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Pulse rhythm01:30

Pulse rhythm

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Pulse rhythm refers to the pattern of pulsations within specific intervals, offering valuable insights into the regularity or irregularity of the heart's beats as observed through the pattern of pulsation within specific intervals. A regular pulse exhibits a consistent heart rate with uniform waveforms and pulsation force, variations of which can be classified as normal, weak, or bounding.
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Assessing a patient's pulse is a fundamental skill in healthcare, but certain situations require special attention:
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Equipments Used To Measure Blood Pressure01:30

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This invasive approach involves cannulating a peripheral artery. During each cardiac contraction, pressure generates mechanical motion within the catheter, transmitted through rigid, fluid-filled tubing to a transducer. This transducer converts mechanical motion into electrical signals displayed as waveforms on a monitor. An automatic flushing system prevents blood backflow. Due to the potential risk of unexpected arterial blood loss, this method is primarily used in intensive...
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Errors occurring during blood pressure monitoring01:25

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Blood pressure monitoring is a crucial clinical procedure in diagnosing and managing various cardiovascular conditions. Despite its significance, the accuracy of blood pressure measurements can be compromised by multiple factors, potentially leading to either falsely high or low readings. These inaccuracies are critical as they can significantly impact patient care. So, it is vital to understand these challenges deeply and adopt strategic approaches to minimize errors.
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Assessing Blood pressure using a doppler ultrasound01:19

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To obtain accurate blood pressure measurements in clinical settings, especially when traditional methods are insufficient, healthcare professionals utilize the Doppler ultrasound technique. This method uses high-frequency sound waves to detect blood flow within the arteries, which is crucial for patients with conditions that complicate circulatory system assessment.
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Measurement of Blood Pressure01:17

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Assessing blood pressure is a standard procedure executed in virtually all medical environments. The method utilized today was established over a hundred years ago by an innovative Russian doctor, Dr. Nikolai Korotkoff. The soft ticking noise, known as Korotkoff sounds, heard while taking blood pressure readings results from turbulent blood flow within the vessels. The apparatus required for this procedure includes a sphygmomanometer, a blood pressure cuff attached to a gauge, and a...
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Related Experiment Video

Updated: May 3, 2026

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

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Automatically Classifying Vestibular Gait Using Time-series Data from Wearable IMUs.

Safa Jabri, Lucy Spicher, Wendy Carender

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |March 5, 2025
    PubMed
    Summary

    Machine learning models using wearable sensors can automatically identify vestibular deficits by analyzing gait. These models learn directly from sensor data, simplifying the process and aiding in fall prevention strategies.

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    Area of Science:

    • Biomechanics
    • Neurology
    • Machine Learning

    Background:

    • Vestibular disorders impair balance, increasing fall risk.
    • Early identification of vestibular deficits is crucial for interventions.
    • Wearable sensors and machine learning offer automated gait assessment potential.

    Purpose of the Study:

    • Develop and validate machine learning models for classifying vestibular gait deficits.
    • Utilize minimally pre-processed inertial measurement unit (IMU) data.
    • Compare model performance against feature-based approaches.

    Main Methods:

    • Trained Bi-directional LSTM (BiLSTM) models on IMU time-series data from 30 participants (15 with vestibular deficits, 15 controls).
    • Models were trained on raw time-series data and fused data (time-series + engineered features).
    • Assessed classification performance using Area Under the Receiver Operating Characteristic Curve (AUROC).

    Main Results:

    • BiLSTM models achieved excellent classification performance (AUROC = 0.86) using minimally pre-processed data.
    • Performance was comparable to previous Random Forest models using engineered features (AUROC = 0.89).
    • BiLSTM models successfully learned discriminative gait patterns from raw IMU data.

    Conclusions:

    • Machine learning models, particularly BiLSTMs, can effectively classify vestibular gait deficits from minimally processed IMU data.
    • Automated analysis of gait kinematics using wearable sensors shows promise for clinical applications.
    • This approach reduces reliance on manual feature engineering for identifying individuals with vestibular impairments.