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Related Concept Videos

Relative Motion Analysis using Rotating Axes - Acceleration01:22

Relative Motion Analysis using Rotating Axes - Acceleration

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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...
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Relative Motion Analysis - Acceleration01:10

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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...
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Relative Motion Analysis using Rotating Axes-Problem Solving01:29

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378
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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Relative Motion Analysis using Rotating Axes01:25

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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...
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Comparative Analysis of Machine Learning Approaches for Fetal Movement Detection with Linear Acceleration and Angular

Lucy Spicher1, Carrie Bell2, Kathleen H Sienko1

  • 1Department of Mechanical Engineering, University of Michigan, Ann Arbor, MI 48109, USA.

Sensors (Basel, Switzerland)
|May 14, 2025
PubMed
Summary
This summary is machine-generated.

Continuous fetal movement monitoring using wearable sensors shows promise for improved antenatal care. Machine learning models effectively detected fetal movements from linear acceleration and angular rate data, enhancing fetal health assessment.

Keywords:
bi-directional long short-term memory (BiLSTM)convolutional neural network (CNN)fetal monitoringinertial measurement units (IMUs)random forest (RF)spectrogramtime–frequency analysiswearable sensors

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Area of Science:

  • Biomedical Engineering
  • Maternal-Fetal Medicine
  • Signal Processing

Background:

  • Reduced fetal movement (RFM) is a critical indicator of fetal risk, necessitating improved antenatal monitoring.
  • Current methods offer only intermittent snapshots of fetal health, requiring clinical settings and expert interpretation.
  • Continuous, objective monitoring systems are needed to enhance antenatal care and early detection of fetal distress.

Purpose of the Study:

  • To explore the utility of linear acceleration and angular rate data from wearable inertial measurement units (IMUs) for continuous fetal movement detection.
  • To develop and compare machine learning models for distinguishing fetal movements from maternal activity.
  • To assess the performance of different machine learning approaches in a real-world antenatal monitoring scenario.

Main Methods:

  • Twenty-three participants wore four abdominal IMUs and one chest reference sensor.
  • Participants manually indicated perceived fetal movements using a handheld button.
  • Machine learning models (Random Forest, BiLSTM, CNN) were trained using accelerometer and gyroscope data, including hand-engineered features, time series, and spectrograms.

Main Results:

  • Combining accelerometer and gyroscope data significantly improved fetal movement detection across all evaluated machine learning models.
  • Convolutional Neural Networks (CNNs) demonstrated superior performance but required larger datasets.
  • Random Forest (RF) and Bi-directional Long Short-Term Memory (BiLSTM) models provided robust performance with smaller datasets and enhanced interpretability, despite increased sensitivity to signal noise.

Conclusions:

  • Wearable IMUs combined with machine learning offer a viable approach for continuous, objective fetal movement monitoring.
  • The choice of machine learning model depends on dataset size and interpretability requirements.
  • This technology has the potential to revolutionize antenatal care by providing continuous insights into fetal well-being.