| Paper ID | BIO-12.3 |
| Paper Title |
SHAPELET BASED VISUAL ASSESSMENT OF CLUSTER TENDENCY IN ANALYZING COMPLEX UPPER LIMB MOTION |
| Authors |
Shreyasi Datta, University of Melbourne, Australia; Chandan Karmakar, Deakin University, Australia; Punit Rathore, Massachusetts Institute of Technology, United States; Marimuthu Palaniswami, University of Melbourne, Australia |
| Session | BIO-12: Feature Extraction and Fusion for Biomedical Applications |
| Location | Gather.Town |
| Session Time: | Friday, 11 June, 11:30 - 12:15 |
| Presentation Time: | Friday, 11 June, 11:30 - 12:15 |
| Presentation |
Poster
|
| Topic |
Biomedical Imaging and Signal Processing: [BIO] Biomedical signal processing |
| IEEE Xplore Open Preview |
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| Virtual Presentation |
Click here to watch in the Virtual Conference |
| Abstract |
The evolution of ubiquitous sensors has led to the generation of copious amounts of waveform data. Human motion waveform analysis has found significance in clinical and home-based activity monitoring. Exploration of cluster structure in such waveform data prior to developing learning models is an important pattern recognition problem. A prominent category of algorithms in this direction, known as Visual Assessment of (cluster) Tendency (VAT), employs visual approaches to study cluster evolution through heat maps. This paper proposes shape-iVAT, a new relative of an improved VAT model, that captures local time-series characteristics through representative subsequences, known as shapelets, to identify interesting patterns in motion data. We propose an unsupervised method for shapelet extraction using maximin shape sampling and shape-based distance computation for selecting key shapelets representing characteristic motion patterns. These shapelets are used to transform waveform data into a dissimilarity matrix for VAT evaluation. We demonstrate that the proposed method outperforms standard VAT with global distance measures for identifying complex upper limb motion captured using a camera-based motion sensing device. We also show that our method has significance in efficient and interpretable cluster tendency assessment for anomaly detection and continuous motion monitoring. |
