ISO/IEEE 11073, IHE, and HL7: Fostering Standards-Based Safe, Reliable, Secure and Interoperable Biomedical Technologies

Sloane, Elliot B.; Thalassinidis, Angelo E.; Silva, Ricardo J.

doi:10.1109/secon.2018.8479122

Cited by 2 publications

(1 citation statement)

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“…A number of non-profit organizations, such as Integrating the Healthcare Enterprise (IHE) 4 and the Personal Connected Health Alliance (PCHA) 5 standard for the exchange, integration, sharing, and retrieval of electronic health information that supports clinical practice, covering in particular clinical systems within the HIS infrastructure [30]. PCHA regularly publishes the Continua design guidelines that define the framework of underlying standards and criteria to meet interoperability of components for applications used for monitoring personal health and wellness [27].…”

Section: Interoperabilitymentioning

confidence: 99%

Wi-Sense: a passive human activity recognition system using Wi-Fi and convolutional neural network and its integration in health information systems

et al. 2021

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A human activity recognition (HAR) system acts as the backbone of many human-centric applications, such as active assisted living and in-home monitoring for elderly and physically impaired people. Although existing Wi-Fi-based human activity recognition methods report good results, their performance is affected by the changes in the ambient environment. In this work, we present Wi-Sense—a human activity recognition system that uses a convolutional neural network (CNN) to recognize human activities based on the environment-independent fingerprints extracted from the Wi-Fi channel state information (CSI). First, Wi-Sense captures the CSI by using a standard Wi-Fi network interface card. Wi-Sense applies the CSI ratio method to reduce the noise and the impact of the phase offset. In addition, it applies the principal component analysis to remove redundant information. This step not only reduces the data dimension but also removes the environmental impact. Thereafter, we compute the processed data spectrogram which reveals environment-independent time-variant micro-Doppler fingerprints of the performed activity. We use these spectrogram images to train a CNN. We evaluate our approach by using a human activity data set collected from nine volunteers in an indoor environment. Our results show that Wi-Sense can recognize these activities with an overall accuracy of 97.78%. To stress on the applicability of the proposed Wi-Sense system, we provide an overview of the standards involved in the health information systems and systematically describe how Wi-Sense HAR system can be integrated into the eHealth infrastructure.

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Section: Interoperabilitymentioning

confidence: 99%