Ambulatory Behavior Assessment Using Deep Learning

This work leverages a custom implementation of a deep neural network-based object detection algorithm to detect people and a set of assistive devices relevant to clinical environments. The object detections form the basis for the quantification of different ambulatory activities and related behaviors. Using features extracted from detected people and objects as input to machine learning models, we quantify how a person ambulates and the mode of ambulation being used.Clinical relevance— This system provides the data required for clinicians and hospitalized patients to work together in the creation, monitoring, and adjustment of ambulatory goals.