An ontology-driven approach to mobile data collection applications for the healthcare industry

Henriques, Gabriela; Lamanna, Laura; Kotowski, Daniel; Hlomani, Hlomani; Stacey, Deborah; Baker, Philip; Harper, Sherilee L.

doi:10.1007/s13721-013-0038-6

Cited by 11 publications

(3 citation statements)

References 15 publications

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“…Henriques et al [24] discussed the importance of data collection in the medical industry. According to this study, traditional methods of data collection such as pen and paper are costly and time-consuming.…”

Section: Discussionmentioning

confidence: 99%

An Android Application for Estimating Muscle Onset Latency using Surface EMG Signal

et al. 2019

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Background: Electromyography (EMG) signal processing and Muscle Onset Latency (MOL) are widely used in rehabilitation sciences and nerve conduction studies. The majority of existing software packages provided for estimating MOL via analyzing EMG signal are computerized, desktop based and not portable; therefore, experiments and signal analyzes using them should be completed locally. Moreover, a desktop or laptop is required to complete experiments using these packages, which costs.Objective: Develop a non-expensive and portable Android application (app) for estimating MOL via analyzing surface EMG.Material and Methods: A multi-layer architecture model was designed for implementing the MOL estimation app. Several Android-based algorithms for analyzing a recorded EMG signal and estimating MOL was implemented. A graphical user interface (GUI) that simplifies analyzing a given EMG signal using the presented app was developed too.Results: Evaluation results of the developed app using 10 EMG signals showed promising performance; the MOL values estimated using the presented app are statistically equal to those estimated using a commercial Windows-based surface EMG analysis software (MegaWin 3.0). For the majority of cases relative error <10%. MOL values estimated by these two systems are linearly related, the correlation coefficient value ~ 0.93. These evaluations revealed that the presented app performed as well as MegaWin 3.0 software in estimating MOL.Conclusions: Recent advances in smart portable devices such as mobile phones have shown the great capability of facilitating and decreasing the cost of analyzing biomedical signals, particularly in academic environments. Here, we developed an Android app for estimating MOL via analyzing the surface EMG signal. Performance is promising to use the app for teaching or research purposes.

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Section: Discussionmentioning

confidence: 99%

An Android Application for Estimating Muscle Onset Latency using Surface EMG Signal

et al. 2019

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“…No comprehensive ontology was identified. Ontologies that explicitly model and structure the domain have been proposed for various purposes in the health care domain, such as interoperability [80] and regulating access control for internet of things-based health care [40,81]. The ontology proposed in this paper uses the HSPAMI study areas as an organizing principle for the existing empirically supported research results [40,81].…”

Section: Use Of Existing Ontologiesmentioning

confidence: 99%

Mapping the Psychosocialcultural Aspects of Healthcare Professionals’ Information Security Practices: Systematic Mapping Study

et al. 2021

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Background Data breaches in health care are on the rise, emphasizing the need for a holistic approach to mitigation efforts. Objective The purpose of this study was to develop a comprehensive framework for modeling and analyzing health care professionals’ information security practices related to their individual characteristics, such as their psychological, social, and cultural traits. Methods The study area was a hospital setting under an ongoing project called the Healthcare Security Practice Analysis, Modeling, and Incentivization (HSPAMI) project. A literature review was conducted for relevant theories and information security practices. The theories and security practices were used to develop an ontology and a comprehensive framework consisting of psychological, social, cultural, and demographic variables. Results In the review, a number of psychological, social, and cultural theories were identified, including the health belief model, protection motivation theory, theory of planned behavior, and social control theory, in addition to some social demographic variables, to form a comprehensive set of health care professionals’ characteristics. Furthermore, an ontology was developed from these theories to systematically organize the concepts. The framework, called the psychosociocultural (PSC) framework, was then developed from the various combined psychological and sociocultural attributes of the ontology. The Human Aspect of Information Security Questionnaire was adopted as a comprehensive tool for gathering staff security practices as mediating variables in the framework. Conclusions Data breaches occur often in health care today. This frequency has been attributed to the lack of experience of health care professionals in information security, the lack of development of conscious care security practices, and the lack of motivation to incentivize health care professionals. The frequent data breaches in health care threaten the mutual trust between health care professionals and patients, which implicitly impacts the quality of the health care service. The modeling and analysis of health care professionals’ security practices can be conducted with the PSC framework by combining methods of statistical survey, observations, and interviews in relation to PSC variables, such as perceptions (perceived benefits, perceived threats, and perceived barriers) or psychological traits, social factors, cultural factors, and social demographics.

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“…These set of conditional tests are called production rules and can be combined with ontologies for efficient processing of information. An ontology provides formal framework for organization and representation of domain knowledge (Henriques et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

Proposing a decision support system for automated mobile asthma monitoring in remote areas

Uwaoma

Mansingh

2017

Information Technology for Development

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Advances in mobile computing have paved the way for the development of several health applications using Smartphone as a platform for data acquisition, analysis and presentation. Such areas where m-health systems have been extensively deployed include monitoring of long-term health conditions like Cardio-Vascular Diseases and pulmonary disorders, as well as detection of changes from baseline measurements of such conditions. Asthma is one of the respiratory conditions with growing concern across the globe due to the economic, social and emotional burden associated with the ailment. The management and control of asthma can be improved by consistent monitoring of the condition in real-time since attack could occur anytime and anywhere. This paper proposes the use of smartphone equipped with built-in sensors, to capture and analyse early symptoms of asthma triggered by exercise. The system design is based on Decision Support System (DSS) techniques for measuring and analysing the level and type of patient's physical activity as well as weather conditions that predispose asthma attack. Preliminary results show that smartphones can be used to monitor and detect asthma symptoms without other networked devices.This would enhance the usability of the health system while ensuring user's data privacy, and reducing the overall cost of system deployment. Further, the proposed system can serve as a handy tool for a quick medical response for asthmatics in low-income countries where there are limited access to specialized medical devices and shortages of health professionals. Development of such monitoring systems signals a positive response to lessen the global burden of asthma.

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An ontology-driven approach to mobile data collection applications for the healthcare industry

Cited by 11 publications

References 15 publications

An Android Application for Estimating Muscle Onset Latency using Surface EMG Signal

An Android Application for Estimating Muscle Onset Latency using Surface EMG Signal

Mapping the Psychosocialcultural Aspects of Healthcare Professionals’ Information Security Practices: Systematic Mapping Study

Proposing a decision support system for automated mobile asthma monitoring in remote areas

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