Deep convolutional neural networks for motion instability identification using kinect

TY - CHAP

T1 - Deep convolutional neural networks for motion instability identification using kinect

AU - Leightley, Daniel

AU - Mukhopadhyay, Subhas C.

AU - Ghayvat, Hemant

AU - Yap, Moi Hoon

PY - 2017/7/19

Y1 - 2017/7/19

N2 - Evaluating the execution style of human motion can give insight into the performance and behaviour exhibited by the participant. This could enable support in developing personalised rehabilitation programmes by providing better understanding of motion mechanics and contextual behaviour. However, performing analyses, generating statistical representations and models which are free from external bins, repeatable and robust is a difficult task. In this work, we propose a framework which evaluates clinically valid motions to identify unstable behaviour during performance using Deep Convolutional Neural Networks. The framework is composed of two parts; 1) Instead of using the whole skeleton as input, we divide the human skeleton into five joint groups. For each group, feature encoding is used to represent spatial and temporal domains to permit high-level abstraction and to remove noise these are then represented using distance matrices. 2) The encoded representations are labelled using an automatic labelling method and evaluated using deep learning. Experimental results demonstrates the ability to correctly classify data compared to classical approaches.

AB - Evaluating the execution style of human motion can give insight into the performance and behaviour exhibited by the participant. This could enable support in developing personalised rehabilitation programmes by providing better understanding of motion mechanics and contextual behaviour. However, performing analyses, generating statistical representations and models which are free from external bins, repeatable and robust is a difficult task. In this work, we propose a framework which evaluates clinically valid motions to identify unstable behaviour during performance using Deep Convolutional Neural Networks. The framework is composed of two parts; 1) Instead of using the whole skeleton as input, we divide the human skeleton into five joint groups. For each group, feature encoding is used to represent spatial and temporal domains to permit high-level abstraction and to remove noise these are then represented using distance matrices. 2) The encoded representations are labelled using an automatic labelling method and evaluated using deep learning. Experimental results demonstrates the ability to correctly classify data compared to classical approaches.

UR - https://www.scopus.com/pages/publications/85027845805

U2 - 10.23919/MVA.2017.7986863

DO - 10.23919/MVA.2017.7986863

M3 - Other chapter contribution

AN - SCOPUS:85027845805

SP - 310

EP - 313

BT - Proceedings of the 15th IAPR International Conference on Machine Vision Applications, MVA 2017

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 15th IAPR International Conference on Machine Vision Applications, MVA 2017

Y2 - 8 May 2017 through 12 May 2017

ER -