Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Relative Motion Analysis using Rotating Axes - Acceleration

Relative Motion Analysis using Rotating Axes - Acceleration

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...

Relative Motion Analysis - Acceleration

Relative Motion Analysis - Acceleration

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...

Relative Motion Analysis using Rotating Axes

Relative Motion Analysis using Rotating Axes

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.
However, to express the relative position of point B relative to point A, an additional frame of reference, denoted as x'y', is necessary. This additional frame not only translates but also rotates relative to the fixed frame, making it...

Measuring Acceleration Due to Gravity

Measuring Acceleration Due to Gravity

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...

Relative Motion Analysis using Rotating Axes-Problem Solving

Relative Motion Analysis using Rotating Axes-Problem Solving

Consider a crane whose telescopic boom rotates with an angular velocity of 0.04 rad/s and angular acceleration of 0.02 rad/s2. Along with the rotation, the boom also extends linearly with a uniform speed of 5 m/s. The extension of the boom is measured at point D, which is measured with respect to the fixed point C on the other end of the boom. For the given instant, the distance between points C and D is 60 meters.
Here, in order to determine the magnitude of velocity and acceleration for point...

Absolute Motion Analysis- General Plane Motion

Absolute Motion Analysis- General Plane Motion

Visualize a drone, with its propellers spinning rapidly, hovering mid-air. The fascinating movements and operations of this drone can be comprehended by applying the principle of general plane motion.
As the drone's propellers rotate, an upward force is generated that counteracts the force of gravity, enabling the drone to lift off from the ground. This initial movement of the drone is along a straight path, representing a form of translational motion. In this phase, every point on the...

You might also read

Related Articles

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

Sort by

Same author

High mobility group box 1 (HMGB1) levels in the placenta and in serum in preeclampsia.

American journal of reproductive immunology (New York, N.Y. : 1989)·2011

Same author

Destabilization of coxsackievirus b3 genome integrated with enhanced green fluorescent protein gene.

Intervirology·2011

Same author

[Clinicopathological features of primary splenic histiocytic sarcoma: a case report and literature review].

Zhonghua xue ye xue za zhi = Zhonghua xueyexue zazhi·2011

Same author

[Comparison of treatment with micro endoscopic discectomy and posterior lumbar interbody fusion using single and double B-Twin expandable spinal spacer].

Zhonghua wai ke za zhi [Chinese journal of surgery]·2011

Same author

Virtual transplantation in designing a facial prosthesis for extensive maxillofacial defects that cross the facial midline using computer-assisted technology.

The International journal of prosthodontics·2011

Same author

Total synthesis of phorboxazole A via de novo oxazole formation: convergent total synthesis.

Journal of the American Chemical Society·2010

Search research articles

Related Experiment Video

Updated: Feb 20, 2026

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

A wearable action recognition system based on acceleration and attitude angles using real-time detection algorithm.

Bo Wang, Xie Ni, Guoru Zhao

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

Summary

This summary is machine-generated.

This study developed a wearable system to detect falls using acceleration and attitude data. The system achieved 96.25% accuracy in recognizing actions, distinguishing falls from daily activities.

More Related Videos

Design and Analysis for Fall Detection System Simplification

Design and Analysis for Fall Detection System Simplification

Published on: April 6, 2020

Evaluation of a Smartphone-based Human Activity Recognition System in a Daily Living Environment

Evaluation of a Smartphone-based Human Activity Recognition System in a Daily Living Environment

Published on: December 11, 2015

Related Experiment Videos

Last Updated: Feb 20, 2026

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

Design and Analysis for Fall Detection System Simplification

Design and Analysis for Fall Detection System Simplification

Published on: April 6, 2020

Evaluation of a Smartphone-based Human Activity Recognition System in a Daily Living Environment

Evaluation of a Smartphone-based Human Activity Recognition System in a Daily Living Environment

Published on: December 11, 2015

Area of Science:

Biomedical Engineering
Wearable Technology
Human-Computer Interaction

Background:

Falls are a significant cause of injury and mortality, necessitating effective detection methods.
Distinguishing falls from daily activities before impact is crucial for timely intervention.

Purpose of the Study:

To develop a wearable action recognition system using unique resultant acceleration and attitude angles to differentiate falls from activities of daily life.
To implement a real-time detection algorithm for immediate fall identification.

Main Methods:

Acquisition of action data using a wearable system.
Data processing with a moving average filter and complementary filter for attitude angles.
Real-time action recognition algorithm to identify six distinct actions.

Main Results:

The real-time action recognition model achieved an overall accuracy of 96.25%.
Accuracy rates were 98% for males and 93.3% for females.
Optimal features and thresholds were successfully extracted for fall detection.

Conclusions:

The developed wearable system demonstrates high sensitivity in recognizing fall-related actions.
This technology has the potential to significantly improve fall detection and prevention strategies.