Daily activities classification on human motion primitives detection dataset

Zi Hau Chin, Hu Ng*, Timothy Tzen Vun Yap, Hau Lee Tong, Chiung Ching Ho, Vik Tor Goh

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

10 Citations (Scopus)


The study is to classify human motion data captured by a wrist worn accelerometer. The classification is based on the various daily activities of a normal person. The dataset is obtained from Human Motion Primitives Detection [1]. There is a total of 839 trials from 14 activities performed by 16 volunteers (11 males and 5 females) ages between 19 to 91 years. A wrist worn tri-axial accelerometer was used to accrue the acceleration data of X, Y and Z axis during each trial. For feature extraction, nine statistical parameters together with the energy spectral density and the correlation between the accelerometer readings are employed to extract 63 features from the raw acceleration data. Particle Swarm Organization, Tabu Search and Ranker are applied to rank and select the positive roles for the later classification process. Classification is implemented using Support Vector Machine, k-Nearest Neighbors and Random Forest. From the experimental results, the proposed model achieved the highest correct classification rate of 91.5% from Support Vector Machine with radial basis function kernel.

Original languageEnglish
Title of host publicationComputational Science and Technology
EditorsRayner Alfred, Ag Asri Ag Ibrahim, Yuto Lim, Patricia Anthony
Number of pages9
ISBN (Electronic)9789811326226
ISBN (Print)9789811326219
Publication statusPublished - 2019
Event5th International Conference on Computational Science and Technology 2018 - Kota Kinabalu, Malaysia
Duration: 29 Aug 201830 Aug 2018

Publication series

NameLecture Notes in Electrical Engineering
ISSN (Print)1876-1100
ISSN (Electronic)1876-1119


Conference5th International Conference on Computational Science and Technology 2018
Abbreviated titleICCST 2018
CityKota Kinabalu


  • Accelerometer
  • Classification
  • Daily activities

ASJC Scopus subject areas

  • Industrial and Manufacturing Engineering


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