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

Dharmendra Sharma, Pavel Davidson, Philipp Müller (Corresponding Author), Robert Adrien Piche

Research output: Contribution to journalArticleScientificpeer-review

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Abstract

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.
Original languageEnglish
Article number1553
Pages (from-to)1-19
Number of pages19
JournalSensors
Volume21
Issue number4
DOIs
Publication statusPublished - 23 Feb 2021
MoE publication typeA1 Journal article-refereed

Keywords

  • gait analysis
  • ground reaction force
  • ground contact time
  • INS/GPS
  • machine learning
  • deep neural networks

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