Name
Title | ||
|---|---|---|
Time Coherent Full-Body Poses Estimated Using Only Five Inertial Sensors: Deep versus Shallow Learning. |
Abstract | ||
|---|---|---|
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 (similar to 6 cm) to that of a deep learning approach (similar to 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). |
Year | DOI | Venue |
|---|---|---|
2019 | 10.3390/s19173716 | SENSORS |
Keywords | Field | DocType |
inertial motion capture,machine learning,neural networks,deep learning,LSTM,time coherence,human movement,reduced sensor set,pose estimation | Motion capture,Jerk,Algorithm,Electronic engineering,Pose,Rigid body,Acceleration,Artificial intelligence,Inertial measurement unit,Engineering,Deep learning,Artificial neural network | Journal |
Volume | Issue | ISSN |
19 | 17.0 | 1424-8220 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Frank J. Wouda | 1 | 0 | 1.35 |
| Matteo Giuberti | 2 | 38 | 6.35 |
| Nina Rudigkeit | 3 | 0 | 0.34 |
| Bert-Jan F van Beijnum | 4 | 45 | 3.37 |
| Mannes Poel | 5 | 4 | 0.78 |
| Peter H. Veltink | 6 | 291 | 42.38 |