In-lab versus at-home activity recognition in ambulatory subjects with incomplete spinal cord injury.

Background: Although commercially available activity trackers can aid in tracking therapy and recovery of patients, most devices perform poorly for patients with irregular movement patterns. Standard machine learning techniques can be applied on recorded accelerometer signals in order to classify the activities of ambulatory subjects with incomplete spinal cord injury in a way that is specific to this population and the location of the recording-at home or in the clinic.

Methods: Subjects were instructed to perform a standardized set of movements while wearing a waist-worn accelerometer in the clinic and at-home.