Literature DB >> 21807521

Ambulatory running speed estimation using an inertial sensor.

Shuozhi Yang1, Chris Mohr, Qingguo Li.   

Abstract

Techniques have been developed to analyze walking gait using accelerometer and gyroscope data from miniature inertial measurement units (IMU), but few attempts have been made to use similar approaches for running gait. The purpose of this study was to develop an algorithm capable of estimating running speed using a single shank-mounted IMU. Raw acceleration and angular velocity were recorded from an IMU sensor attached on the lateral side of the shank in the sagittal plane and a method of reliably detecting the shank vertical and the minimal shank velocity gait event was used to segment a running sequence into individual strides. Through integration, the orientation of the shank segment was determined and an estimate of stride-by-stride running speed was calculated by integrating the acceleration data. The algorithm was verified using data collected from a group of seven volunteers running on a treadmill at speeds between 2.50 m/s and 3.50 m/s. Over the entire speed range, the estimation results gave a percentage root mean square error (%RMSE) of approximately 4.10%. With the accurate estimation capability and portability, the use of the proposed system in outdoor running gait analysis is promising.
Copyright © 2011 Elsevier B.V. All rights reserved.

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Year:  2011        PMID: 21807521     DOI: 10.1016/j.gaitpost.2011.06.019

Source DB:  PubMed          Journal:  Gait Posture        ISSN: 0966-6362            Impact factor:   2.840


  9 in total

Review 1.  Trends Supporting the In-Field Use of Wearable Inertial Sensors for Sport Performance Evaluation: A Systematic Review.

Authors:  Valentina Camomilla; Elena Bergamini; Silvia Fantozzi; Giuseppe Vannozzi
Journal:  Sensors (Basel)       Date:  2018-03-15       Impact factor: 3.576

2.  Wearables for Running Gait Analysis: A Systematic Review.

Authors:  Rachel Mason; Liam T Pearson; Gillian Barry; Fraser Young; Oisin Lennon; Alan Godfrey; Samuel Stuart
Journal:  Sports Med       Date:  2022-10-15       Impact factor: 11.928

Review 3.  Inertial sensor-based methods in walking speed estimation: a systematic review.

Authors:  Shuozhi Yang; Qingguo Li
Journal:  Sensors (Basel)       Date:  2012-05-10       Impact factor: 3.576

4.  The Effect of the Accelerometer Operating Range on Biomechanical Parameters: Stride Length, Velocity, and Peak Tibial Acceleration during Running.

Authors:  Christian Mitschke; Pierre Kiesewetter; Thomas L Milani
Journal:  Sensors (Basel)       Date:  2018-01-05       Impact factor: 3.576

5.  A machine learning approach for gait speed estimation using skin-mounted wearable sensors: From healthy controls to individuals with multiple sclerosis.

Authors:  Ryan S McGinnis; Nikhil Mahadevan; Yaejin Moon; Kirsten Seagers; Nirav Sheth; John A Wright; Steven DiCristofaro; Ikaro Silva; Elise Jortberg; Melissa Ceruolo; Jesus A Pindado; Jacob Sosnoff; Roozbeh Ghaffari; Shyamal Patel
Journal:  PLoS One       Date:  2017-06-01       Impact factor: 3.240

6.  Running Speed Can Be Predicted from Foot Contact Time during Outdoor over Ground Running.

Authors:  Cornelis J de Ruiter; Ben van Oeveren; Agnieta Francke; Patrick Zijlstra; Jaap H van Dieen
Journal:  PLoS One       Date:  2016-09-20       Impact factor: 3.240

7.  Running Speed Estimation Using Shoe-Worn Inertial Sensors: Direct Integration, Linear, and Personalized Model.

Authors:  Mathieu Falbriard; Abolfazl Soltani; Kamiar Aminian
Journal:  Front Sports Act Living       Date:  2021-03-18

8.  Estimation of Foot Trajectory and Stride Length during Level Ground Running Using Foot-Mounted Inertial Measurement Units.

Authors:  Yuta Suzuki; Michael E Hahn; Yasushi Enomoto
Journal:  Sensors (Basel)       Date:  2022-09-20       Impact factor: 3.847

9.  Robust Foot Clearance Estimation Based on the Integration of Foot-Mounted IMU Acceleration Data.

Authors:  Mourad Benoussaad; Benoît Sijobert; Katja Mombaur; Christine Azevedo Coste
Journal:  Sensors (Basel)       Date:  2015-12-23       Impact factor: 3.576

  9 in total

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