ML Anthology

Improving Full-Body Pose Estimation from a Small Sensor Set Using Artificial Neural Networks and a Kalman Filter

Frank J. Wouda, Matteo Giuberti, Giovanni Bellusci, Bert-Jan F. van Beijnum, Peter H. Veltink
AAAI 2019 pp. 10063-10064
doi:10.1609/AAAI.V33I01.330110063 /aaai/2019/wouda2019aaai-improving/

Abstract

Previous research has shown that estimating full-body poses from a minimal sensor set using a trained ANN without explicitly enforcing time coherence has resulted in output pose sequences that occasionally show undesired jitter. To mitigate such effect, we propose to improve the ANN output by combining it with a state prediction using a Kalman Filter. Preliminary results are promising, as the jitter effects are diminished. However, the overall error does not decrease substantially.

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Text

Wouda et al. "Improving Full-Body Pose Estimation from a Small Sensor Set Using Artificial Neural Networks and a Kalman Filter." AAAI Conference on Artificial Intelligence, 2019. doi:10.1609/AAAI.V33I01.330110063

Markdown

[Wouda et al. "Improving Full-Body Pose Estimation from a Small Sensor Set Using Artificial Neural Networks and a Kalman Filter." AAAI Conference on Artificial Intelligence, 2019.](https://mlanthology.org/aaai/2019/wouda2019aaai-improving/) doi:10.1609/AAAI.V33I01.330110063

BibTeX

@inproceedings{wouda2019aaai-improving,
  title     = {{Improving Full-Body Pose Estimation from a Small Sensor Set Using Artificial Neural Networks and a Kalman Filter}},
  author    = {Wouda, Frank J. and Giuberti, Matteo and Bellusci, Giovanni and van Beijnum, Bert-Jan F. and Veltink, Peter H.},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {2019},
  pages     = {10063-10064},
  doi       = {10.1609/AAAI.V33I01.330110063},
  url       = {https://mlanthology.org/aaai/2019/wouda2019aaai-improving/}
}