Musculoskeletal loading from a minimal wearable sensor system during rehabilitation

Project details

“In the future… training and rehabilitation programmes will use wearable and simple imaging technologies to estimate tissue level biomechanics derived from personalised neuromusculoskeletal modelling in real-time in the real-world. The future is not that far away.” (Lloyd, 2021, International Society of Biomechanics in Sports career award paper).

Background

Knowledge of tissue-specific loads on the muscles, bones, tendons, and ligaments of the human musculoskeletal system has implications for the prescription and monitoring of exercises across clinical, sporting, and occupational rehabilitation. These loads can currently be estimated via expensive, challenging, and time-consuming laboratory and/or computer simulation methods but field-based measures of ‘training load’ are mostly limited to whole-body summary metrics such as speed or distance (via Global Positioning Systems), or cumulative accelerations experienced at the upper back (via accelerometer). Recent developments, however, have seen the use of a whole-body set of wearable inertial measurement units (IMUs) to estimate three-dimensional body configuration.

Your Project

Your project will build upon these developments, using a combination of physics-based modelling and machine learning to estimate body positions from a minimal sensor set during a range of lower limb rehabilitation exercises as input to musculoskeletal models. A primary challenge is to predict input data relating to multiple body segments from IMUs positioned on only a few body segments. The novel methods used will include data augmentation approaches to generate a great number of synthetic IMU datasets from lab-based motion capture recordings. All prediction techniques will be applied primarily to real IMU data but also to synthetic data where this facilitates a greater number of trials and/or sensor positions. Iterations of synthetic and experimental IMU trials will determine the minimal number of sensors and their positions/processing to satisfactorily categorise common rehabilitation exercises and subsequently estimate the continuous positions of additional body segments during those exercises.

The aims of the project are to: 1) predict orientations of additional body segments from a minimal sensor set during common lower-limb rehabilitation exercises; 2) estimate tissue-specific loading parameters from minimal sensor inputs via musculoskeletal modelling; 3) evaluate sensor-informed model estimates of internal loading parameters against those from laboratory-based motion capture inputs; and 4) quantify and rank internal loading parameters during common lower limb rehabilitation exercise progressions.

Your Group

Loughborough University has been ranked number one in the world for sport-related subjects for six consecutive years (QS World University Rankings). In REF 2021, 100% of research impact from the School of Sport, Exercise and Health Sciences was rated 'world-leading' or 'internationally excellent'. You will be part of our Sports Biomechanics and Motor Control team, one of the world’s largest sports biomechanics research groups, historically specialising in the computer simulation of sporting movements. We are a community based on mutual support and collaboration. You will research, learn, and practise alongside a team of senior academics, Doctoral Researchers, and postgraduate taught students working in similar research areas. Through our Doctoral College, postgraduate courses, group activities, and continuing professional development programmes, there are continual opportunities for building important research skills and networks among your peers and research academics.

Your Supervisors

Dr McErlain-Naylor’s primary ongoing research focus is the estimation of tissue-specific internal biomechanical load during training and/rehabilitation, and its association with adaptation/injury. A core aspect is the integration of wearable technology and musculoskeletal modelling. Dr Fong is a Reader in Sports Medicine and Biomechanics, whose research focuses on understanding lower-limb injuries as well as their prevention and rehabilitation. Dr Esliger is a Reader in Digital Health, whose research includes profiling physical activity via wearable technology as well as the dose-exposure relationship with outcomes of interest. All supervisors are members of the National Centre for Sport and Exercise Medicine (East Midlands), and the school’s research is closely aligned with the new National Rehabilitation Centre.

Entry requirements

Our entry requirements are listed using standard UK undergraduate degree classifications i.e. first-class honours, upper second-class honours and lower second-class honours. To learn the equivalent for your country, please choose it from the drop-down below.

Entry requirements for United Kingdom

Applicants should have, or expect to achieve, at least a 2:1 bachelor’s degree (or equivalent) in sport and exercise science, biomechanics, engineering, computer science, or a related subject. This project would suit those who understand the role of signal processing in the analysis of human movement. A relevant master’s degree and/or experience in one or more of the following will be an advantage but not essential: 3D motion capture; wearable technology (e.g., inertial measurement units); and Python, MATLAB, or similar programming language.

English language requirements

Applicants must meet the minimum English language requirements. Further details are available on the International website.

How to apply

All applications should be made online. Under programme name, select School of Sport, Exercise and Health Sciences. Please quote the advertised reference number: SSEHS/SMNDF in your application.

To avoid delays in processing your application, please ensure that you submit the minimum supporting documents.

Start date: July 2023