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.
Technological advances have enabled less expensive ways to quantify physical fall risk in the homes of older people.
We are exploring whether unobtrusive monitoring of activities of daily living or regular unsupervised directed routine assessments using new sensor-based technologies can predict falls in older adults more accurately.
We are developing and validating a range of mobile apps to assess fall risk factors in research settings and clinical practice; i.e. questionnaires (fear of falling, physical activity, etc), sensorimotor assessments (balance, vision, etc) and cognitive assessments (executive functioning, processing speed, etc.).
We are also working on Smart home IT support for frail elderly people who live alone.
An engaging self-managed neuro-rehabilitation program using eHealth technologies to improve mobility and enhance independence in people with Parkinson’s disease:
Parkinson’s disease is a multi-systems neurodegenerative disease with the severity of clinical symptoms (including postural instability, gait dysfunction an falls). With the population aging, the number of people affected by Parkinson’s disease is expected to double every 25 years presenting an increasing burden on health service and society as a whole. Falls are a common and devastating event in individuals with Parkinson’s disease and often precipitated by excessive gait variability, postural instability and freezing of gait.
Visual, attentional, haptic and auditory stimuli have been used to improve gait dysfunction in people with Parkinson’s disease. The aim of this project is to develop and evaluate a self-managed program using mHealth technology to improve mobility in people suffering from Parkinson’s disease. Dr Matthew Brodie was awarded a Michael & Elizabeth Gilbert Scholarship in Parkinson’s Disease Research.
An engaging self-management program and scalable intervention using mobile technology to enhance healthy ageing and reduce fall risk in people with intermittent claudication: a randomised trial.
Cardiovascular disease is the leading cause of death and hospital admissions in Australia. Intermittent claudication is an intense cramping leg pain triggered by exercise and a common symptom of Peripheral Arterial Disease. It often causes functional decline, high health service use and loss of independence. Vascular interventions are often used to treat peripheral arterial disease, but are expensive and have limited durability. There is strong evidence that supervised exercise mitigates symptoms and reduces surgery rates. However, compliance and motivation with existing programs is poor (>40% dropout) due to beliefs that exercise-induced pain is harmful.
Supported by the UNSW Medicine Neuroscience, Mental Health and Addiction Theme and SPHERE Clinical Academic Group, we have developed a scalable self-management program for peripheral arterial disease delivered through mobile technology. Our program includes evidence-based standing balance exercises, pain management and interval walking components. It provides individually-tailored tools to empower older people suffering from intermittent claudication to lead more active lives, manage their pain and thereby improving their health outcomes long-term.
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.