Denise McGrath

SHIMMER: A new tool for temporal gait analysis

Development of a flexible wireless sensor platform for measurement of biomechanical and physiological variables related to functional movement would be a vital step towards effective ambulatory monitoring and early detection of risk factors in the ageing population. The small form factor, wirelessly enabled SHIMMER platform has been developed towards this end. This study is focused assessing the utility of the SHIMMER for use in ambulatory human gait analysis. Temporal gait parameters derived from a tri-axial gyroscope contained in the SHIMMER are compared against those acquired simultaneously using the CODA motion analysis system. Results from a healthy adult male subject show excellent agreement (ICC(2, k) > 0.85) in stride, swing and stance time for 10 walking trials and 4 run trials. The mean differences using the Bland and Altman method for stance, stride and swing times were 0.0087, 0.0044 and -0.0061 seconds respectively. These results suggest that the SHIMMER is a versatile cost effective tool for use in temporal gait analysis.

Displacement of centre of mass during quiet standing assessed using accelerometry in older fallers and non-fallers

Postural sway during quiet standing is associated with falls risk in older adults. The aim of this study was to investigate the utility of a range of accelerometer-derived parameters of centre of mass (COM) displacement in identifying older adults at risk of falling. A series of instrumented standing balance trials were performed to investigate postural control in a group of older adults, categorised as fallers or non-fallers. During each trial, participants were asked to stand as still as possible under two conditions: comfortable stance (six repetitions) and semi-tandem stance (three repetitions). A tri-axial accelerometer was secured to the lower back during the trials. Accelerometer data were twice integrated to estimate COM displacement during the trials, with numerical techniques used to reduce integration error. Anterior-posterior (AP) and medial-lateral (ML) sway range, sway length and sway velocity were examined, along with root mean squared (RMS) acceleration. All derived parameters significantly discriminated fallers from non-fallers during both comfortable and semi-tandem stance. Results indicate that these accelerometer-based estimates of COM displacement may improve the discriminative power of quiet standing falls risk assessments, with potential for use in unsupervised balance assessment.

Estimation of minimum ground clearance (MGC) using body-worn inertial sensors

Objective assessment of balance and mobility in elderly populations using body-worn sensors has recently become a prevalent theme in falls-related research. Recent research by the authors identified mean absolute-valued vertical angular velocity measured using shank mounted inertial sensors during a timed-up-and-go test as having a strong association with falls history in a group of elderly adults. This study aimed to investigate the clinical relevance of this parameter by exploring the relationship between it and minimum ground clearance (MGC) measured with an optical motion capture system. MGC is an important variable when considering trip-related falls risk. This paper also presents a method of estimating properties of MGC during walking, across a range of speeds and gait patterns, using body-worn inertial sensors. We found that mean MGC and coefficient of variation (CV) MGC are correlated with mean absolute-valued vertical angular velocity and acceleration as measured by shank or foot mounted inertial sensors. Regression models generated using inertial sensor derived variables were used to robustly estimate the mean MGC and CV MGC measured by an optical marker-tracking system. Foot-mounted sensors were found to yield slightly better results than sensors on the shank. Different walking speeds and gait patterns were not found to influence the accuracy of the models. We conclude that these findings have the potential to evaluate a walking trial using body-worn inertial sensors, which could then be used to identify individuals with increased risk of unprovoked collisions with the ground during locomotion.

Adaptive estimation of temporal gait parameters using body-worn gyroscopes

Body-worn kinematic sensors have been widely proposed for use in portable, low cost, ambulatory monitoring of gait. Such sensor based systems could avoid the need for high-cost laboratory-based methods for measurement of gait. We aimed to evaluate an adaptive gyroscope-based algorithm for automated temporal gait analysis using body-worn wireless gyroscopes. Temporal gait parameters were calculated from initial contact (IC) and terminal contact (TC) points derived from gyroscopes, contained in wireless sensors on the left and right shanks, using a newly developed adaptive algorithm. Gyroscope data from nine healthy adult subjects performing four walks at three different speeds were then compared against data acquired simultaneously using two force-plates. Results show that the mean true error between the adaptive gyroscope algorithm and force-plate was −5.5±7.3 ms and 40.6±19.2 ms for IC and TC points respectively; the latter representing a consistent, systematic error of this magnitude that may be intrinsic to shank-mounted gyroscopes. These results suggest that the algorithm reported here could form the basis of a robust, portable, low-cost system for ambulatory monitoring of gait.

Quantitative falls risk estimation through multi-sensor assessment of standing balance

Falls are the most common cause of injury and hospitalization and one of the principal causes of death and disability in older adults worldwide. Measures of postural stability have been associated with the incidence of falls in older adults. The aim of this study was to develop a model that accurately classifies fallers and non-fallers using novel multi-sensor quantitative balance metrics that can be easily deployed into a home or clinic setting. We compared the classification accuracy of our model with an established method for falls risk assessment, the Berg balance scale. Data were acquired using two sensor modalities--a pressure sensitive platform sensor and a body-worn inertial sensor, mounted on the lower back--from 120 community dwelling older adults (65 with a history of falls, 55 without, mean age 73.7 ± 5.8 years, 63 female) while performing a number of standing balance tasks in a geriatric research clinic. Results obtained using a support vector machine yielded a mean classification accuracy of 71.52% (95% CI: 68.82-74.28) in classifying falls history, obtained using one model classifying all data points. Considering male and female participant data separately yielded classification accuracies of 72.80% (95% CI: 68.85-77.17) and 73.33% (95% CI: 69.88-76.81) respectively, leading to a mean classification accuracy of 73.07% in identifying participants with a history of falls. Results compare favourably to those obtained using the Berg balance scale (mean classification accuracy: 59.42% (95% CI: 56.96-61.88)). Results from the present study could lead to a robust method for assessing falls risk in both supervised and unsupervised environments.

Reliability of quantitative TUG measures of mobility for use in falls risk assessment

Recent advances in body-worn sensor technology have increased the scope for harnessing quantitative information from the timed-up-and-go test (TUG), well beyond simply the time taken to perform the test. Previous research has shown that the quantitative TUG method can differentiate fallers from non-fallers with greater success than the manually timed TUG or the Berg Balance Test. In order to advance this paradigm of falls risk estimation it is necessary to investigate the robustness of the quantitative TUG variables. This study investigated the inter-session and intra-session reliability of 44 quantitative TUG variables measured from the shanks and lower back of 33 study participants aged between 55–65 yrs. For intra-session reliability, 25 variables demonstrated excellent reliability (ICC>0.75), and 12 demonstrated “fair to good reliability” with ICCs between 0.4 and 0.75. Analysis of test-retest reliability resulted in ICC > 0.75 for 18 out of 44 variables, with 20 variables showing fair to good reliability. Turn time parameters demonstrated poor reliability. We conclude that this is a reliable instrument that may be used as part of a long-term falls risk assessment, with further work required to improve certain turn parameters.

Taking balance measurement out of the laboratory and into the home: Discriminatory capability of novel centre of pressure measurement in fallers and non-fallers

We investigated three methods for estimating centre of pressure excursions, as measured using a portable pressure sensor matrix, in order to deploy similar technology into the homes of older adults for longitudinal monitoring of postural control and falls risk. We explored the utility of these three methods as markers of falls risk in a cohort of 120 community dwelling older adults with and without a history of falls (65 fallers, 55 non-fallers). A number of standard quantitative balance parameters were derived using each centre of pressure estimation method. Rank sum tests were used to test for significant differences between fallers and non-fallers while intra-class correlation coefficients were also calculated to determine the reliability of each method. A method based on estimating the changes in the magnitude of pressure exerted on the pressure sensor matrix was found to be the most reliable and discriminative. Our future work will implement this method for home-based balance measurement.

Body-worn sensor based surrogates of minimum ground clearance in elderly fallers and controls

Falls in the elderly are a major problem worldwide with enormous associated economic and societal costs. Minimum ground clearance (MGC) is an important gait variable when considering trip-related falls risk. This study aimed to investigate the clinical relevance of inertial sensor derived parameters, previously shown to be related to MGC. Previous research by the authors reported a surrogate method for assessing minimum ground clearance (MGC) using shank-mounted inertial sensors in young controls. The present study tests this method on a cohort of 114 community dwelling elderly adults, with and without a history of falls, completing a 30m continuous walk. Parameters based on the shank angular velocity signals that were shown to be associated with MGC showed significant differences (p<0.05) between fallers and non-fallers yet did not correlate strongly (r<0.7) with two standard measures of falls risk (TUG & BBS). Weak correlations were observed between the angular velocity derived parameters and gait velocity. We conclude that these parameters are clinically meaningful and therefore may constitute a new measure of falls risk.

Using a tri-axial accelerometer to detect technique breakdown due to fatigue in distance runners: A preliminary perspective

Accelerometer technology is becoming increasingly smaller and cheaper to develop. As a direct result, such devices can potentially be easily integrated into footwear to capture data that provides information about the quality of a persons running technique in the later stages of a fatiguing run. The purpose of this study is to determine if it is possible to detect technique breakdown due to fatigue in a distance runner using shoe mounted accelerometers. We present an algorithm that uses computationally light data from tri-axial foot mounted accelerometers and compares outputs from them to kinematic changes in the runner as the runner fatigues. These preliminary findings show that kinematic changes due to fatigue can be reasonably estimated using outputs from a shoe mounted tri-axial accelerometer.

A comparison of cross-sectional and prospective algorithms for falls risk assessment

Falls are the most common cause of injury and hospitalization and one of the principal causes of death and disability in older adults worldwide. Accurate identification of patients at risk of falls could lead to timely medical intervention, reducing the incidence of falls related injuries along with associated costs. The current best practice for studies of falls and falls risk recommends the use of prospective follow-up data. However, the majority of studies reporting sensor based methods for assessment of falls risk employ cross-sectional falls data (falls history). The purpose of this study was to compare the performance of sensor based falls risk assessment algorithms derived from cross-sectional (N=909) and prospective (N=259) datasets in terms of false positive rate. The utility of any classification algorithm is clearly limited by a high false positive rate. An estimate of the false positive rate for both cross-sectional and prospective algorithms was determined using an inertial sensor data set of 611 TUG tests from 55 healthy control subjects, with no history of falls. We aimed to determine which falls risk assessment algorithm is more effective at classifying falls risk in healthy control subjects. The cross-sectional algorithm correctly classified 94.11% of tests, while the prospective algorithm, correctly classified 79.38% of tests. Results suggest that sensor based falls risk assessment algorithms generated using cross-sectional falls data, may be more effective than those generated using prospective data in classifying healthy controls and reducing associated false positives.