SpecTalk: Conforming IoT Implementations to Sensor Specifications

Lin, Yi‐Bing; Chou, Sheng-Lin

doi:10.3390/s21165260

Cited by 3 publications

(2 citation statements)

References 15 publications

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“…Taiwan’s Ministry of Health and Welfare endorses HL7 FHIR for its streamlined exchange process, leading CMUH-RICU to adopt FHIR as the standard data format. Medical device data are converted to FHIR via a translation mechanism in the processing layer ( Figure 5 (3)), using a web-based Graphical User Interface (GUI) for device feature management (DFM; Figure 7 c) [ 25 ].…”

Section: Design and Implementation Of The Cmuh-ricu Command Centermentioning

confidence: 99%

Design and Implementation of an Intensive Care Unit Command Center for Medical Data Fusion

Feng,

Chen,

Lin

et al. 2024

Sensors

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The rapid advancements in Artificial Intelligence of Things (AIoT) are pivotal for the healthcare sector, especially as the world approaches an aging society which will be reached by 2050. This paper presents an innovative AIoT-enabled data fusion system implemented at the CMUH Respiratory Intensive Care Unit (RICU) to address the high incidence of medical errors in ICUs, which are among the top three causes of mortality in healthcare facilities. ICU patients are particularly vulnerable to medical errors due to the complexity of their conditions and the critical nature of their care. We introduce a four-layer AIoT architecture designed to manage and deliver both real-time and non-real-time medical data within the CMUH-RICU. Our system demonstrates the capability to handle 22 TB of medical data annually with an average delay of 1.72 ms and a bandwidth of 65.66 Mbps. Additionally, we ensure the uninterrupted operation of the CMUH-RICU with a three-node streaming cluster (called Kafka), provided a failed node is repaired within 9 h, assuming a one-year node lifespan. A case study is presented where the AI application of acute respiratory distress syndrome (ARDS), leveraging our AIoT data fusion approach, significantly improved the medical diagnosis rate from 52.2% to 93.3% and reduced mortality from 56.5% to 39.5%. The results underscore the potential of AIoT in enhancing patient outcomes and operational efficiency in the ICU setting.

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Section: Design and Implementation Of The Cmuh-ricu Command Centermentioning

confidence: 99%

Design and Implementation of an Intensive Care Unit Command Center for Medical Data Fusion

Feng,

Chen,

Lin

et al. 2024

Sensors

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“…This approach was used in the ISCTE-IUL University Campus where this 3D IoT representation was provided on mobile devices, and helped the campus reach sustainability goals. The IoT technology is used in multiple related applications, as summarized in [18], where one also proposed a SpecTalk platform to build the code necessary for IoT application against the specifications of the Taiwan Association of Information and Communication Standards (TAICS). The program built by the platform accommodates the Application Programming Interface (API) for the devices under test, which is then used by SpecTalk to test devices with TAICS data formats.…”

Section: Mobility In a Public Spacementioning

confidence: 99%

Community- and Data-Driven Services for Multi-Policy Pedestrian Routing

Damian

Ioniţă

Anton

2022

Sensors

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Pedestrian routing is important in a multitude of public spaces, especially those characterized by a large number of newcomers. Their needs may be diverse, with priority for the shortest path, the less crowded or the less polluted one, the accessibility for reduced mobility, or the sheltering from unfavorable weather conditions. Hence, typical graph-based routing must be enriched to support multiple policies, at the choice of each person. The paper proposes a systemic approach and a set of services for orientation and accessibility, which are both community-driven and data-driven, for correctly perceiving the routing necessities and the surrounding situation. The response time to a pathfinding query depends on the types of policies applied and not only on their number, because each of them contributes to the customization of the weighted graph, although it refers to the same physical space traversed by pedestrians. The paper also presents results of loading tests for up to 5000 Virtual Users, inspired from real-life requirements and executed on a graph that models a real building in our university; different policies are applied to assess performance metrics, with simulated community feedback and sensor data.

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Examining IoT-Based Smart Campus Adoption Model: An Investigation Using Two-Stage Analysis Comprising Structural Equation Modelling and Artificial Neural Network

Sneesl,

Jusoh,

Jabar

et al. 2023

IEEE Access

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The progress and evolution of technology have been rapidly transforming various aspects of our society and daily lives, including colleges and campuses into smarter environments compared to the past. Despite the numerous advantages offered by cutting-edge technologies, such as IoT-based smart campuses, academic research on their implementation suffers from a significant lack of comprehensive information necessary to deliver efficient smart campus solutions. Therefore, the focus of this study is to investigate the significance of IoT-based smart campus adoption from 14 proposed hypotheses. The researchers collected data from stakeholders affiliated with universities in Iraq, resulting in a dataset of 442 observations. To analyze this data, a two-stage approach was employed, consisting of structural equation modeling (SEM) and reevaluated with the artificial neural networks (ANN) method. The findings provide evidence supporting the significance of various constructs. In particular, the model demonstrates satisfactory predictive relevance, indicating its effectiveness in making accurate predictions or forecasts. The ANN analysis suggests that the model has predictive capabilities. Moreover, the study findings support the importance of perceived usefulness in technology-specific factors, facilitating conditions, and propagation in organizational-specific factors, government support, social influence, and external pressure in environmental-specific factors, as well as privacy concerns, self-efficacy, satisfaction, and domain-specific knowledge in end-user-specific factors. Four hypotheses related to perceived ease of use, service collaboration, habit, and innovativeness were rejected. Notably, the study identifies propagation as the most significant predictor in the ANN analysis. The conclusions of this study can be beneficial for university administrators, manufacturers, and policymakers in understanding the essential components of smart campuses to enhance the adoption and maximize the effectiveness of smart solutions.

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SpecTalk: Conforming IoT Implementations to Sensor Specifications

Cited by 3 publications

References 15 publications

Design and Implementation of an Intensive Care Unit Command Center for Medical Data Fusion

Design and Implementation of an Intensive Care Unit Command Center for Medical Data Fusion

Community- and Data-Driven Services for Multi-Policy Pedestrian Routing

Examining IoT-Based Smart Campus Adoption Model: An Investigation Using Two-Stage Analysis Comprising Structural Equation Modelling and Artificial Neural Network

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