Usability Engineering Recommendations for Next-Gen Integrated Interoperable Medical Devices

2021 ◽  
Vol 55 (4) ◽  
pp. 132-142
Author(s):  
Paolo Masci ◽  
Sandy Weininger
Keyword(s):  
Medical Devices ◽  
Hazard Analysis ◽  
Technical Committee ◽  
Medical System ◽  
Analysis Method ◽  
Usability Engineering ◽  
Design Problems ◽  
Model Based ◽  
Key Aspects ◽  
System Prototype

Abstract This article reports on the development of usability engineering recommendations for next-generation integrated interoperable medical devices. A model-based hazard analysis method is used to reason about possible design anomalies in interoperability functions that could lead to use errors. Design recommendations are identified that can mitigate design problems. An example application of the method is presented based on an integrated medical system prototype for postoperative care. The AAMI/UL technical committee used the results of the described analysis to inform the creation of the Interoperability Usability Concepts, Annex J, which is included in the first edition of the new ANSI/AAMI/UL 2800-1:2019 standard on medical device interoperability. The presented work is valuable to experts developing future revisions of the interoperability standard, as it documents key aspects of the analysis method used to create part of the standard. The contribution is also valuable to manufacturers, as it demonstrates how to perform a model-based analysis of use-related aspects of a medical system at the early stages of development, when a concrete implementation of the system is not yet available.

scholarly journals Medical Device Integration Model Based on the Internet of Things

2015 ◽  
Vol 9 (1) ◽  
pp. 256-261 ◽  
Author(s):  
Aiyu Hao ◽  
Ling Wang
Keyword(s):  
Information System ◽  
Internet Of Things ◽  
Medical Device ◽  
Medical Devices ◽  
Medical Information ◽  
Medical Information System ◽  
The Internet ◽  
Model Based ◽  
Device Integration ◽  
The Internet Of Things

At present, hospitals in our country have basically established the HIS system, which manages registration, treatment, and charge, among many others, of patients. During treatment, patients need to use medical devices repeatedly to acquire all sorts of inspection data. Currently, the output data of the medical devices are often manually input into information system, which is easy to get wrong or easy to cause mismatches between inspection reports and patients. For some small hospitals of which information construction is still relatively weak, the information generated by the devices is still presented in the form of paper reports. When doctors or patients want to have access to the data at a given time again, they can only look at the paper files. Data integration between medical devices has long been a difficult problem for the medical information system, because the data from medical devices lack mandatory unified global standards and have outstanding heterogeneity of devices. In order to protect their own interests, manufacturers use special protocols, etc., thus causing medical devices to still be the "lonely island" of hospital information system. Besides, unfocused application of the data will lead to failure to achieve a reasonable distribution of medical resources. With the deepening of IT construction in hospitals, medical information systems will be bound to develop toward mobile applications, intelligent analysis, and interconnection and interworking, on the premise that there is an effective medical device integration (MDI) technology. To this end, this paper presents a MDI model based on the Internet of Things (IoT). Through abstract classification, this model is able to extract the common characteristics of the devices, resolve the heterogeneous differences between them, and employ a unified protocol to integrate data between devices. And by the IoT technology, it realizes interconnection network of devices and conducts associate matching between the data and the inspection with the terminal device in a timely manner.

scholarly journals Model-Based Analysis of Yo-yo Throwing Motion on Single-Link Manipulator

2020 ◽  
Vol 32 (4) ◽  
pp. 822-831
Author(s):  
Hokuto Miyakawa ◽  
◽  
Takuma Nemoto ◽  
Masami Iwase
Keyword(s):  
Angular Velocity ◽  
Numerical Simulations ◽  
Integrated Model ◽  
Analysis Method ◽  
Model Based ◽  
Single Link ◽  
Throwing Motion ◽  
Model Based Analysis

This paper presents a method for analyzing the throwing motion of a yo-yo based on an integrated model of a yo-yo and a manipulator. Our previous integrated model was developed by constraining a model of a white painted commercial yo-yo and a model of a plain single-link manipulator with certain constraining conditions placed between two models. However, for the yo-yo model, the collisions between the string and the axle of the yo-yo were not taken into account. To avoid this problem, we estimate some of the yo-yo parameters from the experiments, thereby preserving the functionality of the model. By applying the new integrated model with the identified parameters, we analyze the throwing motion of the yo-yo through numerical simulations. The results of which show the ranges of the release angle and the angular velocity of the joint of the manipulator during a successful throw. In conclusion, the proposed analysis method is effective in analyzing the throwing motion of a manipulator.

scholarly journals Incorporating pulse wave velocity into model-based pulse contour analysis method for estimation of cardiac stroke volume

2020 ◽  
Vol 195 ◽  
pp. 105553
Author(s):  
Rachel Smith ◽  
Joel Balmer ◽  
Christopher G. Pretty ◽  
Tashana Mehta-Wilson ◽  
Thomas Desaive ◽  
...  
Keyword(s):  
Stroke Volume ◽  
Pulse Wave Velocity ◽  
Wave Velocity ◽  
Pulse Wave ◽  
Pulse Contour Analysis ◽  
Pulse Contour ◽  
Analysis Method ◽  
Contour Analysis ◽  
Model Based ◽  
Cardiac Stroke Volume

Model-Based EEG Analysis: Proposal and Verification of Mathematical Model for Application to Neurotechnology

2019 ◽  
Author(s):  
Kenyu Uehara ◽  
Takashi Saito
Keyword(s):  
Mathematical Model ◽  
Binary Classification ◽  
Least Square ◽  
Mental Condition ◽  
Support Vector ◽  
Model Parameters ◽  
Eeg Analysis ◽  
Analysis Method ◽  
Model Based ◽  
Proposed Model

Abstract We have modeled dynamics of EEG with one degree of freedom nonlinear oscillator and examined the relationship between mental state of humans and model parameters simulating behavior of EEG. At the IMECE conference last year, Our analysis method identified model parameters sequentially so as to match the waveform of experimental EEG data of the alpha band using one second running window. Results of temporal variation of model parameters suggested that the mental condition such as degree of concentration could be directly observed from the dynamics of EEG signal. The method of identifying the model parameters in accordance with the EEG waveform is effective in examining the dynamics of EEG strictly, but it is not suitable for practical use because the analysis (parameter identification) takes a long time. Therefore, the purpose of this study is to test the proposed model-based analysis method for general application as a neurotechnology. The mathematical model used in neuroscience was improved for practical use, and the test was conducted with the cooperation of four subjects. model parameters were experimentally identified approximately every one second by using least square method. We solved a binary classification problem of model parameters using Support Vector Machine. Results show that our proposed model-based EEG analysis is able to discriminate concentration states in various tasks with an accuracy of over 80%.

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