Journal Article

Time Coherent Full-Body Poses Estimated Using Only Five Inertial Sensors: Deep versus Shallow Learning

Veltink, Peter H., Rudigkeit, Nina, van Beijnum, Bert-Jan F., Poel, Mannes, Wouda, Frank J., Giuberti, Matteo

2019

Overview

Full-body motion capture typically requires sensors/markers to be placed on each rigid body segment, which results in long setup times and is obtrusive. The number of sensors/markers can be reduced using deep learning or offline methods. However, this requires large training datasets and/or sufficient computational resources. Therefore, we investigate the following research question: “What is the performance of a shallow approach, compared to a deep learning one, for estimating time coherent full-body poses using only five inertial sensors?”. We propose to incorporate past/future inertial sensor information into a stacked input vector, which is fed to a shallow neural network for estimating full-body poses. Shallow and deep learning approaches are compared using the same input vector configurations. Additionally, the inclusion of acceleration input is evaluated. The results show that a shallow learning approach can estimate full-body poses with a similar accuracy (~6 cm) to that of a deep learning approach (~7 cm). However, the jerk errors are smaller using the deep learning approach, which can be the effect of explicit recurrent modelling. Furthermore, it is shown that the delay using a shallow learning approach (72 ms) is smaller than that of a deep learning approach (117 ms).

• Publisher: MDPI AG,MDPI
• Subject: Acceleration / Accelerometers / Accuracy / Algorithms / Artificial intelligence / Augmented reality / Biomechanics / Biosensing Techniques / Computer science / Deep learning / Gait - physiology / Human Body / human movement / Humans / inertial motion capture / LSTM / Machine Learning / Monitoring, Physiologic - methods / Motion capture / Movement - physiology / Neural networks / Neural Networks, Computer / Pattern recognition / pose estimation / Posture - physiology / reduced sensor set / Sensors / time coherence / Virtual reality