Abstract
Typical optical marker and camera based systems for motion capture suffer from several limitations. They are restricted to indoor environments, have difficulties tracking multiple people simultaneously and require expensive camera setups. In this work, we present a new method for lower-body posture estimation with a wireless smart insole using end-to-end training of a deep neural network. Our model is able to predict the movement of the entire lower body (including the hip, knee, ankle and toe) accurately in a wide range of activities. Inference only takes 1.62ms and hence can be used in real-time. The proposed method can potentially provide a very efficient and portable solution for applications like sports analysis, rehabilitation and virtual reality.
| Original language | English (US) |
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| Title of host publication | 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2019 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 3348-3351 |
| Number of pages | 4 |
| ISBN (Electronic) | 9781538613115 |
| DOIs | |
| State | Published - Jul 2019 |
| Event | 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2019 - Berlin, Germany Duration: Jul 23 2019 → Jul 27 2019 |
Publication series
| Name | Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS |
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| ISSN (Print) | 1557-170X |
Conference
| Conference | 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2019 |
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| Country/Territory | Germany |
| City | Berlin |
| Period | 7/23/19 → 7/27/19 |
Bibliographical note
Publisher Copyright:© 2019 IEEE.