Karol O'Donovan

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.

Fall-detection through vertical velocity thresholding using a tri-axial accelerometer characterized using an optical motion-capture system

Falls in the elderly population are a major problem for todays society. The immediate automatic detection of such events would help reduce the associated consequences of falls. This paper describes the development of an accurate, accelerometer-based fall detection system to distinguish between Activities of Daily Living (ADL) and falls. It has previously been shown that falls can be distinguished from normal ADL through vertical velocity thresholding using an optical motion capture system. In this study however accurate vertical velocity profiles of the trunk were generated by simple signal processing of the signals from a tri-axial accelerometer (TA).

SHIMMER™: An extensible platform for physiological signal capture

Wireless sensor networks have become increasingly common in everyday applications due to decreasing technology costs and improved product performance, robustness and extensibility. Wearable physiological monitoring systems have been utilized in a variety of studies, particularly those investigating ECG or EMG during human movement or sleep monitoring. These systems require extensive validation to ensure accurate and repeatable functionality. Here we validate the physiological signals (EMG, ECG and GSR) of the SHIMMER (Sensing Health with Intelligence, Modularity, Mobility and Experimental Reusability) against known commercial systems. Signals recorded by the SHIMMER EMG, ECG and GSR daughter-boards were found to compare well to those obtained by commercial systems.

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.

Distinguishing falls from normal ADL using vertical velocity profiles

This paper describes a technique for distinguishing falls from activities of daily living (ADL) through vertical velocity thresholding (VVT). To verify that VVT can be used to distinguish falls from ADL and to detect falls prior to impact, simulated fall and ADL testing was carried out on five young healthy subjects. Results show that the VVT method can distinguish falls from ADL with 100% accuracy and with an average lead-time of 323 ms prior to trunk impact and 140 ms prior to knee impact.

Optimum gravity vector and vertical acceleration estimation using a tri-axial accelerometer for falls and normal activities

This study aims to determine an optimum estimate for the gravitational vector and vertical acceleration profiles using a body-worn tri-axial accelerometer during falls and normal activities of daily living (ADL), validated using a camera based motion analysis system. Five young healthy subjects performed a number of simulated falls and normal ADL while trunk kinematics were measured by both an optical motion analysis system and a tri-axial accelerometer. Through low-pass filtering of the trunk tri-axial accelerometer signal between 1Hz and 2.7Hz using a 1st order or higher, Butterworth IIR filter, accurate gravity vector profile can be obtained using the method described here. Results: a high mean correlation (≥0.83: Coefficient of Multiple Correlations) and low mean percentage error (≤2.06m/s2) were found between the vertical acceleration profile generated from the tri-axial accelerometer based sensor to those from the optical motion capture system. This proposed system enables optimum gravity vector and vertical acceleration profiles to be measured from the trunk during falls and normal ADL.

Clinical gait assessment of older adults using open platform tools

Gait impairment is associated with increased falls risk. The gait of 321 community dwelling elderly adults was assessed using the TRIL Gait Analysis Platform (GAP), which was specially designed for ease of use in a research clinic setting by non-experts. The GAP featured body-worn kinematic sensors, a pressure sensitive electronic walkway, and two orthogonally mounted web cameras, and was developed using open platform tools. This flexible platform was applied to objectively measure gait parameters in different gait assessments. The results from the 6 meter walk assessment are presented here. In this assessment, participants were categorized by clinical falls history as ‘fallers’ or ‘non-fallers’. Temporal and spatial gait parameters were examined. Significant differences in spatial parameters were observed when fallers and non-fallers were compared. Temporal parameters were found to differ, though not significantly.