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Related Concept Videos

Data Collection by Observations01:08

Data Collection by Observations

Data collection refers to a systematic way of obtaining, observing, measuring, and analyzing accurate information. Observational studies are one of the most widely used methods of data collection. It involves collecting data by observing the behavior and physical characteristics of a sample without making any modifications to the sample.
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Data collection gathers information needed to make accurate judgments about a patient's present condition. During a health history interview, subjective data is collected from the patient, their caregivers, or family members, and objective data is collected through observations and physical assessment. Patients are the primary source of subjective data. Thus information gathered from patients through interviews, observations, and physical examination is primary data. Secondary sources of data...
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The nursing history captures and records the patient's health status, so that a care plan evolves to meet the patient's individual needs. The nursing health history is a part of the initial assessment. A comprehensive history covers all health dimensions and plays a significant role in the assessment process. A comprehensive history includes the patient's biographical information, reasons for seeking health care, expectations, present and past health history, medications, and family,...
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Reporting and recording are crucial in data documentation. The timely, thorough, and accurate documentation of facts is essential when recording patient data. Failure to record findings during an assessment or interpretation of a problem will result in loss of information and make the patient document unreliable. The reader is left with general impressions if the information is not specific. A recording is documenting data of the individual's health information in a traceable, secure, and...

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VideoA11y: Method and Dataset for Accessible Video Description.

Chaoyu Li1, Sid Padmanabhuni1, Maryam S Cheema1

  • 1School of Computing and Augmented Intelligence, Arizona State University, Tempe, Arizona, USA.

Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. CHI Conference
|September 2, 2025
PubMed
Summary
This summary is machine-generated.

VideoA11y uses multimodal large language models (MLLMs) to create better video descriptions for blind and low vision (BLV) users. This approach significantly improves accessibility and user satisfaction compared to current methods.

Keywords:
Blind and Low Vision UsersMultimodal Large Language ModelsVideo AccessibilityVideo DescriptionVideo Understanding

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Area of Science:

  • Computer Science
  • Human-Computer Interaction
  • Artificial Intelligence

Background:

  • Video descriptions are vital for accessibility for blind and low vision (BLV) users.
  • Existing AI-generated descriptions often lack quality due to training data limitations, failing to meet BLV user needs.

Purpose of the Study:

  • To develop an approach (VideoA11y) using multimodal large language models (MLLMs) and accessibility guidelines for generating high-quality video descriptions for BLV users.
  • To create the VideoA11y-40K dataset, the largest collection of described videos for BLV users.

Main Methods:

  • Leveraging MLLMs and video accessibility guidelines to generate descriptions.
  • Curating the VideoA11y-40K dataset comprising 40,000 described videos.
  • Conducting experiments with sighted and BLV participants, and professional describers to evaluate description quality.

Main Results:

  • VideoA11y descriptions were found to be superior to novice human annotations.
  • The generated descriptions demonstrated comparable quality to trained human annotations in clarity, accuracy, objectivity, descriptiveness, and user satisfaction.
  • MLLMs fine-tuned on the VideoA11y-40K dataset produced high-quality accessible video descriptions.

Conclusions:

  • The VideoA11y approach effectively generates accessible video descriptions for BLV users.
  • The VideoA11y-40K dataset and MLLM fine-tuning advance the field of video accessibility.
  • This work offers a scalable solution for improving visual content access for BLV individuals.