Queensland’s high risk foot database : tracking the length and width of Queensland’s foot ulcers

Background Foot ulcers are a leading cause of avoidable hospital admissions and lower extremity amputations. However, large clinical studies describing foot ulcer presentations in the ambulatory setting are limited. The aim of this descriptive observational paper is to report the characteristics of ambulatory foot ulcer patients managed across 13 of 17 Queensland Health & Hospital Services. Methods Data on all foot ulcer patients registered with a Queensland High Risk Foot Form (QHRFF) was collected at their first consult in 2012. Data is automatically extracted from each QHRFF into a Queensland high risk foot database. Descriptive statistics display age, sex, ulcer types and co-morbidities. Statewide clinical indicators of foot ulcer management are also reported. Results Overall, 2,034 people presented with a foot ulcer in 2012. Mean age was 63(±14) years and 67.8% were male. Co-morbidities included 85% had diabetes, 49.7% hypertension, 39.2% dyslipidaemia, 25.6% cardiovascular disease, 13.7% kidney disease and 12.2% smoking. Foot ulcer types included 51.6% neuropathic, 17.8% neuro-ischaemic, 7.2% ischaemic, 6.6% post-surgical and 16.8% other; whilst 31% were infected. Clinical indicator results revealed 98% had their wound categorised, 51% received non-removable offloading, median ulcer healing time was 6-weeks and 37% had ulcer recurrence. Conclusion This paper details the largest foot ulcer database reported in Australia. People presenting with foot ulcers appear predominantly older, male with several co-morbidities. Encouragingly it appears most patients are receiving best practice care. These results may be a factor in the significant reduction of Queensland diabetes foot-related hospitalisations and amputations recently reported.

A hybrid maximum power point tracking for partially shaded photovoltaic systems in the tropics

Partial shading and rapidly changing irradiance conditions significantly impact on the performance of photovoltaic (PV) systems. These impacts are particularly severe in tropical regions where the climatic conditions result in very large and rapid changes in irradiance. In this paper, a hybrid maximum power point (MPP) tracking (MPPT) technique for PV systems operating under partially shaded conditions witapid irradiance change is proposed. It combines a conventional MPPT and an artificial neural network (ANN)-based MPPT. A low cost method is proposed to predict the global MPP region when expensive irradiance sensors are not available or are not justifiable for cost reasons. It samples the operating point on the stairs of I–V curve and uses a combination of the measured current value at each stair to predict the global MPP region. The conventional MPPT is then used to search within the classified region to get the global MPP. The effectiveness of the proposed MPPT is demonstrated using both simulations and an experimental setup. Experimental comparisons with four existing MPPTs are performed. The results show that the proposed MPPT produces more energy than the other techniques and can effectively track the global MPP with a fast tracking speed under various shading patterns.

Tracking people in 3D using position, size and shape

This paper presents a prototype tracking system for tracking people in enclosed indoor environments where there is a high rate of occlusions. The system uses a stereo camera for acquisition, and is capable of disambiguating occlusions using a combination of depth map analysis, a two step ellipse fitting people detection process, the use of motion models and Kalman filters and a novel fit metric, based on computationally simple object statistics. Testing shows that our fit metric outperforms commonly used position based metrics and histogram based metrics, resulting in more accurate tracking of people.

Person tracking using motion detection and optical flow

Person tracking systems to date have either relied on motion detection or optical flow as a basis for person detection and tracking. As yet, systems have not been developed that utilise both these techniques. We propose a person tracking system that uses both, made possible by a novel hybrid optical flow-motion detection technique that we have developed. This provides the system with two methods of person detection, helping to avoid missed detections and the need to predict position, which can lead to errors in tracking and mistakes when handling occlusion situations. Our results show that our system is able to track people accurately, with an average error less than four pixels, and that our system outperforms the current CAVIAR benchmark system.