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Related Experiment Videos

Wrist sensor-based tremor severity quantification in Parkinson's disease using convolutional neural network.

Han Byul Kim1, Woong Woo Lee2, Aryun Kim3

  • 1Graduate Program of Bioengineering, Seoul National University, Seoul, South Korea.

Computers in Biology and Medicine
|March 4, 2018
PubMed
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A new system using a convolutional neural network (CNN) accurately assesses Parkinson's disease (PD) tremor severity from wearable device data. This technology enables more precise monitoring of PD symptoms in patients' daily lives.

Area of Science:

  • Neurology
  • Biomedical Engineering
  • Artificial Intelligence

Background:

  • Tremor is a primary symptom of Parkinson's disease (PD), necessitating accurate severity assessment for effective treatment.
  • Current methods for tremor assessment may lack the precision required for continuous monitoring in daily life.

Purpose of the Study:

  • To develop and evaluate a novel tremor assessment system utilizing a convolutional neural network (CNN) for differentiating Parkinson's disease symptom severity.
  • To enhance the precision of monitoring Parkinson's disease tremor symptoms in real-world settings.

Main Methods:

  • Tremor signals were collected from 92 Parkinson's disease patients using a custom wearable device (SNUMAP) with accelerometer and gyroscope sensors.
  • Data were transformed into the frequency domain and converted into 2D images for training a CNN model.
Keywords:
Convolutional neural networkMachine learningParkinson's diseaseTremorWearable sensor

Related Experiment Videos

  • The CNN architecture was trained by convolving tremor signal images with kernels and compared against existing machine learning algorithms.

    • Main Results:

    • The proposed CNN-based system achieved high accuracy in differentiating tremor severity.
    • Performance metrics included an accuracy of 0.85 and a linear weighted kappa of 0.85.
    • The CNN model demonstrated superior performance compared to previously studied machine learning algorithms.

    Conclusions:

    • The developed CNN system offers a precise and potentially more accessible method for monitoring Parkinson's disease tremor severity.
    • This approach could facilitate more accurate treatment adjustments and improved patient management in daily life.
    • Wearable sensor data combined with advanced AI presents a promising avenue for objective neurological symptom assessment.