TY - JOUR AU - Wouda, Frank J. AU - Giuberti, Matteo AU - Bellusci, Giovanni AU - van Beijnum, Bert-Jan F. AU - Veltink, Peter H. PY - 2019/07/17 Y2 - 2024/03/29 TI - Improving Full-Body Pose Estimation from a Small Sensor Set Using Artificial Neural Networks and a Kalman Filter JF - Proceedings of the AAAI Conference on Artificial Intelligence JA - AAAI VL - 33 IS - 01 SE - Student Abstract Track DO - 10.1609/aaai.v33i01.330110063 UR - https://ojs.aaai.org/index.php/AAAI/article/view/5168 SP - 10063-10064 AB - <p>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.</p> ER -