State Observability through Prior Knowledge: Tracking Track Cyclers with Inertial Sensors

State Observability through Prior Knowledge: A Conceptional Paradigm in Inertial Sensing

Proceedings of the 16th International Conference on Informatics in Control, Automation and Robotics ◽

10.5220/0007952307810788 ◽

2019 ◽

Cited By ~ 1

Author(s):

Tom Koller ◽

Tim Laue ◽

Udo Frese

Keyword(s):

Prior Knowledge ◽

Inertial Sensing ◽

State Observability

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Locomotion Prediction for Lower Limb Prostheses in Complex Environments via sEMG and Inertial Sensors

Complexity ◽

10.1155/2020/8810663 ◽

2020 ◽

Vol 2020 ◽

pp. 1-12

Author(s):

Fang Peng ◽

Cheng Zhang ◽

Bugong Xu ◽

Jiehao Li ◽

Zhen Wang ◽

...

Keyword(s):

Steady State ◽

Prior Knowledge ◽

Lower Limb ◽

Inertial Sensors ◽

Transient State ◽

State Recognition ◽

Complex Operation ◽

Initial Stage ◽

Lower Limb Prosthesis ◽

Limb Prosthesis

Previous studies have shown that the motion intention recognition for lower limb prosthesis mainly focused on the identification of performed gait. However, the bionic prosthesis needs to know the next movement at the beginning of a new gait, especially in complex operation environments. In this paper, an upcoming locomotion prediction scheme via multilevel classifier fusion was proposed for the complex operation. At first, two motion states, including steady state and transient state, were defined. Steady-state recognition was backtracking of a completed gait, which would be used as prior knowledge of motion prediction. In steady-state recognition, surface electromyographic (sEMG) and inertial sensors were fused to improve recognition accuracy; five typical locomotion modes were recognized by random forest classifier with over 97.8% accuracy. The transient state was defined as an observation period at the initial stage of upcoming movement, in which only the sEMG signal was recorded due to the limitation of sliding window length. LightGBM classifier was validated to outperform other methods in the accuracy and prediction time of transient-state recognition. Finally, a simplified HMM model based on prior knowledge and observation result was constructed to predict upcoming locomotion. The results indicated that the locomotion prediction was over 91% accuracy. The proposed scheme implements the locomotion prediction at the initial stage of each gait and provides critical information for the gait control of lower limb prosthesis.

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State Observability through Prior Knowledge: Analysis of the Height Map Prior for Track Cycling

Sensors ◽

10.3390/s20092438 ◽

2020 ◽

Vol 20 (9) ◽

pp. 2438 ◽

Cited By ~ 2

Author(s):

Tom L. Koller ◽

Udo Frese

Keyword(s):

Prior Knowledge ◽

Pose Estimation ◽

Rank Test ◽

Navigation Systems ◽

Inertial Navigation Systems ◽

Knowledge Analysis ◽

Race Track ◽

Position And Orientation ◽

State Observability ◽

Track Cycling

Inertial navigation systems suffer from unbounded errors in the position and orientation estimates. This drift can be corrected by applying prior knowledge, instead of using exteroceptive sensors. We want to show that the use of prior knowledge can yield full observability of the position and orientation. A previous study showed that track cyclers can be tracked drift-free with an IMU as the only sensor and the knowledge that the bike drives on the track. In this paper, we analyze the observability of the pose in the experiment we conducted. Furthermore, we improve the pose estimation of the previous study. The observability is analyzed by testing the weak observability criterion with a Jacobian rank test. The improved estimator is presented and evaluated on a dataset with three 60-round trials (10 km each). The average RMS is 1.08 m and the estimate is drift-free. The observability analysis reveals that the system can gain complete observability in the curves and observability of the orientation on the straight parts of the race track.

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