Publications Details

Dynamic Shear and Normal Force Detection in a Soft Insole Using Hybrid Optical & Piezoresistive Sensors

Mcarthur, Daniel; Branyan, Callie A.; Tansel, Derya Z.; Liu, Eric V.; Mazumdar, Anirban; Miera, Alexandria; Rittikaidachar, Michal; Spencer, Steven J.; Wood, David; Wheeler, Jason

The development of multi-axis force sensing ca-pabilities in elastomeric materials has enabled new types of human motion measurement with many potential applications. In this work, we present a new soft insole that enables mobile measurement of ground reaction forces (GRFs) outside of a lab-oratory setting. This insole is based on hybrid shear and normal force detecting (SAND) tactile elements (taxels) consisting of optical sensors optimized for shear sensing and piezoresistive pressure sensors dedicated to normal force measurement. We develop polynomial regression and deep neural network (DNN) GRF prediction models and compare their performance to ground-truth force plate data during two walking experiments. Utilizing a 4-layer DNN, we demonstrate accurate prediction of the anterior-posterior (AP), medial-lateral (ML) and vertical components of the GRF with normalized mean absolute errors (NMAE) of <5.1 %, 4.1 %, and 4.5%, respectively. We also demonstrate the durability of the hybrid SAND insole construction through more than 20,000 cycles of use.