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Abstract-This paper proposes an algorithm for wide-band microwave imaging for the detection of a hemorrhagic stroke. A realistic head phantom and finite-difference time-domain program are used to estimate back-scattered signals which are subsequently used in the image reconstruction process. The proposed imaging approach can lead to a portable and cost effective system; particularly suitable for rural medical clinics that lack the necessary resources in effective stroke diagnosing.

Microwave imaging reconstruction is applied to an anatomically realistic, numerical head phantom using the Born iterative method for detection of a haemorrhagic stroke within the brain tissues. A forward model is used to estimate back‐scattered signals which are subsequently used in the image reconstruction process. Image reconstruction using excitation signals at 850 MHz is shown to produce the emulated stroke with a high contrast particular to the estimated conductivity profile.

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BackgroundLifespace is a multidimensional construct that describes the geographic area in which a person lives and conducts their activities, and reflects mobility, health, and well-being. Traditionally, it has been measured by asking older people to self-report the length and frequency of trips taken and assistance required. Global Positioning System (GPS) sensors on smartphones have been used to measure Lifespace of older people, but not with people with Parkinson’s disease (PD).ObjectiveThe objective of this study was to investigate whether GPS data collected via smartphones could be used to indicate the Lifespace of people with PD.MethodsThe dataset was supplied via the Michael J Fox Foundation Data Challenge and included 9 people with PD and 7 approximately matched controls. Participants carried smartphones with GPS sensors over two months. Data analysis compared the PD group and the control group. The impact of symptom severity on Lifespace was also investigated.ResultsVisualization methods for comparing Lifespace were developed including scatterplots and heatmaps. Lifespace metrics for comparison included average daily distance, percentage of time spent at home, and number of trips into the community. There were no significant differences between the PD and the control groups on Lifespace metrics. Visual representations of Lifespace were organized based on the self-reported severity of symptoms, suggesting a trend of decreasing Lifespace with increasing PD symptoms.ConclusionsLifespace measured by GPS-enabled smartphones may be a useful concept to measure the progression of PD and the impact of various therapies and rehabilitation programs. Directions for future use of GPS-based Lifespace are provided.