Automatic segmentation of triaxial accelerometry signals for falls risk estimation.

Falls-related injuries in the elderly population represent one of the most significant contributors to rising health care expense in developed countries. In recent years, falls detection technologies have become more common. However, very few have adopted a preferable falls prevention strategy through unsupervised monitoring in the free-living environment. The basis of the monitoring described herein was a self-administered directed-routine (DR) comprising three separate tests measured by way of a waist-mounted triaxial accelerometer. Using features extracted from the manually segmented signals, a reasonable estimate of falls risk can be achieved. We describe here a series of algorithms for automatically segmenting these recordings, enabling the use of the DR assessment in the unsupervised and home environments. The accelerometry signals, from 68 subjects performing the DR, were manually annotated by an observer. Using the proposed signal segmentation routines, an good agreement was observed between the manually annotated markers and the automatically estimated values. However, a decrease in the correlation with falls risk to 0.73 was observed using the automatic segmentation, compared to 0.81 when using markers manually placed by an observer.

Longitudinal falls-risk estimation using triaxial accelerometry.

Falls among the elderly population are a major cause of morbidity and injury-particularly among the over 65 years age group. Validated clinical tests and associated models, built upon assessment of functional ability, have been devised to estimate an individual's risk of falling in the near future. Those identified as at-risk of falling may be targeted for interventative treatment. The migration of these clinical models estimating falls risk to a surrogate technique, for use in the unsupervised environment, might broaden the reach of falls-risk screening beyond the clinical arena. This study details an approach that characterizes the movements of 68 elderly subjects performing a directed routine of unsupervised physical tasks. The movement characterization is achieved through the use of a triaxial accelerometer. A number of fall-related features, extracted from the accelerometry signals, combined with a linear least squares model, maps to a clinically validated measure of falls risk with a correlation of rho = 0.81 (p < 0.001).

Evaluation of functional deficits and falls risk in the elderly--methods for preventing falls.

Falls in the elderly have a profound impact on their quality of life through injury, increased fear of falling, reduced confidence to perform daily tasks and loss of independence. Falls come at a substantial economic cost. Tools to quantify falls risk and evaluate functional deficits allow interventions to be targeted to those at increased risk of falling and tailored to correct deficits with the aim of reducing falls rate and reducing ones risk of falling. We describe a system to evaluate falls risk and functional deficits in the elderly. The system is based on the evaluation of performance in a simple set of controlled movements known as the directed routine (DR). We present preliminary results of the DR in a cohort of 68 subjects using features extracted from the DR. Linear least-squares models were trained to estimate falls risk, knee-extension strength, proprioception, mediolateral body sway, anteroposterior body sway and contrast sensitivity. The model estimates provided good to fair correlations with (r=0.76 p<0.001), (r=0.65 p<0.001), (r=0.35 p<0.01), (r=0.53 p<0.001), (r=0.48 p<0.001) and (r=0.37 p<0.01) respectively.

A wearable triaxial accelerometry system for longitudinal assessment of falls risk.

Falls-related injuries in the elderly population are a major cause of morbidity and represent one of the most significant contributors to hospitalizations and rising health care expense in developed countries. Many laboratory-based studies have described falls detection systems using wearable accelerometry. However, only a limited number of reports have tried to address the difficult issues of falls detection and falls prevention in unsupervised or free-living environments. We describe a waist-mounted triaxial accelerometry (Triax) system with a remote data collection capability to provide unsupervised monitoring of the elderly. The basis of the monitoring is a self-administered directed-routine (DR) comprising three separate tests measured by way of the Triax. We present an initial evaluation of the DR results in 36 patients to detect early changes in functional ability and facilitate falls risk stratification. Extracted features considered alone show a correlation with falls risk of approximately rho=0.5. Estimation of falls risk using a linear least squares model provides a root-mean-squared error of 0.69 (rho=0.58, p<0.0002).