Automated Analysis and Quantification of Human Mobility using a Depth Sensor

Leightley, D, Yap, MH and McPhee, J (2016) Automated Analysis and Quantification of Human Mobility using a Depth Sensor. IEEE Journal of Biomedical and Health Informatics (99). ISSN 2168-2194

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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 work, we propose a framework that automatically recognises and evaluates human mobility impairments using the Microsoft Kinect One depth sensor. The framework is composed of two parts. Firstly, it recognises motions, such as sit-to-stand or walking 4 metres, using abstract feature representation techniques and machine learning. Secondly, 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 recognise and provide clinically relevant feedback to highlight mobility concerns, hence providing a route towards stratified rehabilitation pathways and clinician led interventions.