Tarik Yardibi

Gait characterization via pulse-Doppler radar

Falls are a major cause of injury in the elderly with almost 1/3rd of people aged 65 and more falling each year [1]. This work aims to use gait measurements from everyday living environments to estimate risk of falling and enable improved interventions. For this purpose, we consider the use of low-cost pulse-Doppler range control radar. These radars can continuously acquire data during normal activity of a person in night and day conditions and even in the presence of obstructing furniture. A short-time Fourier transform of the radar data reveals unique Doppler signatures from the torso motion and the leg swings. Two algorithms that can extract these features from the radar spectrogram are proposed in this study for estimating gait velocity and stride durations. The performance of the proposed radar system is evaluated with experimental data, which consists of 9 different walk types and a total of 27 separate tests. A high accuracy motion-capture camera system has also been used to acquire data simultaneously with the radar and provides the ground truth reference. Results indicate that the proposed radar system is a viable candidate for gait characterization and can be used to accurately track mean gait velocity, mean stride duration and stride duration variability. The gait velocity variability can also be estimated but with relatively larger error levels.

Radar walking speed measurements of seniors in their apartments: Technology for fall prevention

Falls are a significant cause of injury and accidental death among persons over the age of 65. Gait velocity is one of the parameters which have been correlated to the risk of falling. We aim to build a system which monitors gait in seniors and reports any changes to caregivers, who can then perform a clinical assessment and perform corrective and preventative actions to reduce the likelihood of falls. In this paper, we deploy a Doppler radar-based gait measurement system into the apartments of thirteen seniors. In scripted walks, we show the system measures gait velocity with a mean error of 14.5% compared to the time recorded by a clinician. With a calibration factor, the mean error is reduced to 10.5%. The radar is a promising sensing technology for gait velocity in a day-to-day senior living environment.

Radar walk detection in the apartments of elderly

Seniors want to live more independent lifestyles. This comes with some risks including dwindling health and major injuries due to falling. A factor that has been studied and seen to have a correlation to fall risk is change in gait speed. Our goal is to create a passive system that monitors the gait of elderly so that assessments can be given by caregivers if gait changes do occur. This paper will cover a method of using pulse-Doppler radar to detect when walks occur. In unscripted living environments, we are able to detect valid walks. The system does miss walks during the day, but when walks are detected, they are actually valid walks 91.8% of the time using a large data base of radar signals captured in living environments.

Continuous remote vital sign/environment monitoring for returning soldier adjustment assessment

A three-stage study to develop and test an unobtrusive room sensor unit and subject data management system to discover correlation between sensor-based time-series measurements of sleep quality and clinical assessments of combat veterans suffering from Post-traumatic Stress Disorder (PTSD) and mild Traumatic Brain Injury (TBI), is described. Experiments and results for testing sensitivity and robustness of the sensor unit and data management protocol are provided. The current sensitivity of remote vital sign monitoring system is below 20% and 10% for respiration and heart rates, respectively.

Sleep and activity monitoring for Returning Soldier Adjustment Assessment

This paper describes the development of unobtrusive room sensors to discover relationships between sleep quality and the clinical assessments of combat soldiers suffering from post-traumatic stress disorder (PTSD) and mild traumatic brain injury (TBI). We consider the use of a remote room sensor unit composed of a Doppler radar, light, sound and other room environment sensors. We also employ an actigraphy watch. We discuss sensor implementation, radar data analytics and preliminary results using real data from a Warrior Transition Battalion located in Fort Gordon, GA. Two radar analytical approaches are developed and compared against the actigraphy watch estimates - one, emphasizing system knowledge; and the other, clustering on several radar signal features. The radar analytic algorithms are able to estimate sleep periods, signal absence and restlessness in the bed. In our test cases, the radar estimates are shown to agree with the actigraphy watch. PTSD and mild-TBI soldiers do often show signs of sporadic and restless sleep. Ongoing research results are expected to provide further insight.