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Quantifying Gait Characteristics and Neurological Effects in people with Spinal Cord Injury using Data-Driven Techniques

Time: Tue 2024-03-12 10.00

Location: E3, Osquars Backe 14

Video link:

Language: English

Subject area: Engineering Mechanics

Doctoral student: Minh Truong , Farkostteknik och Solidmekanik, KTH MoveAbility

Opponent: Associate Professor Peter Christian Raffalt, Department of Nutrition, Exercise and Sports, University of Copenhagen, Denmark

Supervisor: Professor Elena Gutierrez-Farewik, KTH MoveAbility; Assistant Professor Ruoli Wang, KTH MoveAbility; Dr. Emelie Butler Forslund, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden; Professor Åke Seiger, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden

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QC 20240221


Spinal cord injury, whether traumatic or nontraumatic, can partially or completely damage sensorimotor pathways between the brain and the body, leading to heterogeneous gait abnormalities. Mobility impairments also depend on other factors such as age, weight, time since injury, pain, and walking aids used. The ASIA Impairment Scale is recommended to classify injury severity, but is not designed to characterize individual ambulatory capacity. Other standardized tests based on subjective or timing/distance assessments also have only limited ability to determine an individual's capacity. Data-driven techniques have demonstrated effectiveness in analysing complexity in many domains and may provide additional perspectives on the complexity of gait performance in persons with spinal cord injury. The studies in this thesis aimed to address the complexity of gait and functional abilities after spinal cord injury using data-driven approaches.

The aim of the first manuscript was to characterize the heterogeneous gait patterns in persons with incomplete spinal cord injury. Dissimilarities among gait patterns in the study population were quantified with multivariate dynamic time warping. Gait patterns were classified into six distinct clusters using hierarchical agglomerative clustering. Through random forest classifiers with explainable AI, peak ankle plantarflexion during swing was identified as the feature that most often distinguished most clusters from the controls. By combining clinical evaluation with the proposed methods, it was possible to provide comprehensive analyses of the six gait clusters.    

The aim of the second manuscript was to quantify sensorimotor effects on walking performance in persons with spinal cord injury. The relationships between 11 input features and 2 walking outcome measures - distance walked in 6 minutes and net energy cost of transport - were captured using 2 Gaussian process regression models. Explainable AI revealed the importance of muscle strength on both outcome measures. Use of walking aids also influenced distance walked, and  cardiovascular capacity influenced energy cost. Analyses for each person also gave useful insights into individual performance.    

The findings from these studies demonstrate the large potential of advanced machine learning and explainable AI to address the complexity of gait function in persons with spinal cord injury.