Dan-SMC2013.pdf (184.17 kB)
Download file

Human activity recognition for physical rehabilitation

Download (184.17 kB)
conference contribution
posted on 08.02.2016, 14:31 by Daniel Leightley, John Darby, Baihua LiBaihua Li, Jamie S. McPhee, Moi Hoon Yap
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.



  • Science


  • Computer Science

Published in

Proceedings - 2013 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2013


261 - 266


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




AM (Accepted Manuscript)

Publisher statement

This work is made available according to the conditions of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) licence. Full details of this licence are available at: https://creativecommons.org/licenses/by-nc-nd/4.0/

Publication date



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.