Robotic Technologies and Digital Health Metrics for Assessing Sensorimotor Disability

Abstract

Neurological disorders such as stroke, multiple sclerosis, traumatic brain injury, cerebral palsy, or spinal cord injury result in partial or complete sensorimotor impairments in the affected limbs. To provide an optimal and personalized rehabilitation program, a detailed assessment of the nature and degree of the sensorimotor deficits, as well as their temporal evolution, is crucial. Valid, reliable, and standardized assessments are essential to define the rehabilitation setting and adapt it over the course of a therapy. Many clinical assessments have a limited sensitivity and are not able to capture behavioral intra- and inter-participant variability, which limits their suitability as endpoints for clinical trials and for clinical decision-making. Technological solutions, such as robotics or wearable sensors, are promising approaches that can provide objective, sensitive, and reliable digital health metrics, which could help overcome the common limitations of conventional clinical assessments. This chapter focuses on the novel possibilities that robotic devices offer for assessing upper and lower limb disability and provides an overview of existing approaches. Further, we discuss how such digital health metrics can be selected and validated, and how they could be integrated into predictive computational models. We conclude that robotics and digital health metrics are excellent tools to describe sensorimotor disability that they promise novel insights into long-term recovery and provide the basis for a more data-driven and personalized clinical decision-making.