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Related Experiment Video

Updated: Jan 11, 2026

Author Spotlight: Automated Deep Brain Stimulation for Parkinson's Disease - Exploring the Possibilities and Challenges of Home Monitoring

Author Spotlight: Automated Deep Brain Stimulation for Parkinson's Disease - Exploring the Possibilities and Challenges of Home Monitoring

Published on: July 14, 2023

An Adaptable Phase-Tracking System for Parkinsonian Rest Tremor: Design and In-Clinic Feasibility.

Beatriz S Arruda¹, Moaad Benjaber^2,3,4, John Fleming^2,3,4

¹Department of Clinical and Movement NeurosciencesQueen Square Institute of Neurology, University College London London WC1N 3BG U.K.

IEEE Journal of Translational Engineering in Health and Medicine

|November 12, 2025

Summary

A new wearable system accurately tracks Parkinson's disease (PD) tremor phase in real-time. This adaptive technology offers a foundation for personalized, non-invasive therapies to manage tremor variability.

Keywords:

Adaptive algorithm Parkinson’s disease electrical stimulation phase estimation precision medicine

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Last Updated: Jan 11, 2026

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Published on: September 26, 2019

Area of Science:

Biomedical Engineering
Neuroscience
Clinical Research

Background:

Tremor is a common movement disorder, often associated with Parkinson's disease (PD).
Current treatments for PD tremor, including medication and deep brain stimulation, have limitations.
There is a significant need for non-invasive therapeutic alternatives for PD tremor management.

Purpose of the Study:

To develop and validate a wrist-worn system for real-time estimation of Parkinsonian rest tremor phase.
To create an adaptable algorithm capable of handling tremor variability in individuals with PD.
To lay the groundwork for personalized, non-invasive tremor management strategies.

Main Methods:

A wrist-worn system with an adaptable phase-tracking algorithm was developed.
The algorithm dynamically adjusts to variations in tremor axis and center frequency.
The system underwent offline validation and in-clinic feasibility testing in three PD participants, delivering electrical stimulation.

Main Results:

The system demonstrated robust phase estimation both offline and in all participants.
The algorithm successfully adapted to dynamic changes in tremor characteristics.
Personalized stimulation settings resulted in modest tremor modulation.

Conclusions:

This study presents a novel platform for tremor phase tracking research in PD.
The developed wearable system accounts for PD tremor variability.
This work establishes a foundation for personalized, non-invasive tremor management strategies.