In this paper we present swimming and modeling for Trident, a three-link lamprey-inspired robot that is able to climb on flat smooth walls. We explore two gaits proposed to work for linear swimming, and three gaits for turning maneuvers. We compare the experimental results obtained from these swimming experiments with two different reduced order fluid interaction models, one a previously published potential flow model, and the other a slender cylinder model we developed. We find that depending on the the parameters of swimming chosen, we are able to move forward, backward and sideways with a peak speed of 2.5 cm/s. We identify the conditions when these models apply and aspects that will require additional complexity.
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Gait Recognition Based on Tensor Analysis of Acceleration Data from Wearable Sensors
We use commercial wearable sensors to collect three-dimensional acceleration signals from various gaits. Then, we organize the collected measurements in three-way tensors and present a simple, efficient gait classification scheme based on TUCKER2 tensor decomposition. The proposed scheme derives as multi-linear generalization of the nearest-subspace classifier. Our experimental studies show that the proposed approach manages to automatically identify the motion axes of interest and classify walking, jogging, and running gaits with high accuracy.
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- Award ID(s):
- 1808582
- NSF-PAR ID:
- 10110734
- Date Published:
- Journal Name:
- IEEE Western New York Signal and Image Processing Workshop (IEEE WNYISPW 2018)
- Page Range / eLocation ID:
- 1 to 5
- 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/10.1109/WNYIPW.2018.8576383
