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Related Experiment Video

Updated: Jul 1, 2025

Home-Based Monitor for Gait and Activity Analysis

Home-Based Monitor for Gait and Activity Analysis

Published on: August 8, 2019

Modeling multiple sclerosis using mobile and wearable sensor data.

Shkurta Gashi^1,2, Pietro Oldrati³, Max Moebus⁴

¹Department of Computer Science, ETH Zürich, Zürich, Switzerland. shkurta.gashi@ai.ethz.ch.

NPJ Digital Medicine

|March 12, 2024

Summary

This summary is machine-generated.

Remote monitoring using mobile and wearable sensors can effectively track multiple sclerosis (MS) progression and patient status. This approach aids in early diagnosis, treatment management, and clinical trial identification for people with MS (PwMS).

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Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease

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Published on: July 24, 2019

Area of Science:

Neurology
Biomedical Engineering
Digital Health

Background:

Multiple sclerosis (MS) is a leading cause of non-traumatic disability in young adults, necessitating frequent monitoring for timely intervention.
Current diagnostic and monitoring methods for MS rely on infrequent clinical visits, hindering early treatment access and disease progression management.
There is a critical need for remote, frequent monitoring solutions to improve the management of multiple sclerosis.

Purpose of the Study:

To investigate the feasibility and clinical utility of mobile and wearable sensor-derived features for monitoring multiple sclerosis (MS).
To differentiate people with MS (PwMS) from healthy controls using behavioral markers derived from sensor data.
To assess the ability of these features to recognize MS disability and fatigue levels.

Main Methods:

Collected a dataset from 55 PwMS and 24 healthy controls over 489 days in free-living conditions.
Utilized data from arm-worn devices (heart rate), smartphone applications (phone locks), patient health records (MS type), and validated questionnaires (fatigue levels).
Formalized clinical knowledge to derive behavioral markers characterizing MS from sensor and self-report data.

Main Results:

Demonstrated the feasibility of using features from mobile and wearable sensors for monitoring multiple sclerosis (MS).
Showcased the potential of these digital markers to distinguish people with MS (PwMS) from healthy individuals.
Indicated the capability of the approach to recognize varying levels of MS-related disability and fatigue.

Conclusions:

Mobile and wearable sensors offer a viable solution for continuous, remote monitoring of MS in real-world settings.
This technology can facilitate effective disease management, treatment evaluation, and patient identification for clinical trials.
The findings support the integration of digital health tools into the routine care of individuals with multiple sclerosis.