducibility of Results; Sensors; Shoes; Task analysis; Three dimensional models; Three-dimensional displays; Ultrasonic imaging; Walking; Wearable Electronic Devices; wearable sensors; Wearable technology; Young Adult
• ISSN: 1534-4320; 1558-0210; 1558-0210
• DOI: 10.1109/TNSRE.2018.2885309

Remote monitoring of gait performance offers possibilities for objective evaluation and tackling impairment in motor ability, gait, and balance in populations, such as elderly, stroke, multiple sclerosis, and Parkinson's. This requires a wearable and unobtrusive system capable of estimating ambulatory gait and balance measures, such as the extrapolated center of mass (XCoM) and dynamic margin of stability. These estimations require the knowledge of 3-D forces and moments (F&M) and accurate foot positions. Though an existing ambulatory gait and balance system (AGBS) consisting of 3-D F&M sensors and inertial measurement units can be used for the purpose, it is bulky and conspicuous. Resistive pressure sensors were investigated as an alternative to the onboard 3-D F&M sensors. Subject-specific regression models were built to estimate 3-D F&M from 1-D plantar pressures. The model was applicable for different walking speeds. Different pressure sensor configurations were studied to optimize the system complexity and accuracy. Using resistive sensors only under the toe and heel, we were able to estimate the XCoM with a mean absolute rms error of 2.2±0.3 cm in the walking direction while walking at a preferred speed, when compared to the AGBS. For the same case, the XCoM was classified as ahead or behind the base of support correctly at 97.7±1.7%. In conclusion, this paper shows that pressure sensors, minimally under the heel and toe, offer a lightweight and inconspicuous alternative for F&M sensing, toward estimating ambulatory gait and dynamic balance.