Senior Research Officer
NHMRC EC Fellow
Lecturer, Graduate School of Biomedical Engineering, UNSW
+612 9399 1801
Matt Brodie is a NHMRC EC Fellow with internationally recognised expertise using wearable devices to track human movements, developing bio-signal processing algorithms, and analysing ‘big data’ sets. Highlights include; the MacDiarmid Young Scientist of the Year Award (Future Science and Technology Winner); an International Ski Federation (FIS) Innovation Award; and a Museum exhibition displaying his wearable ‘fusion motion capture’ system. His research objectives are to untangle the complex web of interactions that prevent healthy aging. His main research area is “Wearable Devices for Reducing Falls in Older People and Clinical Populations with Balance Disorders”. Through collaborations he is using wearable sensors to track changing fall risk and prevent falls in older people, stabilise gait in people with Parkinson’s disease, and reduce the effects of contracture in people with Multiple Sclerosis.
VICKY SMITH Executive Assistant
JESSICA TURNER Research Assistant
JOANNE LO Research Assistant
CAMERON HICKS Research Assistant
DR ESTHER VANCE Senior Research Assistant
DANIELA MEINRATH Masters student
DR YOSHIRO OKUBO
JOANA CAETANO PhD student
MAYNA RATANAPONGLEKA Research Assistant
PROF CATHIE SHERRINGTON Senior research officer
Compared with nonspatial cognitive tasks, visuospatial cognitive tasks led to a slower, more variable and less smooth gait pattern. These findings suggest that visuospatial processing might share common networks with locomotor control, further supporting the hypothesis that gait changes during dual task paradigms are not simply due to limited attentional resources but to competition for common networks for spatial information encoding.
Humans are living longer but morbidity has also increased; threatening to create a serious global burden. Our approach is to monitor gait for early warning signs of morbidity. Here we present highlights from a series of experiments into gait as a potential biomarker for Parkinson's disease (PD), ageing and fall risk. Using body-worn accelerometers, we developed several novel camera-less methods to analyze head and pelvis movements while walking. Signal processing algorithms were developed to extract gait parameters that represented the principal components of vigor, head jerk, lateral harmonic stability, and oscillation range. The new gait parameters were compared to accidental falls, mental state and co-morbidities. We observed: 1) People with PD had significantly larger and uncontrolled anterioposterior (AP) oscillations of the head; 2) Older people walked with more lateral head jerk; and, 3) the combination of vigorous and harmonically stable gait was demonstrated by non-fallers. Our findings agree with research from other groups; changes in human gait reflect changes to well-being. We observed; different aspects of gait reflected different functional outcomes. The new gait parameters therefore may be complementary to existing methods and may have potential as biomarkers for specific disorders. However, further research is required to validate our observations, and establish clinical utility.
Morbidity and falls are problematic for older people. Wearable devices are increasingly used to monitor daily activities. However, sensors often require rigid attachment to specific locations and shuffling or quiet standing may be confused with walking. Furthermore, it is unclear whether clinical gait assessments are correlated with how older people usually walk during daily life. Wavelet transformations of accelerometer and barometer data from a pendant device worn inside or outside clothing were used to identify walking (excluding shuffling or standing) by 51 older people (83 ± 4 years) during 25 min of 'free-living' activities. Accuracy was validated against annotated video. Training and testing were separated. Activities were only loosely structured including noisy data preceding pendant wearing. An electronic walkway was used for laboratory comparisons. Walking was classified (accuracy ≥97 %) with low false-positive errors (≤1.9%, κ ≥ 0.90). Median free-living cadence was lower than laboratory-assessed cadence (101 vs. 110 steps/min, p < 0.001) but correlated (r = 0.69). Free-living step time variability was significantly higher and uncorrelated with laboratory-assessed variability unless detrended. Remote gait impairment monitoring using wearable devices is feasible providing new ways to investigate morbidity and falls risk. Laboratory-assessed gait performances are correlated with free-living walks, but likely reflect the individual's 'best' performance.