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.

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.

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

AIMS: 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. METHODS AND RESULTS: Simple rule-of-thumb (RoT) algorithms (i.e. 3 lbs in 1 day and 5 lbs in 3 days) and a moving average convergence divergence (MACD) algorithm were compared. WHF was defined as hospitalization for WHF or worsening of breathlessness or leg oedema. Of 168 patients, 45 were hospitalized with WHF and 76 were hospitalized for other reasons. On average, weight gain occurred in the 14 days prior to WHF hospitalizations but not in the 14 days prior to non-WHF hospitalizations [1.9 +/- 4.7 lbs (0.9 +/- 2.1 kg) vs. -0.4 +/- 2.5 lbs (-0.2 +/- 1.1 kg), P < 0.0001]. The true alerts rate was higher for the RoT algorithms compared with the MACD (58 and 65% vs. 20%). However, the RoT algorithms had much higher false alert rates (54 and 58% vs. 9%) rendering them of little practical use for predicting WHF events. CONCLUSION: A MACD algorithm is more specific but less sensitive than RoT when trying to predict episodes of WHF based on daily weight measurements. However, many episodes of WHF do not appear to be associated with weight gain and therefore telemonitoring of weight alone may not have great value for heart failure management.