Walking pattern classification and walking distance estimation algorithms using gait phase information

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43 Citations (Scopus)

Abstract

This paper presents a walking pattern classification and a walking distance estimation algorithm using gait phase information. A gait phase information retrieval algorithm was developed to analyze the duration of the phases in a gait cycle (i.e., stance, push-off, swing, and heel-strike phases). Based on the gait phase information, a decision tree based on the relations between gait phases was constructed for classifying three different walking patterns (level walking, walking upstairs, and walking downstairs). Gait phase information was also used for developing a walking distance estimation algorithm. The walking distance estimation algorithm consists of the processes of step count and step length estimation. The proposed walking pattern classification and walking distance estimation algorithm have been validated by a series of experiments. The accuracy of the proposed walking pattern classification was 98.87, 95.45, and 95.00 for level walking, walking upstairs, and walking downstairs, respectively. The accuracy of the proposed walking distance estimation algorithm was 96.42 over a walking distance.

Original languageEnglish
Article number26
Pages (from-to)2884-2892
Number of pages9
JournalIEEE Transactions on Biomedical Engineering
Volume59
Issue number10
DOIs
Publication statusPublished - 2012 Sep 27

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Pattern recognition
Decision trees
Information retrieval
Experiments

All Science Journal Classification (ASJC) codes

  • Biomedical Engineering

Cite this

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title = "Walking pattern classification and walking distance estimation algorithms using gait phase information",
abstract = "This paper presents a walking pattern classification and a walking distance estimation algorithm using gait phase information. A gait phase information retrieval algorithm was developed to analyze the duration of the phases in a gait cycle (i.e., stance, push-off, swing, and heel-strike phases). Based on the gait phase information, a decision tree based on the relations between gait phases was constructed for classifying three different walking patterns (level walking, walking upstairs, and walking downstairs). Gait phase information was also used for developing a walking distance estimation algorithm. The walking distance estimation algorithm consists of the processes of step count and step length estimation. The proposed walking pattern classification and walking distance estimation algorithm have been validated by a series of experiments. The accuracy of the proposed walking pattern classification was 98.87, 95.45, and 95.00 for level walking, walking upstairs, and walking downstairs, respectively. The accuracy of the proposed walking distance estimation algorithm was 96.42 over a walking distance.",
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