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Title: Human activity recognition for physical rehabilitation
Authors: Leightley, Daniel
Darby, John
Li, Baihua
McPhee, Jamie S.
Yap, Moi Hoon
Keywords: Kinect
Machine Learning
Random Forests
Support Vector Machines
Issue Date: 2013
Publisher: © IEEE
Citation: LEIGHTLEY, D. ... et al, 2013. Human activity recognition for physical rehabilitation. Proceedings - 2013 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2013, 13th-a6th October, Manchester, UK, pp.261-266
Abstract: The recognition of human activity is a challenging topic for machine learning. We present an analysis of Support Vector Machines (SVM) and Random Forests (RF) in their ability to accurately classify Kinect kinematic activities. Twenty participants were captured using the Microsoft Kinect performing ten physical rehabilitation activities. We extracted the kinematic location, velocity and energy of the skeletal joints at each frame of the activity to form a feature vector. Principle Component Analysis (PCA) was applied as a pre-processing step to reduce dimensionality and identify significant features amongst activity classes. SVM and RF are then trained on the PCA feature space to assess classification performance; we undertook an incremental increase in the dataset size.We analyse the classification accuracy, model training and classification time quantitatively at each incremental increase. The experimental results demonstrate that RF outperformed SVM in classification rate for six out of the ten activities. Although SVM has performance advantages in training time, RF would be more suited to real-time activity classification due to its low classification time and high classification accuracy when using eight to ten participants in the training set. © 2013 IEEE.
Description: This is the accepted manuscript version of the paper. © 2013 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Version: Accepted for publication
DOI: 10.1109/SMC.2013.51
URI: https://dspace.lboro.ac.uk/2134/20250
Publisher Link: http://dx.doi.org/10.1109/SMC.2013.51
ISBN: 9780769551548
Appears in Collections:Conference Papers and Presentations (Computer Science)

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