Portable Activity Monitoring System for Temporal Parameters of Gait Cycles

  • Authors:
  • Jung-Ah Lee;Sang-Hyun Cho;Young-Jae Lee;Heui-Kyung Yang;Jeong-Whan Lee

  • Affiliations:
  • Department of Excercise & Cognitive Rehabilitation, National Rehabilitation Center Research Institute, Seoul, South Korea;Department of Rehabilitation Therapy, Yonsei University, Kangwon-do, South Korea 220-710;School of Biomedical Engineering, Research Institute of Biomedical Engineering, College of Biomedical & Life Science, Konkuk University, Chungju, South Korea 322;School of Electronics and Information Engineering, Chongju University, Chungbuk-do, South Korea 360-764;School of Biomedical Engineering, Research Institute of Biomedical Engineering, College of Biomedical & Life Science, Konkuk University, Chungju, South Korea 322

  • Venue:
  • Journal of Medical Systems
  • Year:
  • 2010

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Abstract

A portable and wireless activity monitoring system was developed for the estimation of temporal gait parameters. The new system was built using three-axis accelerometers to automatically detect walking steps with various walking speeds. The accuracy of walking step-peak detection algorithm was assessed by using a running machine with variable speeds. To assess the consistency of gait parameter analysis system, estimated parameters, such as heel-contact and toe-off time based on accelerometers and footswitches were compared for consecutive 20 steps from 19 individual healthy subjects. Accelerometers and footswitches had high consistency in the temporal gait parameters. The stance, swing, single-limb support, and double-limb support time of gait cycle revealed ICCs values of 0.95, 0.93, 0.86, and 0.75 on the right and 0.96, 0.86, 0.93, 0.84 on the left, respectively. And the walking step-peak detection accuracy was 99.15% (卤0.007) for the proposed method compared to 87.48% (卤0.033) for a pedometer. Therefore, the proposed activity monitoring system proved to be a reliable and useful tool for identification of temporal gait parameters and walking pattern classification.