Data-driven characterization of walking after a spinal cord injury using inertial sensors

Abstract

Background: An incomplete spinal cord injury (SCI) refers to remaining sensorimotor function below the injury with the possibility for the patient to regain walking abilities. However, these patients often suffer from diverse gait deficits, which are not objectively assessed in the current clinical routine. Wearable inertial sensors are a promising tool to capture gait patterns objectively and started to gain ground for other neurological disorders such as stroke, multiple sclerosis, and Parkinson’s disease. In this work, we present a data-driven approach to assess walking for SCI patients based on sensor-derived outcome measures. We aimed to (i) characterize their walking pattern in more depth by identifying groups with similar walking characteristics and (ii) use sensor-derived gait parameters as predictors for future walking capacity. Methods: The dataset analyzed consisted of 66 SCI patients and 20 healthy controls performing a standardized gait test, namely the 6-min walking test (6MWT), while wearing a sparse sensor setup of one sensor attached to each ankle. A data-driven approach has been followed using statistical methods and machine learning models to identify relevant and non-redundant gait parameters. Results: Clustering resulted in 4 groups of patients that were compared to each other and to the healthy controls. The clusters did differ in terms of their average walking speed but also in terms of more qualitative gait parameters such as variability or parameters indicating compensatory movements. Further, using longitudinal data from a subset of patients that performed the 6MWT several times during their rehabilitation, a prediction model has been trained to estimate whether the patient’s walking speed will improve significantly in the future. Including sensor-derived gait parameters as inputs for the prediction model resulted in an accuracy of 80%, which is a considerable improvement of 10% compared to using only the days since injury, the present 6MWT distance, and the days until the next 6MWT as predictors. Conclusions: In summary, the work presented proves that sensor-derived gait parameters provide additional information on walking characteristics and thus are beneficial to complement clinical walking assessments of SCI patients. This work is a step towards a more deficit-oriented therapy and paves the way for better rehabilitation outcome predictions.