Guiding Best Practice Clinical Care With In-Silico Rehabilitation and Statistical Parametric Mapping (SPM 1D) For Unilateral Chronic Hemiparetic Stroke and Trans-Tibial Amputation

Project IDRRG3-19002
PartnerTan Tock Seng Hospital 
Focus
Knee Amputation, Stroke
Research CapabilitiesSPM1D
Clinical PI
Dr Karen CHUA Sui Geok
Senior Consultant, Rehabilitation Medicine
Tan Tock Seng Hospital
Technical PI

Dr Cyril John William DONNELLY
Principal Research Fellow, Rehabilitation Research Institute of Singapore

Dr Ananda SIDARTA
Senior Research Fellow, Rehabilitation Research Institute of Singapore
ResearchersDr Amr Alhossary, Research Fellow, Yong Jia Wei, Project Officer, Babitha Jeevith, Research Associate, Dr Kwong Wai Hang, Research Fellow, Dr Matthew Tay, Rehabilitation Centre, TTSH, Dr Ong Poo Lee, Rehabilitation Centre, TTSH, Dr Wee Seng Kwee, Rehabilitation Centre, TTSH, Phua Min Wee, Rehabilitation Centre, TTSH, Tsurayuki Guanzhi, Foot and Limb Care Centre, TTSH, Trevor Brian Binedell, Foot and Limb Care Centre, TTSH, Tabitha Quake, Foot and Limb Care Centre, TTSH, Chong Wei Binh, Rehabilitation Centre, TTSH

 

The challenge

Gait observation to assess mobility post-stroke is often subjective and lacks sensitivity. Current available clinical instruments also lack sensitivity to capture minute improvement during rehabilitation. Because every stroke is different, can we introduce a more objective evaluation method by adopting recent advancements in technology?

The proposed solution

Objective clinical gait analysis with the use of modern technology is proposed. Here, we employ the motion capture system and movement analytics to assess the mobility and gait of both the stroke and amputees participants.

(1) To collect data using 3D Motion Capture (MOCAP) for 2 groups of subjects with pathological gait populations i.e. (i) chronic hemiparetic strokes and (ii) unilateral transtibial amputees.

(2) To analyse and compare MOCAP data collected for the above populations with reference anonymised data obtained from the local healthy RRIS Ability Data set.

(3) To co-develop with the NTU RRIS team, a clinician-centric SPM1D clinical analysis framework using the validated SPM1D analyses framework for the objective analysis of pathological gait Movement Proficiency Index (MPI) such as stroke and transtibial amputees. This will allow us to develop movement proficiency analysis for whole-body movements down to isolated joint degrees of freedom. 

Current status

The study had been completed in 2022. A total of 15 chronic stroke survivors and amputees participated in our study at the RRIS gait laboratory. Participants performed lower-limb tasks of the Ability Data under the supervision of a clinician. All trials were completed within 2.5 hours. Mocap datasets were labelled, and pre-processed, and group analysis was performed. The primary focus will be spent on the use of Mocap and movement analytics to do objective gait analysis. The study team has also created a Python-based user interface (MovementRx) to view joint trajectories. 

 


Outcome

Publication
  1. Alhossary, A., Pataky, T., Ang, W.T. et al. Versatile clinical movement analysis using statistical parametric mapping in MovementRx. Scientific Reports 13, 2414 (2023).
  2. Gupta D, Donnelly C, Reinbolt J. Physics-Based Guidelines for Accepting Reasonable Dynamic Simulations of Movement. IEEE Trans Biomed Eng. 2022 Mar; 69(3):1194-1201.