Automatic segmentation for one leg stance test with inertial measurement unit

W. Kong, T. Kodama, S. Sessa, Sarah Cosentino, D. Magistro, R. Kawashima, Atsuo Takanishi

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

1 Citation (Scopus)


One Leg Stance (OLS), a test assessing postural stability, is popularly conducted both in clinic and community settings because it is inexpensive and time-efficient. However, the evaluation based on visual observation and manual time measurement with a stop-watch cannot provide quantitative and detailed parameters for longitudinal or cross-sectional studies. In recent years, to overcome these limitations, the use of Inertial Measurement Unit (IMU) as objective measurement analysis tools is becoming more and more popular. However, the greatest issue is that IMU data segmentation is still time-consuming and prone to errors, as the OLS segmentation is being done manually, off-line, on recorded data. In this paper we proposed a novel algorithm for the automatic segmentation of IMU data of the OLS test. The result showed that the correct rate of detection was over 90% which was close to the correct rate in manual segmentation. Compared to manual segmentation with video, besides being less time-consuming, the proposed algorithm closes the loop making the data acquisition and analysis completely automatic, thus can be integrated in self-assessment smart phone applications, allowing the continuous tracking of postural stability also outside clinics and health-care facilities.

Original languageEnglish
Title of host publicationSII 2016 - 2016 IEEE/SICE International Symposium on System Integration
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages6
ISBN (Electronic)9781509033294
Publication statusPublished - 2017 Feb 6
Event2016 IEEE/SICE International Symposium on System Integration, SII 2016 - Sapporo, Japan
Duration: 2016 Dec 132016 Dec 15


Other2016 IEEE/SICE International Symposium on System Integration, SII 2016

ASJC Scopus subject areas

  • Biomedical Engineering
  • Control and Systems Engineering
  • Mechanical Engineering
  • Artificial Intelligence
  • Hardware and Architecture
  • Control and Optimization

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