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Article

Ablation Analysis to Select Wearable Sensors for Classifying Standing, Walking, and Running

1
Department of Aeronautics and Astronautics, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, MA 02139, USA
2
Lincoln Laboratory, Massachusetts Institute of Technology, 244 Wood Street, Lexington, MA 02421-6426, USA
3
Department of Industrial and Operations Engineering, Robotics Institute, University of Michigan, 1205 Beal Avenue, Ann Arbor, MI 48109, USA
*
Author to whom correspondence should be addressed.
Distribution Statement A. Approved for public release. Distribution is unlimited. This material is based upon work supported by the United States Air Force under Air Force Contract No. FA8702-15-D-0001. Any opinions, findings, conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the United States Air Force.
Sensors 2021, 21(1), 194; https://doi.org/10.3390/s21010194
Received: 11 November 2020 / Revised: 18 December 2020 / Accepted: 19 December 2020 / Published: 30 December 2020
(This article belongs to the Special Issue On the Applications of EMG Sensors and Signals)
The field of human activity recognition (HAR) often utilizes wearable sensors and machine learning techniques in order to identify the actions of the subject. This paper considers the activity recognition of walking and running while using a support vector machine (SVM) that was trained on principal components derived from wearable sensor data. An ablation analysis is performed in order to select the subset of sensors that yield the highest classification accuracy. The paper also compares principal components across trials to inform the similarity of the trials. Five subjects were instructed to perform standing, walking, running, and sprinting on a self-paced treadmill, and the data were recorded while using surface electromyography sensors (sEMGs), inertial measurement units (IMUs), and force plates. When all of the sensors were included, the SVM had over 90% classification accuracy using only the first three principal components of the data with the classes of stand, walk, and run/sprint (combined run and sprint class). It was found that sensors that were placed only on the lower leg produce higher accuracies than sensors placed on the upper leg. There was a small decrease in accuracy when the force plates are ablated, but the difference may not be operationally relevant. Using only accelerometers without sEMGs was shown to decrease the accuracy of the SVM. View Full-Text
Keywords: human activity recognition; surface electromyography; inertial measurement units; feature selection; wearable sensors human activity recognition; surface electromyography; inertial measurement units; feature selection; wearable sensors
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MDPI and ACS Style

Gonzalez, S.; Stegall, P.; Edwards, H.; Stirling, L.; Siu, H.C. Ablation Analysis to Select Wearable Sensors for Classifying Standing, Walking, and Running. Sensors 2021, 21, 194. https://doi.org/10.3390/s21010194

AMA Style

Gonzalez S, Stegall P, Edwards H, Stirling L, Siu HC. Ablation Analysis to Select Wearable Sensors for Classifying Standing, Walking, and Running. Sensors. 2021; 21(1):194. https://doi.org/10.3390/s21010194

Chicago/Turabian Style

Gonzalez, Sarah, Paul Stegall, Harvey Edwards, Leia Stirling, and Ho C. Siu 2021. "Ablation Analysis to Select Wearable Sensors for Classifying Standing, Walking, and Running" Sensors 21, no. 1: 194. https://doi.org/10.3390/s21010194

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