Sit-to-Stand Movement Recognition Using Kinect

Acorn, Erik, Dipsis, Nikolaos, Pincus, Tamar and Stathis, Kostas (2015) Sit-to-Stand Movement Recognition Using Kinect. In, Gammerman, A, Vovk, V and Papadopoulos, H (eds.) Statistical Learning and Data Sciences. SLDS 2015. (Lecture Notes in Computer Science, 9047) Springer, 179–192. (doi:10.1007/978-3-319-17091-6_13).

Abstract

This paper examines the application of machine-learning techniques to human movement data in order to recognise and compare movements made by different people. Data from an experimental set-up using a sit-to-stand movement are first collected using the Microsoft Kinect input sensor, then normalized and subsequently compared using the assigned labels for correct and incorrect movements. We show that attributes can be extracted from the time series produced by the Kinect sensor using a dynamic time-warping technique. The extracted attributes are then fed to a random forest algorithm, to recognise anomalous behaviour in time series of joint measurements over the whole movement. For comparison, the k-Nearest Neighbours algorithm is also used on the same attributes with good results. Both methods’ results are compared using Multi-Dimensional Scaling for clustering visualisation.

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Contributors

Author: Tamar Pincus

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