Bringing the Clinic Home: An At-Home Multi-Modal Data Collection Ecosystem to Support Adaptive Deep Brain Stimulation

作者： Gabrielle Strandquist1, Tomasz Frączek2, Tanner Dixon3, Shravanan Ravi3, Raphael Bechtold4, Daryl Lawrence5, Alicia Zeng6, Jack Gallant7, Simon Little3, Jeffrey Herron8 1Computer Science and Engineering,University of Washington, 2Neuroscience,University of Washington, 3Neurology,University of California, San Francisco, 4Bioengineering,University of Washington, 5Bioengineering,University of California, Berkeley, 6Biophysics,University of California, Berkeley, 7Psychology,University of California, Berkeley, 8Neurological Surgery,University of Washington

简介：

Overview

This study presents a prototype at-home multi-modal data collection platform designed to optimize adaptive deep brain stimulation (aDBS) for individuals with neurological movement disorders, specifically Parkinson's disease. Key findings highlight the platform's deployment over a year, successfully monitoring therapy parameters and capturing crucial data while ensuring patient privacy.

Key Study Components

Area of Science

• Neuroscience
• Movement Disorders
• Technology in Rehabilitation

Background

• Adaptive deep brain stimulation (aDBS) is a promising therapy for Parkinson's disease.
• Long-term monitoring and adjustment of therapy parameters outside clinical settings is essential.
• The impact of various data types on assessing Parkinson's disease symptoms is investigated.

Purpose of Study

• To demonstrate the feasibility of at-home aDBS monitoring.
• To evaluate the effectiveness of remote data collection for therapy adjustments.
• To explore changes in symptoms and movement quality over extended periods.

Methods Used

• The study utilizes a multi-modal data collection platform designed for home use.
• Parkinson's disease serves as the biological model for evaluating aDBS efficacy.
• The platform captures video data to analyze kinematic movements, such as finger motions.
• Data collection occurs as patients move freely, enabling naturalistic observation.
• Key steps include continuous data monitoring and remote algorithm updates.

Main Results

• The platform successfully monitored therapy parameters over a year, allowing exploration of symptom progression.
• Data enabled in-depth analysis of movement quality in daily life scenarios.
• Insights gained inform necessary measurements for managing diverse Parkinson's symptoms outside clinical settings.

Conclusions

• The study demonstrates the potential for aDBS therapy to be effectively monitored at home.
• This approach enhances patient privacy and comfort while ensuring effective symptom management.
• It paves the way for further research on remote rehabilitation techniques and their implications in understanding Parkinson's disease mechanisms.

Frequently Asked Questions