Frequency Modulated Continuous Wave Radar Based Fall Risk Monitoring System
Name
copeland-dcope-smme-mech-2024-thesis.pdf
Description
Thesis PDF
Size
10.12 MB
Format
Adobe PDF
Checksum (MD5)
fdde906ea8deb245b3984ec48aa6d6e3
Author(s)
Copeland, Daniel Ilan
Advisor(s)
Anthony, Brian W.
Date Issued
May 2024
Publisher
Massachusetts Institute of Technology
Abstract
Falls represent a significant health risk, especially for the elderly. Fortunately, interventions have been shown to decrease falls when clinicians identify at-risk patients. However, factors such as medication changes, illness, and injuries can rapidly increase fall risk, making timely clinical identification and subsequent interventions challenging to implement. Our study introduces a comprehensive approach to assessing fall risk using a frequency-modulated continuous-wave (FMCW) radar system, addressing the need for frequent, low-cost, longterm balance monitoring solutions. This technology is compared with ground-truth contactbased lab sensors like force plates and motion capture systems, establishing a foundation for accurate balance assessments in home settings. In our cross-sectional analysis, participants performed the one-legged stand test (OLST) with simultaneous data collection from FMCW radar, force plates, and motion capture systems. By integrating the FMCW radar with machine learning algorithms, we achieved a 98.4% accuracy in identifying OLST foot movements and an R-squared of 0.70 in predicting force plate patterns, demonstrating the system’s nuanced capability for balance performance evaluation. Additionally, we examine the efficacy of combining radar technology with machine learning to identify movements similar to those performed in fitness, clinical, and rehabilitation settings. We also explore the use of simulations for optimizing radar system configurations. This thesis demonstrates the effectiveness of FMCW radar technology in laboratory settings and its potential for home-based health monitoring. The study highlights the transformative potential of integrating radar technology with machine learning through detailed experimentation and analysis, offering a versatile tool for health monitoring and fall risk assessment.
MIT Department
Massachusetts Institute of Technology. Department of Mechanical Engineering
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