Discovering Differences in Acoustic Emission Between Healthy and Osteoarthritic Knees Using a Four-Phase Model of Sit-Stand-Sit Movements

Lik-Kwan Shark*, 1, Hongzhi Chen1, John Goodacre2
1 ADSIP (Applied Digital Signal and Image Processing) Research Centre, University of Central Lancashire, Preston, PR1 2HE, UK
2 School of Health and Medicine, Lancaster University, Lancaster, LA1 4YD, UK

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© Shark et al.; Licensee Bentham Open.

open-access license: This is an open access article licensed under the terms of the Creative Commons Attribution Non-Commercial License ( which permits unrestricted, non-commercial use, distribution and reproduction in any medium, provided the work is properly cited

* Address correspondence to this author at the ADSIP (Applied Digital Signal and Image Processing) Research Centre, University of Central Lancashire, Preston, PR1 2HE, UK; Tel: +44 (0)1772 893253; Fax: +44 (0)1772 892915; E-mail:


By performing repeated sit-stand-sit movements to create stress on knee joints, short transient bursts of high frequency acoustic emission (AE) released by the knee joints were acquired from two age matched groups consisting of healthy and osteoarthritic (OA) knees, and significant differences between these two groups were discovered from the signal analysis performed. The analysis is based on a four-phase model of sit-stand-sit movements and a two-feature descriptor of AE bursts. The four phases are derived from joint angle measurement during movement, and they consist of the ascending-acceleration and ascending-deceleration phases in the sit-to-stand movement, followed by the descending-acceleration and descending-deceleration phases in the stand-to-sit movement. The two features are extracted from AE measurement during movement, and they consist of the peak magnitude value and average signal level of each AE burst. The proposed analysis method is shown to provide a high sensitivity for differentiation of the two age matched healthy and OA groups, with the most significant difference found to come from the peak magnitude value in the ascending-deceleration phase, clear quantity and strength differences in the image based visual display of their AE feature profiles due to substantially more AE bursts from OA knee joints with higher peak magnitude values and higher average signal levels, and two well separated clusters in the space formed by the principal components. These results provide ample support for further development of AE as a novel tool to facilitate dynamic integrity assessment of knee joints in clinic and home settings.

Keywords: Acoustic emission, knee joint assessment.