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Hallway Gait Monitoring Using Novel Radar Signal Processing and Unsupervised Learning

Published in : IEEE Sensors Journal (Volume: 22, Issue: 15, August 2022)
Authors : Hajar Abedi, Jennifer Boger, Plinio P. Morita, Alexander Wong, George Shaker
Summary Contributed by:  Hajar Abedi (Author)

In the healthcare system or personal health monitoring, accurately assessing an individual's gait in the natural environment holds significant promise for early detection of health issues. It helps provide better care for the patient and monitor overall well-being. The paper introduces a groundbreaking gait monitoring system that utilizes radar signal processing alongside unsupervised learning techniques, marking a significant advancement in contactless health monitoring technology.

Traditional gait analysis methods often rely on expensive, complex equipment or wearable sensors, which might not always capture the most natural walking patterns due to their intrusive nature. Recognizing these limitations, the researchers developed an innovative solution that employs a MIMO FMCW radar system capable of capturing human gait in highly cluttered environments without requiring direct physical contact or any alterations to the radar antenna.

A radar-based sensor is an excellent alternative to capture gait information under normal living conditions and daily activities of the person as it is affordable, simple, non-invasive, and comfortable to use. The proposed novel corridor gait monitoring system uses radar signal processing and unsupervised learning. It employs a subject detection, association, and tracking method without radar antenna modification. The proposed algorithm is compatible with any MIMO FMCW radar, captures human gait in cluttered environments, and shows minimal error for speed estimation between 0.0040 m/s and 0.0435 m/s.

At the core of the system is a novel algorithm that seamlessly integrates with existing radar technology to monitor spatiotemporal gait parameters, including speed, step points, step time, step count, and step length. The approach addresses the challenges posed by stationary objects and clutter, which can significantly interfere with signal accuracy.

The system developed effectively distinguishes between the walking subject and other static or moving objects in the environment by employing advanced signal processing methods and a density-based spatial clustering algorithm, ensuring highly accurate gait analysis.

The system shows the ability to estimate gait parameters accurately with minimal error. It establishes its potential for various applications, from monitoring the senior citizens in their homes to assisting in the rehabilitation processes in clinical settings. Furthermore, by eliminating the need for wearable sensors, the system offers a more comfortable and less obtrusive option, encouraging widespread adoption and continuous monitoring.

The development of this hallway gait monitoring system represents a significant step forward in the application of radar technology for health and wellness purposes. It provides a more accurate and reliable means of assessing gait and opens new avenues for research and development in contactless health monitoring.

The work here offers a glimpse into the future of personal health monitoring, where advanced technology and novel algorithms come together to provide valuable insights into human movement. This system has the potential to significantly enhance the understanding of gait patterns and their health implications, offering a promising tool for early detection, intervention, and ongoing management of a variety of conditions. In the future, research to explore further applications of this technology and its integration into everyday life will revolutionize the healthcare system, the general well-being, and the quality of life.

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