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KINECAL: A Dataset for Falls-Risk Assessment and Balance Impairment Analysis
Sean Maudsley-Barton1, Moi Hoon Yap2
1Department of Computing and Mathematics, Manchester Metropolitan University, Faculty of Science and Engineering, Manchester, M1 5GD, UK. s.maudsley-barton@mmu.ac.uk.
Abstract:
The field of human action recognition has made great strides in recent years, much helped by the availability of a wide variety of datasets that use Kinect to record human movement. Conversely, progress towards the use of Kinect in clinical practice has been hampered by the lack of appropriate data. In particular, datasets that contain clinically significant movements and appropriate metadata. This paper proposes a dataset to address this issue, namely KINECAL. It contains the recordings of 90 individuals carrying out 11 movements, commonly used in the clinical assessment of balance. The dataset contains relevant metadata, including clinical labelling, falls history labelling and postural sway metrics. KINECAL should be of interest to researchers interested in the clinical use of motion capture and motion analysis.
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