Literature DB >> 28268416

Validation of temporal gait metrics from three IMU locations to the gold standard force plate.

Matthew R Patterson, William Johnston, Niamh O'Mahony, Sam O'Mahony, Eimear Nolan, Brian Caulfield.   

Abstract

The purpose of this work is to compare temporal gait parameters from three different IMU locations to the gold standard force platform. 33 subjects (12 F, 21 M) performed twenty gait trials each while wearing inertial measurement units (IMUs) on the trunk, both shanks and both feet. Data was simultaneously collected from a laboratory embedded force plate. Step times were derived from the raw IMU data at the three IMU locations using methods that have been shown to be accurate. Step times from all locations were valid compared to the force plate. Foot IMU step time was the most accurate (Pearson = .991, CI width = 3.00e2), the trunk IMU was the next most accurate (Pearson = .974, CI width = 4.85e2) and shank step time was the least accurate (Pearson = .958, CI width = 6.80e2). All three sensing locations result in valid estimations of step time compared to the gold standard force plate. These results suggest that the foot location would be most appropriate for clinical applications where very precise temporal parameter detection is required.

Mesh:

Year:  2016        PMID: 28268416     DOI: 10.1109/EMBC.2016.7590790

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


  4 in total

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Journal:  Br J Sports Med       Date:  2020-12-24       Impact factor: 13.800

2.  Using the VERT wearable device to monitor jumping loads in elite volleyball athletes.

Authors:  Faraz Damji; Kerry MacDonald; Michael A Hunt; Jack Taunton; Alex Scott
Journal:  PLoS One       Date:  2021-01-22       Impact factor: 3.240

3.  Design and Validation of an E-Textile-Based Wearable Sock for Remote Gait and Postural Assessment.

Authors:  Federica Amitrano; Armando Coccia; Carlo Ricciardi; Leandro Donisi; Giuseppe Cesarelli; Edda Maria Capodaglio; Giovanni D'Addio
Journal:  Sensors (Basel)       Date:  2020-11-23       Impact factor: 3.576

4.  Deep Learning in Gait Parameter Prediction for OA and TKA Patients Wearing IMU Sensors.

Authors:  Mohsen Sharifi Renani; Casey A Myers; Rohola Zandie; Mohammad H Mahoor; Bradley S Davidson; Chadd W Clary
Journal:  Sensors (Basel)       Date:  2020-09-28       Impact factor: 3.576

  4 in total

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