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Updated: Jun 27, 2026

Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease
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Published on: July 24, 2019

Upper Limb Tremors Classification for Parkinson's Disease Using W-Band (76-81 GHz) Doppler Millimeter-Wave Sensing

Pi-Yun Chen1, Chun-Yu Lin1, Neng-Sheng Pai1

  • 1Department of Electrical Engineering, National Chin-Yi University of Technology, Taichung City 41170, Taiwan.

Sensors (Basel, Switzerland)
|June 26, 2026
PubMed
Summary

This study introduces a contactless millimeter-wave radar system for detecting Parkinson's disease tremors. The system uses advanced signal processing and AI to accurately classify involuntary upper limb tremors (ULTs).

Keywords:
Parkinson’s diseasemicro-Doppler effectshort-range and contactlesstime–frequency transformtwo-dimensional convolutional neural networkupper limb tremor

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Area of Science:

  • Biomedical Engineering
  • Signal Processing
  • Artificial Intelligence in Healthcare

Background:

  • Parkinson's disease (PD) is a progressive neurodegenerative disorder impacting motor function, with involuntary upper limb tremors (ULTs) being a key symptom.
  • Current methods for tremor assessment can be invasive or lack real-time monitoring capabilities.
  • Understanding the micro-Doppler effect (mDE) associated with tremor motion is crucial for developing objective diagnostic tools.

Purpose of the Study:

  • To develop and validate a short-range (<1.0 m), contactless sensing method for detecting and classifying ULTs in Parkinson's disease patients.
  • To leverage Doppler millimeter-wave (mm-Wave) radar technology combined with advanced signal processing and machine learning for tremor analysis.
  • To achieve high accuracy in real-time ULT classification using automated feature extraction and pattern recognition.

Main Methods:

  • Implementation of a W-band (76-81 GHz) Doppler mm-Wave biosensor for contactless sensing of tremor-induced mDE.
  • Application of time-frequency transform (TFT) methods, including Wigner-Ville distribution (WVD) and smoothed pseudo WVD (SPWVD), to extract mDE features from radar signals.
  • Development of a two-dimensional (2D) convolutional neural network (CNN) classifier trained on visual feature patterns (spectrograms) for ULT classification, utilizing 60% of data for training and 40% for testing via 10-fold cross-validation.

Main Results:

  • The proposed classifiers ('WVD + 2D CNN' and 'SPWVD + 2D CNN') achieved high performance in ULT classification.
  • Average precision reached 95.92 ± 0.60%, average recall 95.89 ± 0.62%, and average accuracy 95.89 ± 0.62%.
  • The study demonstrated the system's feasibility for real-time, contactless classification of ULTs in PD patients.

Conclusions:

  • The developed mm-Wave radar system offers a promising non-invasive approach for monitoring and classifying Parkinson's disease tremors.
  • The combination of TFT methods and 2D CNN provides an effective framework for analyzing complex tremor dynamics.
  • This technology has the potential to significantly improve the remote assessment and management of PD patients.