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

An incomplete spinal cord injury (SCI) refers to remaining sensorimotor function below the injury with the possibility for the patient to regain walking abilities. 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. This work is a step towards a more deficit-oriented therapy and paves the way for better rehabilitation outcome predictions.

• 1
  Clustering resulted in 4 groups of patients that were compared to each other and to the healthy controls.
• 2
  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.
• 3
  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.