Quality assessment of emergency corrective maintenance of critical care ventilators within the context of COVID-19 in Sao Paulo, Brazil

This technical report presents the quality assessment process for the emergency corrective maintenance of critical care ventilators in a node, IPT-POLI, of a voluntary network that is part of the initiative +Maintenance of Ventilators, led by the National Service of Industrial Training (SENAI) and its Integrated Manufacturing and Technology Center (CIMATEC) to perform maintenance on unused mechanical ventilators in the context of the COVID-19 pandemic in Brazil. A procedure was established for the quality assessment of equipment subjected to corrective emergency maintenance, covering the essential aspects of the three primary standards (ABNT NBR IEC 60601-1: 2010+A1:2016, ABNT NBR ISO IEC 62353: 2019, and ABNT NBR ISO 80601-2-12:2014) for performance and safety assessment. A set of nine critical care ventilators was evaluated considering the following parameters: leakage current, protective ground resistance, control accuracy, delivered oxygen test, and alarms. The evaluated ventilators underwent corrective emergency maintenance before performance and safety assessments. In the electrical safety tests, all equipment presented values prescribed for the standard. However, the assessment of ventilator parameters revealed that their performance was below the standard. Finally, quality assessment reports were sent to the clinical engineering departments at hospitals. Thus, it can be concluded that criteria selection for the quality assessment in critical care ventilators is crucial and of great significance for future pandemic scenarios, such as the situation experienced during the COVID-19 pandemic.

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Toward an Automatic Quality Assessment of Voice-Based Telemedicine Consultations: A Deep Learning Approach

Sensors ◽

10.3390/s21093279 ◽

2021 ◽

Vol 21 (9) ◽

pp. 3279

Author(s):

Maria Habib ◽

Mohammad Faris ◽

Raneem Qaddoura ◽

Manal Alomari ◽

Alaa Alomari ◽

...

Keyword(s):

Neural Networks ◽

Deep Learning ◽

Quality Assessment ◽

Transcript Level ◽

Assessment Process ◽

Classification Model ◽

Systematic Evaluation ◽

Transcript Levels ◽

Perceptual Evaluation

Maintaining a high quality of conversation between doctors and patients is essential in telehealth services, where efficient and competent communication is important to promote patient health. Assessing the quality of medical conversations is often handled based on a human auditory-perceptual evaluation. Typically, trained experts are needed for such tasks, as they follow systematic evaluation criteria. However, the daily rapid increase of consultations makes the evaluation process inefficient and impractical. This paper investigates the automation of the quality assessment process of patient–doctor voice-based conversations in a telehealth service using a deep-learning-based classification model. For this, the data consist of audio recordings obtained from Altibbi. Altibbi is a digital health platform that provides telemedicine and telehealth services in the Middle East and North Africa (MENA). The objective is to assist Altibbi’s operations team in the evaluation of the provided consultations in an automated manner. The proposed model is developed using three sets of features: features extracted from the signal level, the transcript level, and the signal and transcript levels. At the signal level, various statistical and spectral information is calculated to characterize the spectral envelope of the speech recordings. At the transcript level, a pre-trained embedding model is utilized to encompass the semantic and contextual features of the textual information. Additionally, the hybrid of the signal and transcript levels is explored and analyzed. The designed classification model relies on stacked layers of deep neural networks and convolutional neural networks. Evaluation results show that the model achieved a higher level of precision when compared with the manual evaluation approach followed by Altibbi’s operations team.

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A High Fidelity Quality Assessment of High Pressure Turbine Blades Using Surface Curvature and Gradient-Based Adjoint

Volume 2B: Turbomachinery ◽

10.1115/gt2018-75157 ◽

2018 ◽

Author(s):

Akhil Mulloth ◽

Gabriel Banks ◽

Giulio Zamboni ◽

Simon Bather

Keyword(s):

Quality Assessment ◽

Turbine Blades ◽

Three Dimensional ◽

Surface Curvature ◽

Assessment Process ◽

Screening Process ◽

Profile Tolerance ◽

Gradient Based ◽

Parts Manufacturing ◽

The Impact

Gas turbine performance is highly dependent on the quality of the manufactured parts. Manufacturing variations in the parts can significantly alter the performance, especially efficiency and thus SFC. The legacy process is to accept variations within predefined profile tolerance limits and a few other qualitative parameters, mostly at a few, key two-dimensional aerofoil sections. With the widespread use of White light scans and other similar three-dimensional scans, this has improved to include the three-dimensional profile. The future however may lie with performance based quality assessment of manufactured parts, combined with quantitative surface quality assessment to implement an intelligent screening process for the parts. The adjoint method, typically used for shape optimization is adapted to provide a prediction of the impact on performance due to manufacturing variations. The work presented outlines a three stage quality assessment process for manufactured parts, involving three-dimensional profile tolerance based screening, followed by a surface curvature based screening and finally an Adjoint based performance prediction.

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Virtual Reality Training Applications in Industry - Towards a User-friendly Application Design

10.30844/wgab_2021_4 ◽

2021 ◽

pp. 59-80

Author(s):

Benjamin Knoke ◽

◽

Moritz Quandt ◽

Michael Freitag ◽

Klaus-Dieter Thoben

Keyword(s):

Virtual Reality ◽

Technology Acceptance ◽

User Interfaces ◽

Interface Design ◽

Design Guidelines ◽

Electrical Safety ◽

Industrial Training ◽

Current State ◽

Vr Training ◽

User Friendly

The purpose of this research is to aggregate and discuss the validity of challenges and design guidelines regarding industrial Virtual Reality (VR) training applications. Although VR has seen significant advancements in the last 20 years, the technology still faces multiple research challenges. The challenges towards industrial VR applications are imposed by a limited technological maturity and the need to achieve industrial stakeholders' technology acceptance. Technology acceptance is closely connected with the consideration of individual user requirements for user interfaces in virtual environments. This paper analyses the current state-of-the-art in industrial VR applications and provides a structured overview of the existing challenges and applicable guidelines for user interface design, such as ISO 9241-110. The validity of the identified challenges and guidelines is discussed against an industrial training scenario on electrical safety during maintenance tasks.

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