scholarly journals Relationship between refractive correction, visual symptoms, and optical device selection for low-vision patients in Taiwan

2020 ◽  
Vol 13 (4) ◽  
pp. 249-256
Author(s):  
Lung-Hui Tsai ◽  
Hsi-Pao Hsieh ◽  
Po-Sen Chen ◽  
Chia-Lin Jou ◽  
Kai-yuan Tseng ◽  
...  
Keyword(s):  
Low Vision ◽  
Optical Device ◽  
Visual Symptoms ◽  
Device Selection ◽  
Refractive Correction ◽  
Selection For

Device Selection for Failure Analysis of Chain Fails Using Diagnosis Driven Yield Analysis

2011 ◽  
Author(s):  
Chris Schuermyer ◽  
Brady Benware ◽  
Graham Rhodes ◽  
Davide Appello ◽  
Vincenzo Tancorre ◽  
...  
Keyword(s):  
Failure Analysis ◽  
Case Studies ◽  
Yield Analysis ◽  
Analysis Methodology ◽  
Device Selection ◽  
Selection For ◽  
Random Defects

Abstract This work presents the first application of a diagnosis driven approach for identifying systematic chain fail defects in order to reduce the time spent in failure analysis. The zonal analysis methodology that is applied separates devices into systematic and random populations of chain fails in order to prevent submitting random defects for failure analysis. Two silicon case studies are presented to validate the production worthiness of diagnosis driven yield analysis for chain fails. The defects uncovered in these case studies are very subtle and would be difficult to identify with any other methodology.

Device selection for carotid stenting: reviewing the evidence

2017 ◽  
Vol 15 (10) ◽  
pp. 787-796 ◽  
Author(s):  
Carlo Setacci ◽  
Mariagnese Mele ◽  
Gianmarco de Donato ◽  
Giulia Mazzitelli ◽  
Domenico Benevento ◽  
...  
Keyword(s):  
Carotid Stenting ◽  
Device Selection ◽  
Selection For

scholarly journals Client-Centric Access Device Selection for Heterogeneous QoS Requirements in Beyond 5G IoT Networks

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 219820-219836
Author(s):  
Muhammad Asad ◽  
Saad Qaisar ◽  
Abdul Basit
Keyword(s):  
Access Device ◽  
Device Selection ◽  
Selection For

scholarly journals Human-Centered Design Strategies for Device Selection in mHealth Programs: Development of a Novel Framework and Case Study

10.2196/16043 ◽  
2020 ◽  
Vol 8 (5) ◽  
pp. e16043
Author(s):  
Ashley Marie Polhemus ◽  
Jan Novák ◽  
Jose Ferrao ◽  
Sara Simblett ◽  
Marta Radaelli ◽  
...  
Keyword(s):  
Health Care ◽  
Central Nervous System ◽  
Nervous System ◽  
Human Centered Design ◽  
Regulatory Constraints ◽  
Device Selection ◽  
Selection For ◽  
Selection Framework ◽  
Stakeholder Needs

Background Despite the increasing use of remote measurement technologies (RMT) such as wearables or biosensors in health care programs, challenges associated with selecting and implementing these technologies persist. Many health care programs that use RMT rely on commercially available, “off-the-shelf” devices to collect patient data. However, validation of these devices is sparse, the technology landscape is constantly changing, relative benefits between device options are often unclear, and research on patient and health care provider preferences is often lacking. Objective To address these common challenges, we propose a novel device selection framework extrapolated from human-centered design principles, which are commonly used in de novo digital health product design. We then present a case study in which we used the framework to identify, test, select, and implement off-the-shelf devices for the Remote Assessment of Disease and Relapse-Central Nervous System (RADAR-CNS) consortium, a research program using RMT to study central nervous system disease progression. Methods The RADAR-CNS device selection framework describes a human-centered approach to device selection for mobile health programs. The framework guides study designers through stakeholder engagement, technology landscaping, rapid proof of concept testing, and creative problem solving to develop device selection criteria and a robust implementation strategy. It also describes a method for considering compromises when tensions between stakeholder needs occur. Results The framework successfully guided device selection for the RADAR-CNS study on relapse in multiple sclerosis. In the initial stage, we engaged a multidisciplinary team of patients, health care professionals, researchers, and technologists to identify our primary device-related goals. We desired regular home-based measurements of gait, balance, fatigue, heart rate, and sleep over the course of the study. However, devices and measurement methods had to be user friendly, secure, and able to produce high quality data. In the second stage, we iteratively refined our strategy and selected devices based on technological and regulatory constraints, user feedback, and research goals. At several points, we used this method to devise compromises that addressed conflicting stakeholder needs. We then implemented a feedback mechanism into the study to gather lessons about devices to improve future versions of the RADAR-CNS program. Conclusions The RADAR device selection framework provides a structured yet flexible approach to device selection for health care programs and can be used to systematically approach complex decisions that require teams to consider patient experiences alongside scientific priorities and logistical, technical, or regulatory constraints.

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