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A Machine Learning Strategy for Locomotion Classification and Parameter Estimation Using Fusion of Wearable Sensors

Camargo, Jonathan and Flanagan, Will and Csomay-Shanklin, Noel and Kanwar, Bharat and Young, Aaron (2021) A Machine Learning Strategy for Locomotion Classification and Parameter Estimation Using Fusion of Wearable Sensors. IEEE Transactions on Biomedical Engineering, 68 (5). pp. 1569-1578. ISSN 0018-9294. doi:10.1109/TBME.2021.3065809. https://resolver.caltech.edu/CaltechAUTHORS:20210420-095316623

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Abstract

The accurate classification of ambulation modes and estimation of walking parameters is a challenging problem that is key to many applications. Knowledge of the user's state can enable rehabilitative devices to adapt to changing conditions, while in a clinical setting it can provide physicians with more detailed patient activity information. This study describes the development and optimization process of a combined locomotion mode classifier and environmental parameter estimator using machine learning and wearable sensors. A detailed analysis of the best sensor types and placements for each problem is also presented to provide device designers with information on which sensors to prioritize for their application. For this study, 15 able-bodied subjects were unilaterally instrumented with inertial measurement unit, goniometer, and electromyography sensors and data were collected for extensive ranges of level-ground, ramp, and stair walking conditions. The proposed system classifies steady state ambulation modes with 99% accuracy and ambulation mode transitions with 96% accuracy, along with estimating ramp incline within 1.25 degrees, stair height within 1.29 centimeters, and walking speed within 0.04 meters per second. Mechanical sensors (inertial measurement units, goniometers) are found to be most important for classification, while goniometers dominate ramp incline and stair height estimation, and speed estimation is performed largely with a single inertial measurement unit. The feature tables and Matlab code to replicate the study are published as supplemental materials.


Item Type:Article
Related URLs:
URLURL TypeDescription
https://doi.org/10.1109/tbme.2021.3065809DOIArticle
ORCID:
AuthorORCID
Camargo, Jonathan0000-0001-5219-2110
Flanagan, Will0000-0001-5973-1324
Csomay-Shanklin, Noel0000-0002-2361-1694
Kanwar, Bharat0000-0002-6362-7017
Young, Aaron0000-0002-5376-2258
Additional Information:© 2021 IEEE. Manuscript received May 4, 2020; revised September 10, 2020 and February 2, 2021; accepted March 5, 2021. Date of publication March 12, 2021; date of current version April 21, 2021. This work was supported by a Fulbright fellowship awarded to Jonathan Camargo-Leyva. (Jonathan Camargo and Will Flanagan contributed equally to this work.)
Funders:
Funding AgencyGrant Number
Fulbright FoundationUNSPECIFIED
Subject Keywords:Activity monitoring, sensor fusion, wearable sensors, intent recognition
Issue or Number:5
DOI:10.1109/TBME.2021.3065809
Record Number:CaltechAUTHORS:20210420-095316623
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20210420-095316623
Official Citation:J. Camargo, W. Flanagan, N. Csomay-Shanklin, B. Kanwar and A. Young, "A Machine Learning Strategy for Locomotion Classification and Parameter Estimation Using Fusion of Wearable Sensors," in IEEE Transactions on Biomedical Engineering, vol. 68, no. 5, pp. 1569-1578, May 2021, doi: 10.1109/TBME.2021.3065809
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:108772
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:23 Apr 2021 18:08
Last Modified:30 Apr 2021 18:33

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