No abstract

Body-worn kinematic sensors have been widely proposed as the optimal solution for portable, low cost, ambulatory monitoring of gait. This study aims to evaluate an adaptive gyroscope-based algorithm for automated temporal gait analysis using body-worn wireless gyroscopes. Gyroscope data from nine healthy adult subjects performing four walks at four different speeds were then compared against data acquired simultaneously using two force plates and an optical motion capture system. Data from a poliomyelitis patient, exhibiting pathological gait walking with and without the aid of a crutch, were also compared to the force plate. Results show that the mean true error between the adaptive gyroscope algorithm and force plate was -4.5 ± 14.4 ms and 43.4 ± 6.0 ms for IC and TC points, respectively, in healthy subjects. Similarly, the mean true error when data from the polio patient were compared against the force plate was -75.61 ± 27.53 ms and 99.20 ± 46.00 ms for IC and TC points, respectively. A comparison of the present algorithm against temporal gait parameters derived from an optical motion analysis system showed good agreement for nine healthy subjects at four speeds. These results show that the algorithm reported here could constitute the basis of a robust, portable, low-cost system for ambulatory monitoring of gait.

This study compares the performance of algorithms for body-worn sensors used with a spatiotemporal gait analysis platform to the GAITRite electronic walkway. The mean error in detection time (true error) for heel strike and toe-off was 33.9 ± 10.4 ms and 3.8 ± 28.7 ms, respectively. The ICC for temporal parameters step, stride, swing and stance time was found to be greater than 0.84, indicating good agreement. Similarly, for spatial gait parameters—stride length and velocity—the ICC was found to be greater than 0.88. Results show good to excellent concurrent validity in spatiotemporal gait parameters, at three different walking speeds (best agreement observed at normal walking speed). The reported algorithms for body-worn sensors are comparable to the GAITRite electronic walkway for measurement of spatiotemporal gait parameters in healthy subjects.

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.

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.