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

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 (www.isrctn.com/ISRCTN15993728), 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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The UK's world-leading position in biomedical research is critically dependent upon training scientists with the cutting-edge research skills and technological know-how needed to drive future scientific advances. Since 2009, the EPSRC and MRC CDT in Systems Approaches to Biomedical Science (SABS) has been working with its consortium of 22 industrial and institutional partners to meet this training need.

Over this period, our partners have identified a growing training need caused by the increasing reliance on computational approaches and research software. The new EPSRC CDT in Sustainable Approaches to Biomedical Science: Responsible and Reproducible Research - SABS:R^3 will address this need. By embedding a sustainable approach to software and computational model development into all aspects of the existing SABS training programme, we aim to foster a culture change in how the computational tools and research software that now underpin much of biomedical research are developed, and hence how quantitative and predictive translational biomedical research is undertaken.

As with all CDT Programmes, the future impact of SABS:R^3 will be through its alumni, and by the culture change that its training engenders. By these measures, our existing SABS CDT is already proving remarkably successful. Our alumni have gone on to a wide range of successful careers, 21 in academic research, 19 in industry (including 5 in SABS partner companies) and the other 10 working in organisations from the Office of National Statistics to the EPSRC. SABS' unique Open Innovation framework has facilitated new company connections and a high level of operational freedom, facilitating 14 multi-company, pre-competitive, collaborative doctoral research projects between 11 companies, each focused on a SABS student.

The impact of sustainable and open computational approaches on biomedical research is clear from existing SABS' student projects. Examples include SAbDab which resulted from the first-ever co-sponsored doctorate in SABS, by UCB and Roche. It was released as open source software, is embedded in the pipelines of several pharmaceutical companies (including UCB, Medimmune, GSK, and Lonza) and has resulted in 13 papers. The SABS student who developed SAbDab was initially seconded to MedImmune, sponsored by EPSRC IAA funding; he went on to work at Roche, and is now at BenevolentAI. Similarly, PanDDA, multi-dataset X-ray crystallographic software to detect ligand-bound states in protein complexes is in CCP4 and is an integral part of Diamond Light Source's XChem Pipeline. The SABS student who developed PanDDA was awarded an EMBO Fellowship.

Future SABS:R^3 students will undertake research supported by both our industrial partners and academic supervisors. These supervisors have a strong track record of high impact research through the release of open source software, computational tools, and databases, and through commercialisation and licensing of their research. All of this research has been undertaken in collaboration with industrial partners, with many examples of these tools now in routine use within partner companies.

The newly focused SABS:R^3 will permit new industrial collaborations. Six new partners have joined the consortium to support this new bid, ranging from major multinationals (e.g. Unilever) to SMEs (e.g. Lhasa). SABS:R^3 will continue to make all of its research and teaching resources publicly available and will continue to help to create other centres with similar aims. To promote a wider cultural change, the SABS:R^3 will also engage with the academic publishing industry (Elsevier, OUP, and Taylor & Francis). We will explore novel ways of disseminating the outputs of computational biomedical research, to engender trust in the released tools and software, facilitate more uptake and re-use.