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Continuous Accelerometry-Based Tremor Detection During Daily Living.

Luis Martinez1, Orlando Martinez1, Stephen L Schmidt2

  • 1Department of Electrical and Computer Engineering, California State University, Los Angeles, Los Angeles, CA 90032, USA.

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|March 14, 2026
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Summary
This summary is machine-generated.

We developed an automated algorithm using a wrist-worn accelerometer to continuously detect Parkinsonian tremor. This algorithm distinguishes tremor from daily activities, aiding adaptive Deep Brain Stimulation (DBS) control.

Keywords:
Parkinsonian tremoradaptive DBSimplanted accelerometertremor detectionwearable accelerometer

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Area of Science:

  • Biomedical Engineering
  • Neuroscience
  • Wearable Technology

Background:

  • Parkinson's disease (PD) management requires advanced adaptive Deep Brain Stimulation (DBS) control.
  • Continuous, real-time tremor detection is crucial for optimizing DBS therapy.
  • Existing methods for tremor assessment can be limited in daily living scenarios.

Purpose of the Study:

  • To develop and validate an automated algorithm for continuous Parkinsonian tremor detection.
  • To assess the feasibility of using wearable accelerometers for tremor monitoring.
  • To differentiate Parkinsonian tremor from voluntary movements during activities of daily living.

Main Methods:

  • Developed an automated algorithm utilizing triaxial acceleration data from a commercial wrist-worn accelerometer (Apple Watch) and an implanted DBS device.
  • Algorithm identifies voluntary activity, then detects peaks in the 4-7 Hz Parkinsonian tremor frequency band.
  • Employed continuous wavelet transforms for enhanced peak and harmonic detection compared to short-time Fourier transform.

Main Results:

  • The developed algorithm demonstrated accurate Parkinsonian tremor detection during daily activities.
  • Tremor detection accuracy was higher using the Apple Watch compared to the implanted DBS device (IPG).
  • The algorithm showed strong correlations with DBS intensity (r = -0.93 to -0.97) and the industry standard metric (r = 0.57).

Conclusions:

  • The automated algorithm provides a clinically relevant method for continuous Parkinsonian tremor detection using a wearable accelerometer.
  • This technology facilitates adaptive DBS control by distinguishing tremor from voluntary movements during everyday life.
  • The findings support the use of convenient, commercial wrist-worn devices for PD tremor monitoring.