title: Automated Analysis and Quantification of Human Mobility Using a Depth Sensor.
abstract: Analysis and quantification of human motion to support clinicians in the decision-making process is the desired outcome for many clinical-based approaches. However, generating statistical models that are free from human interpretation and yet representative is a difficult task. In this paper, we propose a framework that automatically recognizes and evaluates human mobility impairments using the Microsoft Kinect One depth sensor. The framework is composed of two parts. First, it recognizes motions, such as sit-to-stand or walking 4 m, using abstract feature representation techniques and machine learning. Second, evaluation of the motion sequence in the temporal domain by comparing the test participant with a statistical mobility model, generated from tracking movements of healthy people. To complement the framework, we propose an automatic method to enable a fairer, unbiased approach to label motion capture data. Finally, we demonstrate the ability of the framework to recognize and provide clinically relevant feedback to highlight mobility concerns, hence providing a route toward stratified rehabilitation pathways and clinician-led interventions.
authors.forename: Daniel
authors.initials: D
authors.lastname: Leightley
authors.forename: Jamie S
authors.initials: JS
authors.lastname: McPhee
authors.forename: Moi Hoon
authors.initials: MH
authors.lastname: Yap
mesh_terms: D000465:Algorithms; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000295:instrumentation*; D018670:Monitoring, Ambulatory / Q000295:instrumentation* / Q000379:methods*; D009068:Movement / Q000502:physiology*; D012815:Signal Processing, Computer-Assisted; D016138:Walking / Q000502:physiology*