Literature DB >> 15759579

Assessment of walking features from foot inertial sensing.

Angelo M Sabatini1, Chiara Martelloni, Sergio Scapellato, Filippo Cavallo.   

Abstract

An ambulatory monitoring system is developed for the estimation of spatio-temporal gait parameters. The inertial measurement unit embedded in the system is composed of one biaxial accelerometer and one rate gyroscope, and it reconstructs the sagittal trajectory of a sensed point on the instep of the foot. A gait phase segmentation procedure is devised to determine temporal gait parameters, including stride time and relative stance; the procedure allows to define the time intervals needed for carrying an efficient implementation of the strapdown integration, which allows to estimate stride length, walking speed, and incline. The measurement accuracy of walking speed and inclines assessments is evaluated by experiments carried on adult healthy subjects walking on a motorized treadmill. Root-mean-square errors less than 0.18 km/h (speed) and 1.52% (incline) are obtained for tested speeds and inclines varying in the intervals [3, 6] km/h and [-5, + 15]%, respectively. Based on the results of these experiments, it is concluded that foot inertial sensing is a promising tool for the reliable identification of subsequent gait cycles and the accurate assessment of walking speed and incline.

Mesh:

Year:  2005        PMID: 15759579     DOI: 10.1109/TBME.2004.840727

Source DB:  PubMed          Journal:  IEEE Trans Biomed Eng        ISSN: 0018-9294            Impact factor:   4.538


  83 in total

1.  Automatic individual calibration in fall detection--an integrative ambulatory measurement framework.

Authors:  Jian Liu; Thurmon E Lockhart
Journal:  Comput Methods Biomech Biomed Engin       Date:  2011-12-08       Impact factor: 1.763

2.  Inertial sensors in estimating walking speed and inclination: an evaluation of sensor error models.

Authors:  Shuozhi Yang; Annemarie Laudanski; Qingguo Li
Journal:  Med Biol Eng Comput       Date:  2012-03-15       Impact factor: 2.602

3.  An adaptive gyroscope-based algorithm for temporal gait analysis.

Authors:  Barry R Greene; Denise McGrath; Ross O'Neill; Karol J O'Donovan; Adrian Burns; Brian Caulfield
Journal:  Med Biol Eng Comput       Date:  2010-11-02       Impact factor: 2.602

4.  BioKin: an ambulatory platform for gait kinematic and feature assessment.

Authors:  Samitha W Ekanayake; Andrew J Morris; Mike Forrester; Pubudu N Pathirana
Journal:  Healthc Technol Lett       Date:  2015-02-25

5.  Gait Cycle Validation and Segmentation Using Inertial Sensors.

Authors:  G V Prateek; Pietro Mazzoni; Gammon M Earhart; Arye Nehorai
Journal:  IEEE Trans Biomed Eng       Date:  2019-11-25       Impact factor: 4.538

6.  Quaternion-based strap-down integration method for applications of inertial sensing to gait analysis.

Authors:  A M Sabatini
Journal:  Med Biol Eng Comput       Date:  2005-01       Impact factor: 2.602

7.  Statistical prediction of load carriage mode and magnitude from inertial sensor derived gait kinematics.

Authors:  Sol Lim; Clive D'Souza
Journal:  Appl Ergon       Date:  2018-11-29       Impact factor: 3.661

8.  Identification of Patients with Sarcopenia Using Gait Parameters Based on Inertial Sensors.

Authors:  Jeong-Kyun Kim; Myung-Nam Bae; Kang Bok Lee; Sang Gi Hong
Journal:  Sensors (Basel)       Date:  2021-03-04       Impact factor: 3.576

9.  Validity and repeatability of inertial measurement units for measuring gait parameters.

Authors:  Edward P Washabaugh; Tarun Kalyanaraman; Peter G Adamczyk; Edward S Claflin; Chandramouli Krishnan
Journal:  Gait Posture       Date:  2017-04-12       Impact factor: 2.840

10.  Ambulatory human motion tracking by fusion of inertial and magnetic sensing with adaptive actuation.

Authors:  H Martin Schepers; Daniel Roetenberg; Peter H Veltink
Journal:  Med Biol Eng Comput       Date:  2009-12-17       Impact factor: 2.602

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