Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Huntington Disease l: Introduction

Huntington Disease l: Introduction

Huntington disease or HD is a progressive, fatal neurodegenerative disorder inherited in an autosomal dominant pattern.PathophysiologyIt is caused by expansion of the CAG trinucleotide repeat in the HTT gene on chromosome 4 (4p16.3), producing an abnormal huntingtin protein with an expanded polyglutamine tract. This misfolded protein disrupts cellular function, leading to neuronal death. Normal alleles have ≤26 repeats, 27–35 are intermediate (risk of expansion), 36–39 show reduced penetrance,...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Bridging the global Parkinson's divide: Technology as a structural solution for equitable and brain health-integrated care.

Journal of Parkinson's disease·2026

Same author

Prediction of α-synuclein seed amplification assay positivity in remotely followed <i>LRRK2</i> G2019S carriers using a validated data-driven model.

Journal of clinical and translational science·2026

Same author

Patient-Reported Symptom Burden in Individuals With Parkinson Disease.

Neurology. Clinical practice·2026

Same author

Primary prevention of Parkinson's disease: proceedings, the 8C's and a position statement from the Parkinson's disease prevention think tank.

NPJ Parkinson's disease·2026

Same author

PD GENEration: An International Parkinson's Disease Genetic Research Study.

medRxiv : the preprint server for health sciences·2026

Same author

Multimodal Remote Digital Phenotyping for Detecting and Tracking Early Parkinsonian Change in LRRK2 Carriers.

Research square·2026

Same journal

Somatosensory cortex shapes perceptual decision bias via the superior colliculus.

Research square·2026

Same journal

Combinatorial Targeting of Avapritinib-Driven MAP Kinase Activation in High-Grade Glioma.

Research square·2026

Same journal

Supporting Implementation of the National Standards for Cancer Survivorship Care: Development of the Cancer Survivorship Maturity Model (CSMM).

Research square·2026

Same journal

Operationalizing a walking exercise prescription based on 6-minute walk test results.

Research square·2026

Same journal

Age but not sex modifies lymphoid immune responses in murine sepsis.

Research square·2026

Same journal

Indirect effect, through aspects of neighborhood affluence and racial/ethnic composition, of receiving a Section 8 voucher on the prevalence of psychiatric disorders among boys and girls in the Moving to Opportunity study.

Research square·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jun 13, 2026

An Objective and Child-friendly Assessment of Arm Function by Using a 3-D Sensor

An Objective and Child-friendly Assessment of Arm Function by Using a 3-D Sensor

Published on: February 12, 2018

Using Wearable Sensors and Machine Learning to Assess Upper Limb Function in Huntington's Disease.

Adonay S Nunes¹, İlkay Yıldız Potter¹, Ram Kinker Mishra¹

¹BioSensics LLC, 57 Chapel St, Newton, MA, USA.

Research Square

|June 17, 2024

Summary

This summary is machine-generated.

Wearable sensors and machine learning can detect upper limb changes in Huntington's disease (HD) and prodromal HD (pHD). This technology aids early detection, remote monitoring, and treatment evaluation for neurological disorders.

Keywords:

Huntington’s disease accelerometer digital health biomarkers upper limb function wearable sensors

More Related Videos

Home-Based Monitor for Gait and Activity Analysis

Home-Based Monitor for Gait and Activity Analysis

Published on: August 8, 2019

Capturing Representative Hand Use at Home Using Egocentric Video in Individuals with Upper Limb Impairment

Capturing Representative Hand Use at Home Using Egocentric Video in Individuals with Upper Limb Impairment

Published on: December 23, 2020

Related Experiment Videos

Last Updated: Jun 13, 2026

An Objective and Child-friendly Assessment of Arm Function by Using a 3-D Sensor

An Objective and Child-friendly Assessment of Arm Function by Using a 3-D Sensor

Published on: February 12, 2018

Home-Based Monitor for Gait and Activity Analysis

Home-Based Monitor for Gait and Activity Analysis

Published on: August 8, 2019

Capturing Representative Hand Use at Home Using Egocentric Video in Individuals with Upper Limb Impairment

Capturing Representative Hand Use at Home Using Egocentric Video in Individuals with Upper Limb Impairment

Published on: December 23, 2020

Area of Science:

Neurology
Biomedical Engineering
Data Science

Background:

Huntington's disease (HD) impacts both upper and lower limb function, but clinical assessments primarily focus on lower limb symptoms.
Wearable sensors offer a method to collect real-world data, complementing clinical evaluations and providing deeper insights into disease progression.

Purpose of the Study:

To monitor upper limb function in individuals with HD, prodromal HD (pHD), and controls (CTR) using wrist-worn wearable sensors during daily activities.
To utilize deep learning and machine learning models to analyze kinematic features of goal-directed movements (GDMs) and predict disease groups and clinical scores.

Main Methods:

A wrist-worn wearable sensor (PAMSys ULM) was used to collect data over seven days from 16 HD patients, 7 pHD patients, and 16 controls.
A deep learning model detected GDMs, and kinematic features were extracted. Statistical and machine learning models were employed for prediction tasks.
High participant compliance (average 99%) ensured data quality for analysis.

Main Results:

Significant differences in GDM features were observed between HD, pHD, and CTR groups.
HD participants exhibited fewer long-duration GDMs compared to controls. The entropy of velocity zero-crossing length segments showed the largest effect size and strongest correlation with clinical scores.
Classification models achieved 67% balanced accuracy for distinguishing groups, with 0.72 recall for the HD group. Regression models accurately predicted clinical scores, explaining up to 60% of the variance in the upper extremity function subdomain of the UHDRS.

Conclusions:

Wearable sensors and machine learning show promise for early identification of phenoconversion in HD.
This approach facilitates remote monitoring of HD patients and evaluation of treatment efficacy in clinical trials.
The study highlights the potential of upper limb monitoring for comprehensive assessment and management of Huntington's disease.