Paul Edward Cuddihy

Predicting hospitalization due to worsening heart failure using daily weight measurement : analysis of the Trans-European Network-Home-Care Management System (TEN-HMS) study

We sought to test the utility of weight gain algorithms to predict episodes of worsening heart failure (WHF) using home‐telemonitoring data collected as part of the TEN‐HMS study.

Algorithm to automatically detect abnormally long periods of inactivity in a home

An algorithm has been developed to automatically construct individual models of normal activity within a home using motion sensor data. Alerts can be generated when a period of inactivity exceeds a normal length for a particular residence. Alerting frequency has been optimized on a total of 650 days of real data from four homes of seniors who live independently. Results suggest that an inexpensive system that does not require the occupant to push any buttons or wear any devices could nonetheless alert within hours if a senior is unusually inactive. Further, such algorithms may facilitate widespread deployment of smart home technology to persons with different behavior patterns and home layouts by using automatic learning in place of potentially tedious manual configuration.

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.

Quantitative Gait Measurement With Pulse-Doppler Radar for Passive In-Home Gait Assessment

In this paper, we propose a pulse-Doppler radar system for in-home gait assessment of older adults. A methodology has been developed to extract gait parameters including walking speed and step time using Doppler radar. The gait parameters have been validated with a Vicon motion capture system in the lab with 13 participants and 158 test runs. The study revealed that for an optimal step recognition and walking speed estimation, a dual radar set up with one radar placed at foot level and the other at torso level is necessary. An excellent absolute agreement with intraclass correlation coefficients of 0.97 was found for step time estimation with the foot level radar. For walking speed, although both radars show excellent consistency they all have a system offset compared to the ground truth due to walking direction with respect to the radar beam. The torso level radar has a better performance (9% offset on average) in the speed estimation compared to the foot level radar (13%-18% offset). Quantitative analysis has been performed to compute the angles causing the systematic error. These lab results demonstrate the capability of the system to be used as a daily gait assessment tool in home environments, useful for fall risk assessment and other health care applications. The system is currently being tested in an unstructured home environment.

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.

Maximum Daily 6 Minutes of Activity: An Index of Functional Capacity Derived from Actigraphy and Its Application to Older Adults with Heart Failure

OBJECTIVES: To compare the correlation between the maximum 6 minutes of daily activity (M6min) and standard measures of functional capacity in older adults with heart failure (HF) with that in younger subjects and its prognostic utility.

A novel approach to aircraft engine anomaly detection and diagnostics

Accurate and timely failure detection and diagnosis is critical to reliable and affordable aircraft engine operation. This work describes a statistical and fuzzy logic based approach that analyzes multiple engine performance parameters for trend recognition, shift evaluation and failure classification. It integrates the statistical data analysis and fuzzy logic reasoning processes and provides powerful data fusion capability. The system captures and diagnoses failures as soon as the engine performance-shifting trend is recognizable, based on customizable probability. This approach improves upon current diagnostic processes in a number of ways. First, the dimensionality is increased so that multiple relevant parameters are integrated into the diagnosis. This helps reduce single dimension false alarms. Second, this approach effectively handles the noise in engine performance data. Many diagnoses depend on detecting changes in the data that fall within three standard deviations of the pre-event data, historically leading to false alerts and diagnoses. Finally, this approach seamlessly integrates the noise in the data with the uncertainty in the diagnostic models, rolling it up into a single score for each potential diagnosis. This increases consistency, and removes a substantial amount of subjective judgment from the diagnostic process. This approach has been successfully applied to a series of General Electric commercial airline engines, demonstrating high accuracy and consistency. The methodology is expected to be generally applicable to a wide variety of engine models and failure modes.

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.

ELSI: A Medical Equipment Diagnostic System

A case-based reasoning system for diagnosing medical equipment, called ELSI, has been in use by the GE corporation since 1994. When a customer or field engineer calls the service center for help with a problem, the equipments error log is automatically downloaded. In ninety seconds or less, ELSI displays a sorted list of the best-matching logs in a case base of previous known problems, shows the fix, service notes, explains which sections of the log match, and which fixes each section predicts. This diagnostic information allows the service center engineer to recommend a temporary work-around or remote fixes to a customer, or helps a field engineer show up on site with the right parts the first time.

An automatic in-home fall detection system using Doppler radar signatures

One in three elders over the age of 65 falls each year in the United States. This paper describes a non-invasive fall detection system based on a Doppler radar sensor. The developed system has been tested in two environments: laboratory and real senior living apartments. While some laboratory results appeared in our previous papers, the main novelty of this paper consists in the deployment of our fall detection system in six apartments from TigerPlace (a senior living facility in Columbia, Missouri). The fall detection results obtained in our laboratory were excellent, with the radar placed on the ceiling performing better than on the floor. The fall detection system was then evaluated using radar data collected over two weeks in six TigerPlace apartments. The fall detection system successfully detected all six natural senior falls in an apartment for the examined one week.