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Related Concept Videos

Data Collection I01:30

Data Collection I

7.6K
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...
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Data Collection II01:29

Data Collection II

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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...
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Data Collection by Observations01:08

Data Collection by Observations

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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.
An astronomer viewing the motion and brightness of stars in the sky and recording the data is an example of observational data collection. A botanist recording...
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Data Collection by Experiments01:13

Data Collection by Experiments

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Data collection is a systematic method of obtaining, observing, measuring, and analyzing accurate information. An experimental study is a standard method of data collection that involves the manipulation of the samples by applying some form of treatment prior to data collection. It refers to manipulating one variable to determine its changes on another variable. The sample subjected to treatment is known as “experimental units.”
An example of the experimental method is a public...
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Data Collection III01:05

Data Collection III

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The physical assessment examines the patient for objective data that defines the patient's condition, and aids in formulating the nursing care plan. The purpose of physical assessment is a health status appraisal, which includes identifying health problems, and establishing a database for nursing intervention.
The principles to begin the physical assessment include conducting a comprehensive or problem-related history in a quiet, well-lit room, emphasizing privacy and comfort for the...
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Data Collection by Survey01:07

Data Collection by Survey

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The systematic method of obtaining and analyzing accurate information of a population is called data collection. A survey is a standard method of data collection that involves collecting information from a target human population about their experience, opinion, or knowledge of a product, service, or process. The responses are recorded and interpreted. The most common survey examples are written questionnaires, face-to-face or telephonic conversations, focus groups, and electronic (e-mail or...
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Related Experiment Video

Updated: Nov 23, 2025

Author Spotlight: Automated Deep Brain Stimulation for Parkinson's Disease - Exploring the Possibilities and Challenges of Home Monitoring
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On-Device Deep Personalization for Robust Activity Data Collection.

Nattaya Mairittha1, Tittaya Mairittha1, Sozo Inoue1

  • 1Graduate School of Engineering, Kyushu Institute of Technology, 1-1 Sensui-cho, Tobata-ku, Kitakyushu-shi, Fukuoka 804-8550, Japan.

Sensors (Basel, Switzerland)
|December 30, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces on-device personalization for activity recognition, using personalized activity estimates to boost user engagement and improve data labeling quality for mobile sensing. The system enhances accuracy while reducing computational costs on mobile devices.

Keywords:
activity recognitiondata collectiondeep learningfine-tuningon-device personalizationsmartphone sensorsuser feedback

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Area of Science:

  • Computer Science
  • Machine Learning
  • Human-Computer Interaction

Background:

  • Activity data collection relies heavily on user engagement for labeling.
  • Mobile deep neural networks offer on-device intelligence.
  • On-device personalization is key for efficient mobile sensing systems.

Purpose of the Study:

  • Propose a novel on-device personalization for activity recognition data labeling.
  • Enhance user motivation and data quality through personalized feedback.
  • Develop an efficient system for mobile activity recognition.

Main Methods:

  • Fine-tuning a Deep Recurrent Neural Network (DRNN) for data efficiency.
  • Employing model pruning to reduce on-device computation costs.
  • Integrating DRNN fine-tuning and model pruning for a robust system.

Main Results:

  • The proposed system significantly improved activity recognition accuracy for individual users.
  • Reduced computational cost and inference latency on mobile devices.
  • Demonstrated feasibility and capability in realistic settings with over 16,800 activity windows.

Conclusions:

  • On-device personalization effectively addresses challenges in mobile activity data collection.
  • The integrated approach offers a personalized user experience and efficient mobile sensing.
  • Highlights future research in efficient activity data collection designs.