2021

Sensors

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

Dharmendra Sharma, Pavel Davidson, Philipp Müller, Robert Piché

VTT Technical Research Centre of Finland, Kaitoväylä

Schlagwörter

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

Zusammenfassung

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.

Moticon's Schlussfolgerungen

This study aimed to estimate vertical ground reaction forces based on n inertial navigation system combined with GPS and machine learning techniques. The performance of the estimation method was validated against data derived from Moticon sensor insoles. Data collection was performed on an even track and included walking as well as running tests with the data logger attached to the subject’s' chest. Various features commonly used in walking and running analysis like step length and ground contact time were considered for data processing. The method used for estimating vertical ground reaction forces showed promising results and revealed several approaches for improvements.

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