Smartphone orientation estimation algorithm combining Kalman filter with gradient descent
Availability and all-in-one functionality of smartphones have become a multipurpose personal tool to improve our daily life. Recent advancements in hardware and accessibility of smartphones have spawn huge potential for assistive healthcare, in particular telerehabilitation. However, using smartphon...
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sg-ntu-dr.10356-1400002020-05-26T03:31:43Z Smartphone orientation estimation algorithm combining Kalman filter with gradient descent Yean, Seanglidet Lee, Bu Sung Yeo, Chai Kiat Vun, Chan Hua Oh, Hong Lye School of Computer Science and Engineering Engineering::Computer science and engineering Algorithm Sensor Fusion Availability and all-in-one functionality of smartphones have become a multipurpose personal tool to improve our daily life. Recent advancements in hardware and accessibility of smartphones have spawn huge potential for assistive healthcare, in particular telerehabilitation. However, using smartphone sensors face certain challenges, in particular, accurate orientation estimation, which is usually less of a problem in specialized motion tracking sensor devices. Drift is one of the challenges. We first propose a simple feedback loop complementary filter (CFF) to reduce the error caused by the integration of the gyroscope's data in the orientation estimation. Next, we propose a new and better orientation estimation algorithm which combines quaternion-based kalman filter with corrector estimates using gradient descent (KFGD). We then evaluate CFF's and KFGD's performance on two early-stage rehabilitation exercises. The results show that CFF is capable of fast motion tracking and confirm that the feedback loop can correct the error caused by the integration of gyroscope data. The KFGD orientation estimation is comparable to XSENS Awinda and has shown itself to be stable than and outperforms CFF. KFGD also outperforms the prominent Madgwick algorithm using mobile data. Thus, KFGD is suitable for low-cost motion sensors or mobile inertial sensors, especially during early recovery stage of sport injuries and exercise for the elderly. 2020-05-26T03:31:43Z 2020-05-26T03:31:43Z 2017 Journal Article Yean, S., Lee, B. S., Yeo, C. K., Vun, C. H., & Oh, H. L. (2018). Smartphone orientation estimation algorithm combining Kalman filter with gradient descent. IEEE Journal of Biomedical and Health Informatics, 22(5), 1421-1433. doi:10.1109/JBHI.2017.2780879 2168-2194 https://hdl.handle.net/10356/140000 10.1109/JBHI.2017.2780879 29990245 2-s2.0-85037597642 5 22 1421 1433 en IEEE Journal of Biomedical and Health Informatics © 2017 IEEE. All rights reserved. |
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Engineering::Computer science and engineering Algorithm Sensor Fusion Yean, Seanglidet Lee, Bu Sung Yeo, Chai Kiat Vun, Chan Hua Oh, Hong Lye Smartphone orientation estimation algorithm combining Kalman filter with gradient descent |
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Availability and all-in-one functionality of smartphones have become a multipurpose personal tool to improve our daily life. Recent advancements in hardware and accessibility of smartphones have spawn huge potential for assistive healthcare, in particular telerehabilitation. However, using smartphone sensors face certain challenges, in particular, accurate orientation estimation, which is usually less of a problem in specialized motion tracking sensor devices. Drift is one of the challenges. We first propose a simple feedback loop complementary filter (CFF) to reduce the error caused by the integration of the gyroscope's data in the orientation estimation. Next, we propose a new and better orientation estimation algorithm which combines quaternion-based kalman filter with corrector estimates using gradient descent (KFGD). We then evaluate CFF's and KFGD's performance on two early-stage rehabilitation exercises. The results show that CFF is capable of fast motion tracking and confirm that the feedback loop can correct the error caused by the integration of gyroscope data. The KFGD orientation estimation is comparable to XSENS Awinda and has shown itself to be stable than and outperforms CFF. KFGD also outperforms the prominent Madgwick algorithm using mobile data. Thus, KFGD is suitable for low-cost motion sensors or mobile inertial sensors, especially during early recovery stage of sport injuries and exercise for the elderly. |
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School of Computer Science and Engineering |
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School of Computer Science and Engineering Yean, Seanglidet Lee, Bu Sung Yeo, Chai Kiat Vun, Chan Hua Oh, Hong Lye |
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Article |
author |
Yean, Seanglidet Lee, Bu Sung Yeo, Chai Kiat Vun, Chan Hua Oh, Hong Lye |
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Yean, Seanglidet |
title |
Smartphone orientation estimation algorithm combining Kalman filter with gradient descent |
title_short |
Smartphone orientation estimation algorithm combining Kalman filter with gradient descent |
title_full |
Smartphone orientation estimation algorithm combining Kalman filter with gradient descent |
title_fullStr |
Smartphone orientation estimation algorithm combining Kalman filter with gradient descent |
title_full_unstemmed |
Smartphone orientation estimation algorithm combining Kalman filter with gradient descent |
title_sort |
smartphone orientation estimation algorithm combining kalman filter with gradient descent |
publishDate |
2020 |
url |
https://hdl.handle.net/10356/140000 |
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1681057310612389888 |