Innovative telemedicine and mobile phone technology in the management of diabetic foot ulcers

Background Diabetic foot ulcers (DFU) are a leading cause of diabetes-related hospitalisation and can be costly to manage without access to appropriate expert care. Within Queensland and indeed across many parts of Australia, there is an inequality in accessing specialist services for individuals with DFU. Recent National Health and Medical Research Council (NHMRC) diabetic foot guidelines recommend remote expert consultation with digital imaging should be made available to people with DFU to improve their clinical outcomes. Telemedicine appears to show promise in improving access to diabetic foot specialist services; however diabetic foot telemedicine models to date have relied upon videoconferencing, store and forward technology and/or customised appliances to obtain digital imagery which all require either expensive infrastructure or a timed reply to the request for advice. Whilst mobile phone advice services have been used with success in general diabetes management and telehealth services have improved diabetic foot outcomes, the rapid emergence in the use of mobile phones has established a need to review the role that various forms of telemedicine play in the management of DFU. The aim of this paper is to review traditional telemedicine modalities that have been used in the management of DFU and to compare that to new and innovative technology that are emerging. Process Studies investigating the management of DFU using various forms of telemedicine interventions will be included in this review. They include the use of videoconferencing technology, hand held digital still photography purpose built imaging devices and mobile phone imagery. Electronic databases (Pubmed, Medline and CINAHL) will be searched using broad MeSH terms and keywords that cover the intended area of interest. Findings It is anticipated that the results of this narrative review will provide delegates of the 2015 Australasian Podiatry Conference an insight into the types of emerging innovative diagnostic telemedicine technologies in the management of DFU against the backdrop of traditional and evidence based modalities. It is anticipated that the findings will drive further research in the area of mobile phone imagery and innovation in the management of DFU.

Nash equilibrium based semantic cache in mobile sensor grid database systems

Mobile applications are being increasingly deployed on a massive scale in various mobile sensor grid database systems. With limited resources from the mobile devices, how to process the huge number of queries from mobile users with distributed sensor grid databases becomes a critical problem for such mobile systems. While the fundamental semantic cache technique has been investigated for query optimization in sensor grid database systems, the problem is still difficult due to the fact that more realistic multi-dimensional constraints have not been considered in existing methods. To solve the problem, a new semantic cache scheme is presented in this paper for location-dependent data queries in distributed sensor grid database systems. It considers multi-dimensional constraints or factors in a unified cost model architecture, determines the parameters of the cost model in the scheme by using the concept of Nash equilibrium from game theory, and makes semantic cache decisions from the established cost model. The scenarios of three factors of semantic, time and locations are investigated as special cases, which improve existing methods. Experiments are conducted to demonstrate the semantic cache scheme presented in this paper for distributed sensor grid database systems.

Place categorization and semantic mapping on a mobile robot

In this paper we focus on the challenging problem of place categorization and semantic mapping on a robot with-out environment-specific training. Motivated by their ongoing success in various visual recognition tasks, we build our system upon a state-of-the-art convolutional network. We overcome its closed-set limitations by complementing the network with a series of one-vs-all classifiers that can learn to recognize new semantic classes online. Prior domain knowledge is incorporated by embedding the classification system into a Bayesian filter framework that also ensures temporal coherence. We evaluate the classification accuracy of the system on a robot that maps a variety of places on our campus in real-time. We show how semantic information can boost robotic object detection performance and how the semantic map can be used to modulate the robot’s behaviour during navigation tasks. The system is made available to the community as a ROS module.