Ubiquitous computing environments that are involved in healthcare applications are typically characterized by dynamically changing contexts. The contextual information must be efficiently processed in order to support medical decision making. The ubiquitous computing healthcare ecosystem must be capable of extracting medically valuable characteristics, making precise decisions, and taking medically appropriate actions. In this framework, deep learning networks can be used for data fusion of large and complex sets of information in order to make the appropriate medical diagnoses. The quality of decisions depends on the selection of appropriate network weights, which define a transformation of the given input into a diagnosis. Denotational mathematics provide a promising framework for modeling deep learning networks and adjusting their behavior by adapting their weights for the given input. Furthermore, the fidelity of the network's output can be controlled by applying a regulator to the weights values. The authors show that Denotational Mathematics can serve as a rigorous framework for modeling and controlling deep learning networks, thereby enhancing the quality of medical decision making.
According to the ubiquitous computing paradigm, dispersed computers within the home environment can support the residents’ health by being aware of all the developing and evolving situations. The context-awareness of the supporting computers stems from the data acquisition of the occurring events at home. In some cases, different sensors provide input of identical type, thereby raising conflict-related issues. Thus, for each type of input data, fusion methods must be applied on the raw data to obtain a dominant input value. Also, for diagnostic inference purpose, data fusion methods must be applied on the values of the available classes of multiple contextual data structures. Dempster-Shafer theory offers the algorithmic tools to efficiently fuse the data of each input type or class. The employment of threading technology accelerates the computational process and carrying out benchmarks on publicly available data set, is shown to be more efficient. Thus, threading technology proved promising for home UbiHealth applications by lowering the number of required cooperating computers.
In the home ubiquitous computing (UbiComp) environment the wirelessly and ad-hoc networked computing devices are connected with sensors and actuators monitoring, recording, and intervening in the performed activities. The unattended applications operating at this environment must be fault tolerant and redundant. However, the lack of standardization and the uncontrolled evolvement of the developed situations at home result in a hostile environment for computer applications. Also, the need to support the individual's mobility at home increases further the level of difficulty of the associated computing efforts. The individual's mobility is supported providing computing services which can migrate from the currently running device to the neighboring one in order to follow every inhabitant's disposition. Migration presupposes the selection of adequate policies supported by the suitable software infrastructure to exploit the conceptual implementation of polymorphism that allows the preservation of functionality while autonomously transferred to another computing device. This paper provides a formal description with Denotational Mathematics of the operational prerequisites of such an autonomous system that achieves the migration of applications for the continuous support of the individual's mobility. The infrastructure must be capable of enforcing strategies and policies related to migration either by transporting the supporting applications or by referencing them. The achievement of the provision of healthcare at the UbiComp home presupposes the continuous support of the individual's mobility.
Home healthcare promises significant advantages over the traditional hospitalization, provided the support of the contemporary scientific and technological achievements. The ubiquitous computing paradigm suits the home healthcare provided that the dispersed computing devices in the home environment can actively participate in the interpretation of the developed, each time, medical context. Large numbers of disseminated sensors and computing devices, wirelessly and ad-hoc connected, present problems related to energy limitations and the patients' mobility introduce systemic complexity, uncertainty, and ambiguity. The formal description of such systems requires the inclusion of extensive details becoming tedious, if not impractical. Denotational Mathematics provides an alternative formal methodology framework capable to formally describe the constituting components, the performed operations, and the static and dynamic behaviors of complex system. Employing the expressive power of Denotational Mathematics, it is attempted to design a system that develops medically valid contextual contents adequate to support patients hospitalized at home. The formally described design provides the contents of the medical context enriched by the rules of the current state of medical knowledge. Denotational Mathematics provides the means to formally present the conceptual comparison between technically obtained medical contexts against predetermined medical contexts to obtain valid interpretations. The presented design has the ambition to formally describe the required cooperation of discrete ubiquitous computing applications to achieve the development of a commonly interpreted medical context at home.
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