Development of a method for monitoring the balance of people with Multiple Sclerosis using smartphone inertial sensors

Lead Research Organisation: University of Oxford
Department Name: Sustain Approach to Biomedical Sci CDT


Multiple Sclerosis causes damage to the central nervous system, often resulting in challenges with mobility and the ability to maintain balance (also described as loss of postural stability).
By evaluating the balance of people with Multiple Sclerosis, clinicians and researchers can better understand the symptoms and how they change with disease progression.
Existing clinical assessment scales can be subjective and offer only a snapshot of symptoms at one time in a clinic setting and patient self-reporting of features such as number of falls is known to be unreliable. Other techniques for monitoring balance and stability rely on infrastructure such as motion capture cameras, force plates & pressure sensors, which require financial investment and are limited by the rate at which they can assess patients. Systems of inertial measurement sensors have previously been used to monitor the movement of individuals with diseases.
This project aims to develop, improve, and assess the validity of smartphone-based measures of balance of people with Multiple Sclerosis. With their low cost and widespread availability, the sensors in a smartphone's inertial measurement unit can allow for near constant monitoring with near real time data potential available.
The industrial partners, F. Hoffmann-La Roche AG's pRED Informatics - Digital Biomarkers group, have worked with The University of Plymouth on a trial exploring the symptoms of people with multiple sclerosis and how they can be monitored using smartphones (, which will be the source of the data used in this project.
This work will begin with developing a data processing pipeline using signal processing to extract clinically relevant features of balance from smartphone inertial sensor data.
Force plates have previously been used to assess balance in people with multiple sclerosis, and this work will attempt to generate synthetic force plate measurements using smartphone inertial sensors, reducing the need for expensive infrastructure.
By building models based on motion capture data and simulating virtual inertial measurement unit placements, sensor placement can be explored and optimised.
Passive monitoring data will be analysed to see if clinically meaningful features of balance in people with Multiple Sclerosis can be detected in an individual's daily life without needing to perform specific tests.
Finally, this work will investigate whether similar techniques can be used to assess dynamic balance in people with MS when they are walking, drawing on ideas from control engineering and robotics.
This work will make it easier to monitor the symptoms of Multiple Sclerosis related to balance by using sensors and instrumentation already included in most smartphones, which means no new equipment will be required. The creation of a new tool for monitoring the progression of symptoms of loss of postural stability will provide patients and clinicians with useful data to inform the management of the patient's condition. The tool can also be used in research to monitor the symptoms of trial participants between research visits to understand the effect of interventions that are being investigated.
This work is in the "Healthcare technologies" and "Engineering" EPSRC Research themes, the work is in the: "Assistive technology, rehabilitation and musculoskeletal biomechanics", "Digital signal processing", "Sensors and instrumentation", and "Biological informatics" research areas, additionally this work will draw on ideas from "Non-linear systems", "Control engineering", and "Robotics".


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Studentship Projects

Project Reference Relationship Related To Start End Student Name
EP/S024093/1 30/09/2019 30/03/2028
2445256 Studentship EP/S024093/1 30/09/2020 29/09/2024 Olivia Simpson