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Related Experiment Video

Updated: Sep 1, 2025

Video Movement Analysis Using Smartphones ViMAS: A Pilot Study

Video Movement Analysis Using Smartphones ViMAS: A Pilot Study

Published on: March 14, 2017

Tremor evaluation using smartphone accelerometry in standardized settings.

Gürdal Sahin^1,2,3, Pär Halje¹, Sena Uzun^3,4

¹Integrative Neurophysiology and Neurotechnology, Department of Experimental Medical Sciences, Lund University, Lund, Sweden.

Frontiers in Neuroscience

|August 18, 2022

Summary

This summary is machine-generated.

Patients with essential tremor and Parkinson's disease can now use smartphone accelerometry for at-home self-testing. This data enables personalized deep-brain stimulation (DBS) adjustments for improved tremor symptom relief.

Keywords:

Parkinson’s disease closed-loop essential tremor inertia sensors neuromodulation

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Last Updated: Sep 1, 2025

Video Movement Analysis Using Smartphones ViMAS: A Pilot Study

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Published on: March 14, 2017

Evaluation of a Smartphone-based Human Activity Recognition System in a Daily Living Environment

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Published on: December 11, 2015

Home-Based Monitor for Gait and Activity Analysis

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Published on: August 8, 2019

Area of Science:

Neuroscience
Biomedical Engineering
Medical Technology

Background:

Tremor significantly impacts daily life and fluctuates with various factors.
Deep-brain stimulation (DBS) is a treatment for tremor, but requires optimized parameter adjustments.
Current methods for assessing tremor severity and adjusting DBS are often subjective or require clinical visits.

Purpose of the Study:

To describe a procedure for patients to perform self-tests at home using smartphone inertia sensors.
To generate sensor data for on-line adjustments of DBS parameters.
To assess the feasibility of using smartphone accelerometry for closed-loop DBS control in tremor patients.

Main Methods:

Developed a home-based self-testing procedure utilizing smartphone inertia sensors.
Collected tremor pattern data from Parkinson's disease and essential tremor patients.
Compared smartphone accelerometry data with clinical ratings (Fahn-Tolosa-Marin) and finger-attached sensors.

Main Results:

Characterized detailed tremor pattern features using smartphone accelerometry.
Demonstrated high reliability and detail in smartphone-derived tremor descriptors.
Showed that smartphone data can be used for closed-loop control of DBS.

Conclusions:

Smartphone accelerometry offers a widely accessible and reliable method for tremor assessment.
This technology facilitates personalized, on-line adjustments of DBS parameters for enhanced symptomatic relief.
The proposed procedure has the potential to improve the management of movement disorders like Parkinson's disease and essential tremor.