A mobile context-aware medical training system for the reduction of pathogen transmission

Abstract

Even with the adoption of modern technology within the medical system, the spread of deadly pathogens remains a silent, yet deadly killer. Indeed, e-health, and in particular m-health is at the forefront of the computing portion of modern medical care. It follows the ubiquity of mobile computing devices that has become synonymous with modern life. Although the focus of m-health is patient based, this paper attempts to provide a focus on the healthcare service provider in an attempt to reduce the fatalities due to pathogen transmissions in care facilities. This paper proposes, and describes the creation and evaluation of an AI based context-aware mobile learning system designed to provide real-time training and support for medical cleaning staff. This learning system aims to bridge the gap between context-aware learning systems and m-health. As such, the system provides context information to the learner about the various threats and best ways to deal with possible pathogens in a long-term care scenario. The evaluation, field tested within the adult long-term care system demonstrates the benefit and validity of the system in both training and ongoing usage within the medical system.

Affordances and Core Functions of Smart Learning Environments: A Systematic Literature Review

“…SLEs record data from stakeholders' context throughout learning activities using sensors installed in the environment [58], or in embedded systems [34], [44], [52], [59], such as smartphones and wearables. The results of the analysis show that tracking and monitoring affordances (traceable onwards) were identified in 31% of the publications.…”

“…Learning management systems (LMSs) were referenced in [28], [29], [35], [38], [39], [42]- [46], [49], [50], [53], [58], [59], [62], [63], [87], [88] Desktop computers 17 (25%) [19], [21], [22], [24], [31], [33], [40], [42], [43], [60], [63], [64], [67], [73], [77], [78], [87] Learning management system 14 (20.59%)…”

“…[21]- [23], [39], [50], [55], [62], [69]- [72], [79], [80], [86] Cameras 11 (13.41%) [20], [34], [35], [43], [44], [47], [49], [52], [63], [65], [66] Microcontrollers 7 (10.29%) [34], [47], [48], [58], [59], [66], [67] Virtual/remote laboratories 6 (8.82%) [20], [47], [53], [73], [74], [82] Biometric sensors 5 (7.35%) [34], [52], [66], [67], [71] Environmental sensors 5 (7.35%) [41], [53], [59], [66],…”

“…[21]- [23], [30], [31], [36], [38], [41], [50], [53], [57], [61], [68], [70], [71], [79], [80] Data mining 9 (13.24%) [22], [27], [31], [36], [54], [67], [69], [72], [80] Ontologies 6 (8.82%) [21], [22], [25], [32], [60], [62] Artificial intelligence 6 (8.82%) [19], [54], [59], [66], [75], [78] Cloud computing 5 (7.35%) [34], [44], [55], [56] favored the inclusion of data processing and analysis techniques in SLEs (see Table V). The results of the analysis are presented considering that some of these data processing techniques might overlap in specific taxonomies.…”

“…Ontologies are frequently used in educational contexts to formulate models of knowledge that can be understood by both humans and machines. For example, the work by Lalingkar [28], [29], [35], [38], [39], [42]- [46], [49], [50], [53], [58], [59], [62], [63], [87], [88] Desktop computers 17 (25%) [19], [21], [22], [24], [31], [33], [40], [42], [43], [60], [63], [64], [67], [73], [77], [78], [87] Data visualizations 12 (17.65%) [21], [28], [41], [50], [53], [55], [58], [70], [71], [73], [80], [84] Videos 7 (10.29%) [30], [34], [43], [46],…”