Optimizing the Sensor Placement for Foot Plantar Center of Pressure without Prior Knowledge Using De...
Optimizing the Sensor Placement for Foot Plantar Center of Pressure without Prior Knowledge Using Deep Reinforcement Learning
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Basel: MDPI AG
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Language
English
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Basel: MDPI AG
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We study the foot plantar sensor placement by a deep reinforcement learning algorithm without using any prior knowledge of the foot anatomical area. To apply a reinforcement learning algorithm, we propose a sensor placement environment and reward system that aims to optimize fitting the center of pressure (COP) trajectory during the self-selected s...
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Optimizing the Sensor Placement for Foot Plantar Center of Pressure without Prior Knowledge Using Deep Reinforcement Learning
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TN_cdi_doaj_primary_oai_doaj_org_article_284d2214a16c4725ab771a4787ad2e76
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https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_doaj_primary_oai_doaj_org_article_284d2214a16c4725ab771a4787ad2e76
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ISSN
1424-8220
E-ISSN
1424-8220
DOI
10.3390/s20195588
