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Related Concept Videos

Kinematic Equations: Problem Solving01:15

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When analyzing one-dimensional motion with constant acceleration, the problem-solving strategy involves identifying the known quantities and choosing the appropriate kinematic equations to solve for the unknowns. Either one or two kinematic equations are needed to solve for the unknowns, depending on the known and unknown quantities. Generally, the number of equations required is the same as the number of unknown quantities in the given example. Two-body pursuit problems always require two...
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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.
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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:
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Kinematic Equations - III01:18

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The first two kinematic equations have time as a variable, but the third kinematic equation is independent of time. This equation expresses final velocity as a function of the acceleration and distance over which it acts. The fourth kinematic equation does not have an acceleration term and provides the final position of the object at time t in terms of the initial and final velocities. This equation is useful when the value of the constant acceleration is unknown.
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In mechanical engineering, one-degree-of-freedom systems form the basis of a wide range of electrical and mechanical components. Using these models, engineers can predict the behavior of various parts in a larger system, which gives them insight into how different forces interact with each other.
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Relative Motion Analysis using Rotating Axes-Problem Solving01:29

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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.
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Updated: Jun 27, 2025

Trajectory Data Analyses for Pedestrian Space-time Activity Study
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Implementing Gait Kinematic Trajectory Forecasting Models on an Embedded System.

Madina Shayne1, Leonardo A Molina2, Bin Hu3

  • 1Department of Mechanical and Manufacturing Engineering, University of Calgary, 2500 University Drive NW, Calgary, AB T2N 1N4, Canada.

Sensors (Basel, Switzerland)
|April 27, 2024
PubMed
Summary
This summary is machine-generated.

Forecasting through Recurrent Topology (FReT) offers a computationally efficient method for predicting gait kinematics in wearable assistive devices. This smart algorithm improves accuracy and balances performance, paving the way for advanced sensor technologies.

Keywords:
embedded systemforecastgaitsensorwearable

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Area of Science:

  • Biomechanics and Robotics
  • Machine Learning for Wearable Technology

Background:

  • Wearable assistive devices like prostheses and exoskeletons require smart algorithms for gait prediction.
  • Computational load limitations on embedded systems hinder complex gait models.
  • Existing models struggle with adaptability and resource demands.

Purpose of the Study:

  • To deploy and evaluate the Forecasting through Recurrent Topology (FReT) algorithm on an embedded system for gait kinematic prediction.
  • To assess FReT's accuracy, computational time, and precision against neural network models.
  • To determine FReT's suitability for real-time applications in assistive devices.

Main Methods:

  • FRET algorithm deployed on an embedded system using lower-limb motion sensor data from fifteen subjects.
  • Comparison with pretrained and iteratively updated NNET and deep-NNET (D-NNET) models.
  • Evaluation metrics included accuracy (normalized root-mean-square error), computational time, and precision.

Main Results:

  • FRET significantly outperformed NNET and D-NNET models, reducing normalized root-mean-square error by nearly 50%.
  • FRET demonstrated a superior balance between accuracy, computational efficiency, and precision.
  • The algorithm proved adaptable to evolving data structures with iterative updates.

Conclusions:

  • The FRET framework on embedded systems offers improved performance for gait kinematic forecasting.
  • FRET represents a significant advancement for sensor-aided technologies in assistive ambulatory devices.
  • Lightweight and adaptive algorithms are crucial for next-generation wearable assistive technology.