Literature DB >> 22255090

A concurrent comparison of inertia sensor-based walking speed estimation methods.

Annemarie Laudanski1, Shuozhi Yang, Qingguo Li.   

Abstract

This study performed a concurrent comparison of two walking speed estimation methods using shank- and foot-mounted inertial measurement units (IMUs). Based on the cyclic gait pattern of the stance leg during walking, data was segmented into a series of individual stride cycles. The angular velocity and linear accelerations of the shank and foot over each of these cycles were then integrated to determine the walking speed. The evaluation was performed on 10 healthy subjects during treadmill walking where known treadmill speeds were compared with the estimated walking speeds under normal and toe-out walking conditions. Results from the shank-mounted IMU sensor yielded more accurate walking speed estimates, with a maximum root mean square estimation error (RMSE) of 0.09 m/s in normal walking and 0.10 m/s in toe-out conditions; while the foot-mounted IMU sensors yielded a maximum RMSE of 0.14 m/s in normal walking and 0.26 m/s in toe-out conditions. Shank-mounted IMU sensors may prove to be of great benefit in accurately estimating walking speeds in patients whose gait is characterized by abnormal foot motions.

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Year:  2011        PMID: 22255090     DOI: 10.1109/IEMBS.2011.6090941

Source DB:  PubMed          Journal:  Conf Proc IEEE Eng Med Biol Soc        ISSN: 1557-170X


  8 in total

Review 1.  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

2.  Regression Model-Based Walking Speed Estimation Using Wrist-Worn Inertial Sensor.

Authors:  Shaghayegh Zihajehzadeh; Edward J Park
Journal:  PLoS One       Date:  2016-10-20       Impact factor: 3.240

3.  A Novel Method for Estimating Knee Angle Using Two Leg-Mounted Gyroscopes for Continuous Monitoring with Mobile Health Devices.

Authors:  Eric Allseits; Kyoung Jae Kim; Christopher Bennett; Robert Gailey; Ignacio Gaunaurd; Vibhor Agrawal
Journal:  Sensors (Basel)       Date:  2018-08-22       Impact factor: 3.576

Review 4.  Wearable-Sensor-based Detection and Prediction of Freezing of Gait in Parkinson's Disease: A Review.

Authors:  Scott Pardoel; Jonathan Kofman; Julie Nantel; Edward D Lemaire
Journal:  Sensors (Basel)       Date:  2019-11-24       Impact factor: 3.576

5.  Validation of IMU-based gait event detection during curved walking and turning in older adults and Parkinson's Disease patients.

Authors:  Robbin Romijnders; Elke Warmerdam; Clint Hansen; Julius Welzel; Gerhard Schmidt; Walter Maetzler
Journal:  J Neuroeng Rehabil       Date:  2021-02-06       Impact factor: 4.262

6.  Detection of freezing of gait in Parkinson disease: preliminary results.

Authors:  Christine Azevedo Coste; Benoît Sijobert; Roger Pissard-Gibollet; Maud Pasquier; Bernard Espiau; Christian Geny
Journal:  Sensors (Basel)       Date:  2014-04-15       Impact factor: 3.576

7.  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

8.  What is the Best Configuration of Wearable Sensors to Measure Spatiotemporal Gait Parameters in Children with Cerebral Palsy?

Authors:  Lena Carcreff; Corinna N Gerber; Anisoara Paraschiv-Ionescu; Geraldo De Coulon; Christopher J Newman; Stéphane Armand; Kamiar Aminian
Journal:  Sensors (Basel)       Date:  2018-01-30       Impact factor: 3.576

  8 in total

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