Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Application of Linearization and Approximation01:29

Application of Linearization and Approximation

185
A drone flying through complex terrain often relies on more than one sensing method to estimate small changes in altitude. Along with direct measurements, air pressure provides a useful indirect indicator of vertical movement. Atmospheric pressure decreases as altitude increases, and this relationship is commonly described using an exponential model. Although accurate, converting pressure measurements into altitude values requires calculations that are too complex to perform repeatedly during...
185
Measuring Acceleration Due to Gravity01:12

Measuring Acceleration Due to Gravity

1.5K
Consider a coffee mug hanging on a hook in a pantry. If the mug gets knocked, it oscillates back and forth like a pendulum until the oscillations die out.
A simple pendulum can be described as a point mass and a string. Meanwhile, a physical pendulum is any object whose oscillations are similar to a simple pendulum, but cannot be modeled as a point mass on a string because its mass is distributed over a larger area. The behavior of a physical pendulum can be modeled using the principles of...
1.5K
Relative Motion Analysis - Acceleration01:10

Relative Motion Analysis - Acceleration

1.1K
A slider-crank mechanism converts rotational motion from the crank into linear motion of the slider or vice versa. This mechanism consists of three main parts: the crank, the connecting rod, and the slider. The movement of the slider-crank is an example of general plane motion as the fluctuating angle between the crank and the connecting rod. Consider a segment AB where point A is at the end of the slider and point B is on the diametrically opposite end to point A, on a crack. The variance in...
1.1K
Relative Motion Analysis using Rotating Axes - Acceleration01:22

Relative Motion Analysis using Rotating Axes - Acceleration

1.0K
Consider a component AB undergoing a linear motion. Along with a linear motion, point B also rotates around point A. To comprehend this complex movement, position vectors for both points A and B are established using a stationary reference frame. The absolute velocity of point B is determined by adding the absolute velocity of point A, the relative velocity of point B in the rotating frame, and the effects caused by the angular velocity within the rotating frame.
Time differentiation is...
1.0K
Variation in Acceleration due to Gravity near the Earth's Surface01:20

Variation in Acceleration due to Gravity near the Earth's Surface

3.0K
An object's apparent weight is its weight measured by a spring balance at its location. It is different from its true weight, the force with which the Earth pulls it, because of the Earth's rotation. Mathematically, an object's apparent weight equals its true weight minus the centripetal force that keeps it in a circular motion along with the Earth's surface every 24 hours.
The difference between the true and apparent weights is proportional to the square of the Earth's...
3.0K
Differential Equations: Problem Solving01:21

Differential Equations: Problem Solving

207
When analyzing the motion of falling objects, it is essential to consider not only the force of gravity but also the opposing force of air resistance. A practical example involves releasing a heavy test weight during a safety check on a ship. As the weight falls from rest, gravity accelerates it downward while air resistance exerts an upward force that increases with velocity. This dynamic interplay of forces is well described by differential equations, which provide a mathematical framework...
207

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Mapping alkaloids with mass spectrometry imaging: Application of spatial distribution in medicinal plants and animal tissues.

Talanta·2026
Same author

Knowledge, acceptance, and willingness to pay for expanded carrier screening among obstetric patients in China: Implications for genetic counseling practice.

Journal of genetic counseling·2026
Same author

Strain-coordination strategy enabling long-cycling all-solid-state lithium-sulfur batteries.

Nature communications·2026
Same author

Daily activity patterns from wearable accelerometry predict physical frailty and concern about falling.

NPJ digital medicine·2026
Same author

The DNA-binding protein PfAP2-V regulates erythrocyte invasion and pathogenesis of the human malaria parasite Plasmodium falciparum.

Science China. Life sciences·2026
Same author

Biomimetic Ion Channel Design for Simultaneous Lithium-Ion Flux Regulation and Interfacial Stabilization in Lithium Metal Batteries.

Small (Weinheim an der Bergstrasse, Germany)·2026

Related Experiment Video

Updated: Apr 18, 2026

Design and Analysis for Fall Detection System Simplification
08:05

Design and Analysis for Fall Detection System Simplification

Published on: April 6, 2020

11.3K

A low-power fall detection algorithm based on triaxial acceleration and barometric pressure.

Changhong Wang, Michael R Narayanan, Stephen R Lord

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |January 9, 2015
    PubMed
    Summary
    This summary is machine-generated.

    This study presents a low-power fall detection algorithm using accelerometry and barometric pressure. The system achieves over 96% accuracy in detecting falls while reducing power consumption by 10.9%.

    More Related Videos

    Author Spotlight: Innovations in iTUG Test for Enhanced Risk Assessment and Cognitive Insights
    05:26

    Author Spotlight: Innovations in iTUG Test for Enhanced Risk Assessment and Cognitive Insights

    Published on: October 25, 2024

    2.0K
    An Instrumented Pull Test to Characterize Postural Responses
    12:18

    An Instrumented Pull Test to Characterize Postural Responses

    Published on: April 6, 2019

    11.7K

    Related Experiment Videos

    Last Updated: Apr 18, 2026

    Design and Analysis for Fall Detection System Simplification
    08:05

    Design and Analysis for Fall Detection System Simplification

    Published on: April 6, 2020

    11.3K
    Author Spotlight: Innovations in iTUG Test for Enhanced Risk Assessment and Cognitive Insights
    05:26

    Author Spotlight: Innovations in iTUG Test for Enhanced Risk Assessment and Cognitive Insights

    Published on: October 25, 2024

    2.0K
    An Instrumented Pull Test to Characterize Postural Responses
    12:18

    An Instrumented Pull Test to Characterize Postural Responses

    Published on: April 6, 2019

    11.7K

    Area of Science:

    • Wearable Technology
    • Biomedical Engineering
    • Signal Processing

    Background:

    • Fall detection is crucial for elderly care and patient monitoring.
    • Existing systems often suffer from high power consumption, limiting wearable applications.
    • Integrating multiple sensor types can improve accuracy and robustness.

    Purpose of the Study:

    • To develop a low-power fall detection algorithm.
    • To enhance energy efficiency in wearable health monitoring devices.
    • To achieve high sensitivity and specificity in fall detection.

    Main Methods:

    • Utilized triaxial accelerometry and barometric pressure signals.
    • Implemented a dynamic sampling rate adjustment for the accelerometer.
    • Optimized data transmission between sensors and a central controller.

    Main Results:

    • Achieved sensitivity and specificity both exceeding 96% on a validation dataset.
    • Demonstrated a 10.9% reduction in power consumption compared to baseline methods.
    • Validated performance on a dataset including simulated falls and daily living activities.

    Conclusions:

    • The proposed algorithm effectively detects falls with high accuracy.
    • Dynamic sampling and optimized data management significantly reduce power usage.
    • This approach offers a promising solution for energy-efficient wearable fall detection systems.