e-space
Manchester Metropolitan University's Research Repository

Automated Analysis and Quantification of Human Mobility using a Depth Sensor

Leightley, D and 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

[img]
Preview

Download (3MB) | Preview

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.

Impact and Reach

Statistics

Downloads
Activity Overview
120Downloads
129Hits

Additional statistics for this dataset are available via IRStats2.

Altmetric

Actions (login required)

Edit Item Edit Item