Foot Modeling and Smart Plantar Pressure Reconstruction from Three Sensors

Abstract

In order to monitor pressure under feet, this study presents a biomechanical model of the human foot. The main elements of the foot that induce the plantar pressure distribution are described. Then the link between the forces applied at the ankle and the distribution of the plantar pressure is established. Assumptions are made by defining the concepts of a 3D internal foot shape, which can be extracted from the plantar pressure measurements, and a uniform elastic medium, which describes the soft tissues behaviour. In a second part, we show that just 3 discrete pressure sensors per foot are enough to generate real time plantar pressure cartographies in the standing position or during walking. Finally, the generated cartographies are compared with pressure cartographies issued from the F-SCAN system. The results show 0.01 daN (2% of full scale) average error, in the standing position.

Effect of Sensor Size, Number and Position under the Foot to Measure the Center of Pressure (CoP) Displacement and Total Center of Pressure (CoPT) Using an Anatomical Foot Model

“…The model establishes a relationship between posture and plantar pressure through mechanical foot characteristics. It is detailed in [ 12 ], and in summary, it takes into account three elements, shown in Figure 1 : FS—the foot seat is the location of the foot in space given by the position of the ankle and the rotations around it; IS—the internal foot shape is a surface profile that describes the non-compressive material. The internal foot shape consists of the foot skeleton covered by other rigid media, such as the ligaments and the muscles; EM—the elastic medium is the soft tissue that covers the internal foot shape.…”

“…Numerous aspects of insole monitoring systems have been discussed, including the number of sensors [ 11 ]. Simplified instrumented insoles with three to ten [ 12 ] or sixteen [ 13 ] pressure sensors per foot have been developed. Sensors are placed mainly under the heel, midfoot, and forefoot for clinical gait analysis [ 14 ] or to detect gait phases [ 15 ].…”

“…The aim of this study is to experimentally validate our method [ 12 ], which utilizes an anatomical model of the foot coupled with a learning algorithm capable of generating plantar pressure maps in a standing position with three discrete pressure sensors per foot.…”

Plantar Pressure‐Based Insole Gait Monitoring Techniques for Diseases Monitoring and Analysis: A Review

“…[ 15 ] Therefore, machine learning allows a sensing system with low sensing density to reconstruct the high‐resolution plantar pressure pattern map. [ 145,146 ]…”

“…Machine learning methods are utilized to determine the structure of the foot skeleton and elastic medium. [ 145 ] The information about the deviation of the plantar plate and the foot skeleton of the plantar pressure is obtained with the collected data from the three FSR sensors (arranged at the positions of the heel, first metatarsal, and fourth metatarsal), [ 146 ] and the pressure distribution is determined. [ 145 ] In this study, the average error is 2% of full scale with 0.1 N average error of each sensor, and the SD is 0.05 N (as shown in Figure 20b).…”