Assistive technologies such as wheelchairs, canes, and walkers have significantly improved the mobility, function, and quality of life for individuals with spinal cord injury (SCI). In this article, we propose a framework which combines machine learning algorithms with wearable sensors to capture and track mobility in individuals with SCI. Pilot testing in two individuals without SCI indicated that four to seven features obtained from sensors worn on the body or placed on the assistive technology could successfully detect mobility and mobility modes. The classification accuracy for Naïve Bayes and Decision Tree algorithms to detect mobility from non-mobility activity varied from 87.4% to 97.6%. The classification accuracy for detecting six mobility modes within mobility ranged from 88.5% to 90.6%. The proposed
framework has the potential to assist researchers and clinicians to study complex mobility patterns of individuals with SCI and provide adaptive rehabilitation and physical activity interventions in the community.