Human Gait Patterns Classification based on MEMS Data using Unsupervised and Supervised Learning Algorithms

My N. Nguyen, Kar-Kin Zao, Hai Nguyen Thanh

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

With the proliferation of smartphones and wearable devices having Micro-Electro-Mechanical Systems (MEMS) sensors built in, data samples of linear acceleration and angular velocity can be collected almost anytime anywhere. These motion data can be used to identify various types of human motions and to detect the anomaly of individuals movements. This work presents attempts to use the unsupervised Affinity Propagation (AP) clustering algorithm and the supervised Support Vector Machine (SVM) classification algorithm to identify four types of human gait motions: walking, jogging, climbing upstairs and downstairs. Features of three-dimensional linear acceleration that can enable the algorithms to identify these motion types correctly were selected by analyzing the variation of the feature values among different motion types. Efficacy of Affinity Propagation (AP), Linear and Non-linear Support Vector Machine (SVM) algorithms were also studied by comparing their ratios of correct, false positive, false negative and F1 score classification. This preliminary study demonstrated Linear SVM achieved the best performance, followed by Affinity Propagation. Quite surprisingly, Non-linear SVM appeared to be inferior to the other two algorithms.
Original languageEnglish
Title of host publicationPROCEEDINGS OF 2019 11TH INTERNATIONAL CONFERENCE ON KNOWLEDGE AND SYSTEMS ENGINEERING (KSE 2019)
PublisherIEEE
Pages405-409
Number of pages5
ISBN (Print)978-1-7281-3003-3
StatePublished - 2019
Event11th International Conference on Knowledge and Systems Engineering (KSE) - Da Nang, Viet Nam
Duration: 24 Oct 201926 Oct 2019

Conference

Conference11th International Conference on Knowledge and Systems Engineering (KSE)
CountryViet Nam
CityDa Nang
Period24/10/1926/10/19

Keywords

  • human gait motions
  • Affinity Propagation
  • motion sensors
  • Support Vector Machine

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