Bio-signal data gathering, management and analysis within a patient-centred health care context

The healthcare service is under pressure to do more with less, and changing the way the service is modelled could be the key to saving resources and increasing efficacy. This change could be possible using patient-centric care models. This model would include straightforward and easy-to-use telemonitoring devices and a flexible data management structure. The structure would maintain its state by ingesting many sources of data, then tracking this data through cleaning and processing into models and estimates to obtaining values from data which could be used by the patient. The system can become less disease-focused and more health-focused by being preventative in nature and allowing patients to be more proactive and involved in their care by automating the data management. This work presents the development of a new device and a data management and analysis system to utilise the data from this device and support data processing along with two examples of its use. These are signal quality and blood pressure estimation. This system could aid in the creation of patient-centric telecare systems

An intelligent information forwarder for healthcare big data systems with distributed wearable sensors

© 2016 IEEE. An increasing number of the elderly population wish to live an independent lifestyle, rather than rely on intrusive care programmes. A big data solution is presented using wearable sensors capable of carrying out continuous monitoring of the elderly, alerting the relevant caregivers when necessary and forwarding pertinent information to a big data system for analysis. A challenge for such a solution is the development of context-awareness through the multidimensional, dynamic and nonlinear sensor readings that have a weak correlation with observable human behaviours and health conditions. To address this challenge, a wearable sensor system with an intelligent data forwarder is discussed in this paper. The forwarder adopts a Hidden Markov Model for human behaviour recognition. Locality sensitive hashing is proposed as an efficient mechanism to learn sensor patterns. A prototype solution is implemented to monitor health conditions of dispersed users. It is shown that the intelligent forwarders can provide the remote sensors with context-awareness. They transmit only important information to the big data server for analytics when certain behaviours happen and avoid overwhelming communication and data storage. The system functions unobtrusively, whilst giving the users peace of mind in the knowledge that their safety is being monitored and analysed

Prior knowledge-based deep learning method for indoor object recognition and application

Indoor object recognition is a key task for indoor navigation by mobile robots. Although previous work has produced impressive results in recognizing known and familiar objects, the research of indoor object recognition for robot is still insufficient. In order to improve the detection precision, our study proposed a prior knowledge-based deep learning method aimed to enable the robot to recognize indoor objects on sight. First, we integrate the public Indoor dataset and the private frames of videos (FoVs) dataset to train a convolutional neural network (CNN). Second, mean images, which are used as a type of colour knowledge, are generated for all the classes in the Indoor dataset. The distance between every mean image and the input image produces the class weight vector. Scene knowledge, which consists of frequencies of occurrence of objects in the scene, is then employed as another prior knowledge to determine the scene weight. Finally, when a detection request is launched, the two vectors together with a vector of classification probability instigated by the deep model are multiplied to produce a decision vector for classification. Experiments show that detection precision can be improved by employing the prior colour and scene knowledge. In addition, we applied the method to object recognition in a video. The results showed potential application of the method for robot vision

Communicable diseases as health risks at mass gatherings other than Hajj: what is the evidence?

Mass gatherings are characterized by the concentration of people temporally and spatially, and may lead to the emergence of infectious diseases due to enhanced transmission between attendees. This is well-demonstrated in the context of the Hajj and Umrah pilgrimages in Saudi Arabia. The goal of this review was to present the available evidence on outbreaks associated with a variety of pathogens, or also the lack thereof, as assessed by thorough surveillance at any mass gatherings with the exception of those in Saudi Arabia. A systematic search for relevant articles in the literature was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Sixty-eight studies were identified. Although outbreaks have not been reported frequently in or after mass gatherings outside the Hajj and Umrah pilgrimages, they have sometimes occurred at Muslim, Christian, and Hindu religious events, at sports events, and at large-scale open air festivals. In this review it was found that the most common outbreaks at these mass gatherings involved vaccine preventable diseases, mainly measles and influenza, but also mumps and hepatitis A. Meningococcal disease has rarely been recorded. Additionally it was found that the transmission of various communicable diseases that may not be prevented by vaccination has been recorded in association with mass gatherings. These were mainly gastrointestinal infections, caused by a variety of pathogens. It was also noted that some outbreaks occurring at mass gatherings have resulted in the international spread of communicable diseases