There are three objectives of the project:
O1: Personalised neuro-musculoskeletal modelling: A novel upper limb neuro-musculoskeletal mathematical model will be established to predict individual muscle contribution during movement. A hybrid calibration procedure will be developed to estimate personalised model parameters (i.e. model optimal muscle fibre length, tendon slack length, muscle fibre contraction velocity, muscle damping, and pennation angle, etc.).
O2: Personalised quantitative upper extremity assessment: A quantitative, personalised and objective MII will be developed via dissimilarity analysis to quantify the movement differences between the impaired arm and the healthy arm during mirrored exercises and obtain the objective upper extremity impairment assessment.
O3: Two-stage feasibility study: 10 asymptomatic volunteers (5 males and 5 females) and 20 stroke survivors (individuals with any extent of arm weakness, 10 females and 10 males) will be recruited to evaluate the effectiveness of the developed MII, with the support of National Demonstration Centre for Rehabilitation at Leeds Teaching Hospital NHS Trust.
To achieve O1, a series of physics-informed data-driven musculoskeletal models were proposed for personalised neuro-musculoskeletal modelling. The feasibility and effectiveness of the proposed approaches were verified on 10 healthy volunteers. Different from existing physics-based and data-driven modelling approaches, the proposed approaches can accurately reflect the underlying physical neuro-mechanical processes and have fast execution speed. To achieve O2, a quantitative, personalised, and objective motor impairment index was established by evaluating the difference between the healthy arm and the impaired arm during mirrored exercises to quantify the motor impairment. The effectiveness of the established approach was verified with regard to the conventional clinical approaches, such as FM-UE and ARAT scores. To achieve O3, 10 asymptomatic volunteers were first recruited to evaluate the system at the Leeds’ Rehabilitation Robotics and Sensing Lab. 20 volunteers with stroke were also recruited to further evaluate the system at the lab.
