Orientation estimation for instrumented helmet using neural networks
This work presents an integrated solution for head orientation estimation, which is a critical component for applications of virtual and augmented reality systems. The proposed solution builds upon the measurements from the inertial sensors and magnetometer added to an instrumented helmet, and an or...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
SAGE Publishing
2023-09-01
|
| Series: | Measurement + Control |
| Online Access: | https://doi.org/10.1177/00202940221149062 |
| _version_ | 1797731242908581888 |
|---|---|
| author | Muhammad Hamad Zaheer Se Young Yoon Brian K Higginson |
| author_facet | Muhammad Hamad Zaheer Se Young Yoon Brian K Higginson |
| author_sort | Muhammad Hamad Zaheer |
| collection | DOAJ |
| description | This work presents an integrated solution for head orientation estimation, which is a critical component for applications of virtual and augmented reality systems. The proposed solution builds upon the measurements from the inertial sensors and magnetometer added to an instrumented helmet, and an orientation estimation algorithm is developed to mitigate the effect of bias introduced by noise in the gyroscope signal. Convolutional Neural Network (CNN) techniques are introduced to develop a dynamic orientation estimation algorithm with a structure motivated by complementary filters and trained on data collected to represent a wide range of head motion profiles. The proposed orientation estimation method is evaluated experimentally and compared to both learning and non-learning-based orientation estimation algorithms found in the literature for comparable applications. Test results support the advantage of the proposed CNN-based solution, particularly for motion profiles with high acceleration disturbance that are characteristic of head motion. |
| first_indexed | 2024-03-12T11:56:00Z |
| format | Article |
| id | doaj.art-acb1b832d3d145c49b6e041ce91fb67b |
| institution | Directory Open Access Journal |
| issn | 0020-2940 |
| language | English |
| last_indexed | 2024-03-12T11:56:00Z |
| publishDate | 2023-09-01 |
| publisher | SAGE Publishing |
| record_format | Article |
| series | Measurement + Control |
| spelling | doaj.art-acb1b832d3d145c49b6e041ce91fb67b2023-08-30T21:35:55ZengSAGE PublishingMeasurement + Control0020-29402023-09-015610.1177/00202940221149062Orientation estimation for instrumented helmet using neural networksMuhammad Hamad Zaheer0Se Young Yoon1Brian K Higginson2Department of Electrical and Computer Eng., University of New Hampshire, Durham, NH, USADepartment of Electrical and Computer Eng., University of New Hampshire, Durham, NH, USAWarfighter Systems Integration Lab, Galvion Ltd, Portsmouth, NH, USAThis work presents an integrated solution for head orientation estimation, which is a critical component for applications of virtual and augmented reality systems. The proposed solution builds upon the measurements from the inertial sensors and magnetometer added to an instrumented helmet, and an orientation estimation algorithm is developed to mitigate the effect of bias introduced by noise in the gyroscope signal. Convolutional Neural Network (CNN) techniques are introduced to develop a dynamic orientation estimation algorithm with a structure motivated by complementary filters and trained on data collected to represent a wide range of head motion profiles. The proposed orientation estimation method is evaluated experimentally and compared to both learning and non-learning-based orientation estimation algorithms found in the literature for comparable applications. Test results support the advantage of the proposed CNN-based solution, particularly for motion profiles with high acceleration disturbance that are characteristic of head motion.https://doi.org/10.1177/00202940221149062 |
| spellingShingle | Muhammad Hamad Zaheer Se Young Yoon Brian K Higginson Orientation estimation for instrumented helmet using neural networks Measurement + Control |
| title | Orientation estimation for instrumented helmet using neural networks |
| title_full | Orientation estimation for instrumented helmet using neural networks |
| title_fullStr | Orientation estimation for instrumented helmet using neural networks |
| title_full_unstemmed | Orientation estimation for instrumented helmet using neural networks |
| title_short | Orientation estimation for instrumented helmet using neural networks |
| title_sort | orientation estimation for instrumented helmet using neural networks |
| url | https://doi.org/10.1177/00202940221149062 |
| work_keys_str_mv | AT muhammadhamadzaheer orientationestimationforinstrumentedhelmetusingneuralnetworks AT seyoungyoon orientationestimationforinstrumentedhelmetusingneuralnetworks AT briankhigginson orientationestimationforinstrumentedhelmetusingneuralnetworks |