Analysis of Failure Mechanism and Reliability Enhancement of Silicon Strain Gauge-Based Pressure Sensor for Automotive Applications.

Hydrogen fuel cell vehicles have gained more attention as future automobiles due to their environmental benefits and extended driving ranges. Concurrently, the global hydrogen sensor market is also experiencing substantial growth. These sensors are integrated into vehicles to detect hydrogen leakage and concentration, thereby ensuring the safety of hydrogen fuel cell vehicles. In particular, hydrogen pressure sensors, commonly installed on the manifold and regulator of vehicles, can measure hydrogen pressure and diagnose safety concerns caused by hydrogen leakage in advance. In this paper, we identify the vulnerable points of hydrogen pressure sensors when exposed to vehicle driving environments, investigate failure mechanisms, and provide process optimization techniques. Specifically, our reliability modeling verifies that the components of a printed circuit board (PCB) exposed to humid environments undergo corrosion due to ion migration, leading to the generation of extrinsic series or parallel resistances, which in turn cause fluctuations of output voltage. Through structural and elemental analysis, we pinpoint process-related factors that make components vulnerable to humidity, thereby suggesting recommendations for enhancing the manufacturing process. Based on this analysis in the development stage, we can proactively address and improve reliability and further safety-related issues for future automobiles, thus preventing real field issues.

Empirical Study on Human Movement Classification Using Insole Footwear Sensor System and Machine Learning.

This paper presents a plantar pressure sensor system (P2S2) integrated in the insoles of shoes to detect thirteen commonly used human movements including walking, stooping left and right, pulling a cart backward, squatting, descending, ascending stairs, running, and falling (front, back, right, left). Six force sensitive resistors (FSR) sensors were positioned on critical pressure points on the insoles to capture the electrical signature of pressure change in the various movements. A total of 34 adult participants were tested with the P2S2. The pressure data were collected and processed using a Principal Component Analysis (PCA) for input to the multiple machine learning (ML) algorithms, including k-NN, neural network and Support-Vector Machine (SVM) algorithms. The ML models were trained using four-fold cross-validation. Each fold kept subject data independent from other folds. The model proved effective with an accuracy of 86%, showing a promising result in predicting human movements using the P2S2 integrated in shoes.

Lower Body Joint Angle Prediction Using Machine Learning and Applied Biomechanical Inverse Dynamics.

Extreme angles in lower body joints may adversely increase the risk of injury to joints. These injuries are common in the workplace and cause persistent pain and significant financial losses to people and companies. The purpose of this study was to predict lower body joint angles from the ankle to the lumbosacral joint (L5S1) by measuring plantar pressures in shoes. Joint angle prediction was aided by a designed footwear sensor consisting of six force-sensing resistors (FSR) and a microcontroller fitted with Bluetooth LE sensors. An Xsens motion capture system was utilized as a ground truth validation measuring 3D joint angles. Thirty-seven human subjects were tested squatting in an IRB-approved study. The Gaussian Process Regression (GPR) linear regression algorithm was used to create a progressive model that predicted the angles of ankle, knee, hip, and L5S1. The footwear sensor showed a promising root mean square error (RMSE) for each joint. The L5S1 angle was predicted to be RMSE of 0.21° for the X-axis and 0.22° for the Y-axis, respectively. This result confirmed that the proposed plantar sensor system had the capability to predict and monitor lower body joint angles for potential injury prevention and training of occupational workers.