Development of a Low-Profile Planar Sensor for the Detection of Normal and Shear Forces

Abstract

Individuals with balance and mobility problems might benefit by the use of devices that detect small changes in ground reaction forces and potentially be used to assist movement. For maximum effectiveness, such sensors must measure pressure in all three dimensions. Impact and shear plantar force are essential variables in inverse dynamics reconstructions of the human joint force. Various force sensors have been proposed to monitor plantar forces of the human foot. Most of them have a single-axis measurement, and few are intended for monitoring normal and shear stress. This article proposes a low-cost, biocompatible triaxial piezoresistive sensor developed using simple fabrication techniques and inexpensive machinery. The sensor can detect pressures from 0-800kPa with high response and recovery with minimum hysteresis and repeatable results of over than 100 cycles.

Insole Systems for Disease Diagnosis and Rehabilitation: A Review

“…Fitting [40] Values of four FSRs are used to calculate the resistance in x, y, z axes through fitting equations Sensitivity: 375 kPa/V Sensing range: 0-800 kPa Compressive Sensing [41] Stage 1: OMP Stage 2: LASSO RMSE of peak pressure <6.7 kPa When number of sensors = 4 Larger sensors produced lower RMSE Compressive Sensing [42] Stage 1: obtain separate PSD images Stage 2: Topelitz measurement matrix Reconstructing accuracy: 97.76%…”

“…Continuous plantar pressure map When the K of GMM equals to 10 RMSE of pressure <14kpa Table 1. Brief information of pressure reconstruction research in Section 3 [19,[38][39][40][41][42][43][44][45][46].…”

“…Based on these principles, three current frequently used methods have been proposed and classified [ 37 ]: compressed sensing (CS), machine learning (ML), and fitting [ 37 ]. Table 1 [ 19 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 ] provides a brief detail of each example in Section 3 .…”

“…Based on these principles, three current frequently used methods have been proposed and classified [37]: compressed sensing (CS), machine learning (ML), and fitting [37]. Table 1 [19,[38][39][40][41][42][43][44][45][46] provides a brief detail of each example in Section 3. The reconstruction of PSD can be succinctly described as converting analog signals into digital signals for further computer-based processing methods.…”

“…Table 1. Brief information of pressure reconstruction research in Section 3 [19,[38][39][40][41][42][43][44][45][46].…”