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

Measuring Acceleration Due to Gravity01:12

Measuring Acceleration Due to Gravity

1.3K
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...
1.3K
Relative Motion Analysis - Acceleration01:10

Relative Motion Analysis - Acceleration

950
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...
950
Kinematic Equations - II01:17

Kinematic Equations - II

15.3K
The second kinematic equation expresses the final position of an object in terms of its initial position, the distance traveled with the initial constant velocity, and the distance traveled due to a change in velocity. Similar to the first kinematic equation, this equation is also only valid when the acceleration is constant throughout the motion of an object.
Suppose a car merges into freeway traffic on a 200 m long ramp. If its initial velocity is 10 m/s and it accelerates at 2 m/s2, then the...
15.3K
Relative Motion Analysis - Velocity01:24

Relative Motion Analysis - Velocity

820
A stroke engine has a slider-crank mechanism that 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.
When an external force is exerted, it sets the crank into a rotational movement. This, in turn, instigates the motion of the connecting rod, leading to what is referred to as a general plane motion. This process involves two key points - point A on the connecting rod...
820
Kinematic Equations - I01:26

Kinematic Equations - I

16.8K
When an object moves with constant acceleration, the velocity of the object changes at a constant rate throughout the motion. The kinematic equations of motions are derived for such cases where the acceleration of the object is constant. The first kinematic equation gives an insight into the relationship between velocity, acceleration, and time. We can see, for example:
16.8K

You might also read

Related Articles

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

Sort by
Same author

Special Issue on Acoustic Sensors and Their Applications (Vol. 1).

Sensors (Basel, Switzerland)·2023
Same author

Graph-to-signal transformation based classification of functional connectivity brain networks.

PloS one·2019
Same author

The effects of explosion sound on the brain based on electroencephalogram signals.

International journal of environmental health research·2019
Same author

A Novel EEG Based Spectral Analysis of Persistent Brain Function Alteration in Athletes with Concussion History.

Scientific reports·2017
Same author

Respiratory rate measurements via Doppler radar for health monitoring applications.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2017
Same author

Evidence of brain functional deficits following sport-related mild traumatic brain injury.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2017
Same journal

Analysis of End-Tidal CO2 Variability During Plateau Waves Episodes: An Information Theoretic Approach<sup></sup>.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025
Same journal

AI and Tomosynthesis for Breast Cancer Molecular Subtyping: A step toward precision medicine<sup></sup>.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025
Same journal

Towards Sustainable Protein Recovery from Biological Waste: Assessing Polyethersulfone-based Microfiltration.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025
Same journal

Analysis of the cardiovascular response to standardized polymicrobial peritonitis experimental model.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025
Same journal

Automated Wrist Ultrasound Image Bone Enhancement and Segmentation Using Deep Learning.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025
Same journal

A Deep Learning approach for Depressive Symptoms assessment in Parkinson's disease patients using facial videos.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025
See all related articles

Related Experiment Video

Updated: Feb 20, 2026

An Inertial Measurement Unit Based Method to Estimate Hip and Knee Joint Kinematics in Team Sport Athletes on the Field
06:52

An Inertial Measurement Unit Based Method to Estimate Hip and Knee Joint Kinematics in Team Sport Athletes on the Field

Published on: May 26, 2020

8.7K

Gait speed estimation using Kalman Filtering on inertial measurement unit data.

Md Nafiul Alam, Tamanna Tabassum Khan Munia, Reza Fazel-Rezai

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |October 25, 2017
    PubMed
    Summary
    This summary is machine-generated.

    This study developed an algorithm using inertial motion sensors to accurately measure gait speed for motor disorder diagnosis and rehabilitation. The algorithm, enhanced with a Kalman Filter, achieved a low average error rate of 0.23 m/h.

    More Related Videos

    Home-Based Monitor for Gait and Activity Analysis
    07:24

    Home-Based Monitor for Gait and Activity Analysis

    Published on: August 8, 2019

    7.3K
    Simulation of Human-induced Vibrations Based on the Characterized In-field Pedestrian Behavior
    10:52

    Simulation of Human-induced Vibrations Based on the Characterized In-field Pedestrian Behavior

    Published on: April 13, 2016

    9.2K

    Related Experiment Videos

    Last Updated: Feb 20, 2026

    An Inertial Measurement Unit Based Method to Estimate Hip and Knee Joint Kinematics in Team Sport Athletes on the Field
    06:52

    An Inertial Measurement Unit Based Method to Estimate Hip and Knee Joint Kinematics in Team Sport Athletes on the Field

    Published on: May 26, 2020

    8.7K
    Home-Based Monitor for Gait and Activity Analysis
    07:24

    Home-Based Monitor for Gait and Activity Analysis

    Published on: August 8, 2019

    7.3K
    Simulation of Human-induced Vibrations Based on the Characterized In-field Pedestrian Behavior
    10:52

    Simulation of Human-induced Vibrations Based on the Characterized In-field Pedestrian Behavior

    Published on: April 13, 2016

    9.2K

    Area of Science:

    • Biomechanics
    • Rehabilitation Engineering
    • Wearable Technology

    Background:

    • Gait speed is a critical indicator for diagnosing motor disorders and assessing rehabilitation progress.
    • Accurate gait speed measurement traditionally requires specialized equipment, limiting its accessibility.
    • Inertial Motion Sensors (IMDs) offer a portable and potentially more accessible solution for gait analysis.

    Purpose of the Study:

    • To develop and validate an algorithm for measuring gait speed using inertial motion sensors (tri-axial accelerometer and gyroscope).
    • To assess the accuracy and performance of the proposed algorithm across various walking speeds.
    • To evaluate the effectiveness of a Kalman Filter in refining gait speed estimations.

    Main Methods:

    • An algorithm was designed to process data from a tri-axial accelerometer and gyroscope.
    • Gait speed was measured using a treadmill at four controlled speeds: 0.5, 1, 2, and 3 miles/hour.
    • A Kalman Filter was implemented to tune and improve the accuracy of the calculated gait speed.
    • Algorithm performance was evaluated by comparing estimated speeds against actual treadmill speeds using mean square error.

    Main Results:

    • The proposed algorithm demonstrated reasonable accuracy in estimating gait speed across different treadmill settings.
    • The average error rate between the estimated and actual speed was found to be 0.23 m/h.
    • Optimal algorithm performance was observed at a treadmill speed of 1 mile/hour.
    • The Kalman Filter effectively reduced uncertainty, providing a more precise gait speed approximation.

    Conclusions:

    • The developed algorithm offers a viable method for gait speed measurement using inertial motion sensors.
    • The algorithm shows potential for aiding in the diagnosis of motor disorders and monitoring rehabilitation.
    • Kalman Filter integration significantly enhances the reliability and accuracy of gait speed estimation from IMD data.