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Open AccessArticle

Indirect Estimation of Vertical Ground Reaction Force from a Body-Mounted INS/GPS Using Machine Learning

1
VTT Technical Research Centre of Finland, Kaitoväylä 1, 90570 Oulu, Finland
2
Faculty of Information Technology and Communication Sciences, Tampere University, 33720 Tampere, Finland
*
Author to whom correspondence should be addressed.
Current address: Huawei Technologies Research & Development (UK) Ltd., Edinburgh EH9 3BF, UK.
Academic Editor: Chris Rizos
Sensors 2021, 21(4), 1553; https://doi.org/10.3390/s21041553
Received: 18 December 2020 / Revised: 5 February 2021 / Accepted: 17 February 2021 / Published: 23 February 2021
(This article belongs to the Special Issue Sensor-Based Information for Personalized Exercise and Training)
Vertical ground reaction force (vGRF) can be measured by force plates or instrumented treadmills, but their application is limited to indoor environments. Insoles remove this restriction but suffer from low durability (several hundred hours). Therefore, interest in the indirect estimation of vGRF using inertial measurement units and machine learning techniques has increased. This paper presents a methodology for indirectly estimating vGRF and other features used in gait analysis from measurements of a wearable GPS-aided inertial navigation system (INS/GPS) device. A set of 27 features was extracted from the INS/GPS data. Feature analysis showed that six of these features suffice to provide precise estimates of 11 different gait parameters. Bagged ensembles of regression trees were then trained and used for predicting gait parameters for a dataset from the test subject from whom the training data were collected and for a dataset from a subject for whom no training data were available. The prediction accuracies for the latter were significantly worse than for the first subject but still sufficiently good. K-nearest neighbor (KNN) and long short-term memory (LSTM) neural networks were then used for predicting vGRF and ground contact times. The KNN yielded a lower normalized root mean square error than the neural network for vGRF predictions but cannot detect new patterns in force curves. View Full-Text
Keywords: gait analysis; ground reaction force; ground contact time; INS/GPS; machine learning; deep neural network gait analysis; ground reaction force; ground contact time; INS/GPS; machine learning; deep neural network
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MDPI and ACS Style

Sharma, D.; Davidson, P.; Müller, P.; Piché, R. Indirect Estimation of Vertical Ground Reaction Force from a Body-Mounted INS/GPS Using Machine Learning. Sensors 2021, 21, 1553. https://doi.org/10.3390/s21041553

AMA Style

Sharma D, Davidson P, Müller P, Piché R. Indirect Estimation of Vertical Ground Reaction Force from a Body-Mounted INS/GPS Using Machine Learning. Sensors. 2021; 21(4):1553. https://doi.org/10.3390/s21041553

Chicago/Turabian Style

Sharma, Dharmendra; Davidson, Pavel; Müller, Philipp; Piché, Robert. 2021. "Indirect Estimation of Vertical Ground Reaction Force from a Body-Mounted INS/GPS Using Machine Learning" Sensors 21, no. 4: 1553. https://doi.org/10.3390/s21041553

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