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This paper introduces a new invariant extended Kalman filter design that produces real-time state estimates and rapid error convergence for the estimation of the human body movement even in the presence of sensor misalignment and initial state estimation errors. The filter fuses the data returned by an inertial measurement unit (IMU) attached to the body (e.g., pelvis or chest) and a virtual measurement of zero stance-foot velocity (i.e., leg odometry). The key novelty of the proposed filter lies in that its process model meets the group affine property while the filter explicitly addresses the IMU placement error by formulating its stochastic process model as Brownian motions and incorporating the error in the leg odometry. Although the measurement model is imperfect (i.e., it does not possess an invariant observation form) and thus its linearization relies on the state estimate, experimental results demonstrate fast convergence of the proposed filter (within 0.2 seconds) during squatting motions even under significant IMU placement inaccuracy and initial estimation errors.
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Applicability Study of the PRIMAD Model to LIGO Gravitational Wave Search Workflows
The PRIMAD model with its six components (i.e., Platform, Research Objective, Implementation, Methods, Actors, and Data) provides an abstract taxonomy to represent computational experiments and promote reproducibility by design. In this paper, we employ a post-hoc assessment of the model applicability to a set of Laser Interferometer Gravitational-Wave Observatory (LIGO) workflows from literature sources (i.e., published papers). Our work outlines potential advantages and limitations of the model in terms of its levels of abstraction and means of application.
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- PAR ID:
- 10155423
- Date Published:
- Journal Name:
- 2nd International Workshop on Practical Reproducible Evaluation of Computer Systems (P-RECS'19)
- Page Range / eLocation ID:
- 1 to 6
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3322790.3330591
