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 - Velocity

Relative Motion Analysis - Velocity

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...

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 instrumental in...

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...

Discrete Fourier Transform

Discrete Fourier Transform

The Discrete Fourier Transform (DFT) is a fundamental tool in signal processing, extending the discrete-time Fourier transform by evaluating discrete signals at uniformly spaced frequency intervals. This transformation converts a finite sequence of time-domain samples into frequency components, each representing complex sinusoids ordered by frequency. The DFT translates these sequences into the frequency domain, effectively indicating the magnitude and phase of each frequency component present...

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...

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...

You might also read

Related Articles

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

Sort by

Same author

Patient self-assessment and virtual visit-based treatment decisions in rheumatoid arthritis: results from the multicentre Telemedicine in Rheumatoid Arthritis trial.

EULAR rheumatology open·2026

Same author

An Information-Driven Approach for the Early Health Technology Sustainability Assessment and the Frugal Design of the Internet of Medical Things: An Exploratory Study of Wearable Activity Monitoring Devices.

JMIR mHealth and uHealth·2026

Same author

Impact of personalized coaching on the use of digital health interventions for movement therapy in rheumatology: a randomized controlled trial.

Scientific reports·2026

Same author

Whole body analysis of functional communities and topological features of gait with different speeds in Parkinson's disease.

Journal of neurology·2026

Same author

Association Between Freezing of Gait and Sleep Quality in People with Parkinson's Disease.

Brain sciences·2026

Same author

The Digital Exposome: A Life Course Framework for Health in the Digital Age.

Journal of medical Internet research·2026

Search research articles

Related Experiment Video

Updated: Jul 10, 2026

Design and Analysis for Fall Detection System Simplification

Design and Analysis for Fall Detection System Simplification

Published on: April 6, 2020

Walk detection with a kinematic sensor: frequency and wavelet comparison.

Pierre Barralon¹, Nicolas Vuillerme, Norbert Noury

¹Lab. TIMC-IMAG, CNRS, La Tronche, France. Pierre.barralon@imag.fr

Conference Proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference

|October 20, 2007

Summary

This study introduces a chest-mounted kinematic sensor for detecting walking activity in elderly individuals living independently. Wavelet decomposition, specifically Discrete Wavelet Transform (DWT), proved most effective for monitoring mobility and health.

More Related Videos

Microfluidic Platform with Multiplexed Electronic Detection for Spatial Tracking of Particles

Microfluidic Platform with Multiplexed Electronic Detection for Spatial Tracking of Particles

Published on: March 13, 2017

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: Jul 10, 2026

Design and Analysis for Fall Detection System Simplification

Design and Analysis for Fall Detection System Simplification

Published on: April 6, 2020

Microfluidic Platform with Multiplexed Electronic Detection for Spatial Tracking of Particles

Microfluidic Platform with Multiplexed Electronic Detection for Spatial Tracking of Particles

Published on: March 13, 2017

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
Gerontology
Signal Processing

Background:

Health Smart Homes aim to monitor elderly individuals living independently.
Monitoring physiological and non-physiological parameters is crucial for assessing health.
Walk detection is a key indicator of mobility and overall health in the elderly.

Purpose of the Study:

To develop and evaluate algorithms for accurate walk detection in elderly people using kinematic data.
To compare the performance of Fourier analysis and wavelet decomposition methods for walk phase detection.

Main Methods:

Utilized a kinematic sensor placed on the chest to record subject movements.
Applied six algorithms for walk phase detection: two based on Fourier analysis and four using wavelet decomposition (Discrete Wavelet Transform - DWT, and Continuous Wavelet Transform - CWT).
Evaluated algorithm performance on real-world data from 20 elderly participants.

Main Results:

The Discrete Wavelet Transform (DWT) method demonstrated the highest efficiency in walk detection.
Achieved 78.5% sensitivity and 67.6% specificity using the DWT approach.
Compared performance across different algorithms, highlighting DWT's superiority.

Conclusions:

The DWT-based algorithm is the most efficient method for detecting walk activity in elderly individuals using chest-mounted kinematic sensors.
This technology can aid in monitoring the mobility and health of older adults living independently.
Smart home health monitoring systems can benefit from advanced signal processing techniques for accurate activity recognition.