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Nursing Assessment

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The two sources for collecting information are primary and secondary. After gathering information, interpretation and validation help to complete the data. The purpose of assessment is to establish data with the initial information, to interpret data about the patient's perceived needs and health problems, and to respond to these problems identified.
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Nursing Evaluation

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Related Experiment Video

Updated: Jun 21, 2026

Design and Analysis for Fall Detection System Simplification

Design and Analysis for Fall Detection System Simplification

Published on: April 6, 2020

A fall and near-fall assessment and evaluation system.

Anh Dinh¹, Yang Shi, Daniel Teng

¹Department of Electrical and Computer Engineering, University of Saskatchewan, Canada.

The Open Biomedical Engineering Journal

|August 8, 2009

Summary

The FANFARE project created a wearable system for fall detection. Machine learning analysis showed the Naïve Bayes algorithm offers fast and accurate fall and near-fall identification.

Keywords:

Fall detection fall classification machine learning near-fall data collection wearable device wireless communications.

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Area of Science:

Biomedical Engineering
Gerontology
Data Science

Background:

Falls and near-falls pose significant health risks, necessitating reliable detection methods.
Existing fall detection systems often lack comprehensive data collection capabilities.
The Falls And Near Falls Assessment Research and Evaluation (FANFARE) project addresses this gap.

Purpose of the Study:

To develop a wearable system for collecting data crucial for fall and near-fall analysis.
To evaluate the effectiveness of different machine learning algorithms for fall detection using collected data.

Main Methods:

Development of a wearable system comprising a computer and wireless sensors.
Measurement, display, and storage of fall-related parameters including postural activities and heart rate variability.
Application of various machine learning algorithms to analyze stored data for fall and near-fall classification.

Main Results:

Successful development and testing of the FANFARE system.
Identification of the Naïve Bayes algorithm as the most effective for fall detection.
Naïve Bayes demonstrated fast model building and high accuracy in distinguishing falls from near-falls.

Conclusions:

The FANFARE system provides a viable solution for wearable fall detection.
The Naïve Bayes algorithm is a highly suitable machine learning model for real-time fall and near-fall analysis.
This research contributes to improved fall prevention and safety technologies.