Machine Learning Models for Human Fall Detection using Millimeter Wave Sensor

Abstract

Accidental falls are a common threat to the health of older adults, which can reduce their ability to remain independent. Fall detection sensors have become essential lifesaving health monitoring systems for the elderly. We describe a privacy protecting system for stance monitoring of occupants within a room using a millimeter wave (mmWave) sensor. We studied various machine learning models that are best suited to analyze the response from a mmWave system output. After comparing several machine learning algorithms, we found that feedforward neural networks provide the highest test accuracy.