Novel magnetometer-free inertial-measurement-unit-based orientation estimation approach for measuring upper limb kinematics
This study addresses challenges in sensor fusion for accurate and robust joint orientation estimation in human movement analysis using wearable inertial measurement units (IMUs). A magnetometer-free refined Kalman filter (KF) approach is presented and validated to address various indoor environmenta...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Cambridge University Press
2025-01-01
|
| Series: | Wearable Technologies |
| Subjects: | |
| Online Access: | https://www.cambridge.org/core/product/identifier/S2631717625100030/type/journal_article |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1839647902105862144 |
|---|---|
| author | Souha Baklouti Taysir Rezgui Abdelbadia Chaker Anis Sahbani Sami Bennour |
| author_facet | Souha Baklouti Taysir Rezgui Abdelbadia Chaker Anis Sahbani Sami Bennour |
| author_sort | Souha Baklouti |
| collection | DOAJ |
| description | This study addresses challenges in sensor fusion for accurate and robust joint orientation estimation in human movement analysis using wearable inertial measurement units (IMUs). A magnetometer-free refined Kalman filter (KF) approach is presented and validated to address various indoor environmental constraints and challenges posed by human movement. These include variability in motion and dynamics, as well as magnetic disturbances caused by ferromagnetic materials or electronic interferences. Our proposed approach utilizes a Kalman-filter-based framework that analyzes the accelerometer’s alignment with the Earth’s frame to estimate orientation and correct gyroscope readings, eliminating reliance on magnetometer inputs. The algorithm was tested on both controlled robotic movements and real-world upper-limb-motion-monitoring scenarios. First, a comparative analysis was conducted on the double-stage Kalman filter (DSKF) and complementary filter using the collected robot motion encoder data. The results demonstrated superior performance in orientation estimation, particularly in yaw measurements, where the proposed method significantly improved accuracy. It achieved a lower root mean square error (RMSE =
$ {2.447}^{\circ } $
) and mean absolute error (MAE =
$ {2.006}^{\circ } $
), outperforming both the DSKF and complementary filter approaches. Additionally, the study’s findings were validated against a standard motion capture system, revealing error metrics within generally acceptable ranges (
$ \le 12.4\% $
of the joint range of motion [ROM]) and strong correlation coefficients (
$ {r}^2>0.89 $
). However, some deviations were observed during complex motion cycle intervals, highlighting opportunities for further refinement. These findings suggest that the proposed approach presents a promising alternative for human joint orientation estimation in industrial settings with magnetic distortions. |
| format | Article |
| id | doaj-art-b64a2ee1209f4a4f8821f1eb1cb054f1 |
| institution | Matheson Library |
| issn | 2631-7176 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Cambridge University Press |
| record_format | Article |
| series | Wearable Technologies |
| spelling | doaj-art-b64a2ee1209f4a4f8821f1eb1cb054f12025-06-30T00:45:25ZengCambridge University PressWearable Technologies2631-71762025-01-01610.1017/wtc.2025.10003Novel magnetometer-free inertial-measurement-unit-based orientation estimation approach for measuring upper limb kinematicsSouha Baklouti0https://orcid.org/0000-0002-1863-7689Taysir Rezgui1Abdelbadia Chaker2Anis Sahbani3Sami Bennour4Mechanical Laboratory of Sousse (LMS), National School of Engineers of Sousse, https://ror.org/00dmpgj58University of Sousse, Sousse, Tunisia ENOVA Robotics S.A., Sousse, TunisiaApplied Mechanics, and Systems Research Laboratory (LASMAP), Tunisia Polytechnic School, https://ror.org/057x6za15University of Carthage, La Marsa, TunisiaMechanical Laboratory of Sousse (LMS), National School of Engineers of Sousse, https://ror.org/00dmpgj58University of Sousse, Sousse, Tunisia Department of GMSC, Pprime Institute CNRS, ENSMA, UPR 3346, University of Poitiers, Poitiers, FranceENOVA Robotics S.A., Sousse, Tunisia https://ror.org/05neq8668Sorbonne University, Paris, FranceMechanical Laboratory of Sousse (LMS), National School of Engineers of Sousse, https://ror.org/00dmpgj58University of Sousse, Sousse, Tunisia National School of Engineers of Monastir, University of Monastir, Monastir, Tunisia Computer Engineering, Production and Maintenance Laboratory (LGIPM), University of Lorraine, Metz, FranceThis study addresses challenges in sensor fusion for accurate and robust joint orientation estimation in human movement analysis using wearable inertial measurement units (IMUs). A magnetometer-free refined Kalman filter (KF) approach is presented and validated to address various indoor environmental constraints and challenges posed by human movement. These include variability in motion and dynamics, as well as magnetic disturbances caused by ferromagnetic materials or electronic interferences. Our proposed approach utilizes a Kalman-filter-based framework that analyzes the accelerometer’s alignment with the Earth’s frame to estimate orientation and correct gyroscope readings, eliminating reliance on magnetometer inputs. The algorithm was tested on both controlled robotic movements and real-world upper-limb-motion-monitoring scenarios. First, a comparative analysis was conducted on the double-stage Kalman filter (DSKF) and complementary filter using the collected robot motion encoder data. The results demonstrated superior performance in orientation estimation, particularly in yaw measurements, where the proposed method significantly improved accuracy. It achieved a lower root mean square error (RMSE = $ {2.447}^{\circ } $ ) and mean absolute error (MAE = $ {2.006}^{\circ } $ ), outperforming both the DSKF and complementary filter approaches. Additionally, the study’s findings were validated against a standard motion capture system, revealing error metrics within generally acceptable ranges ( $ \le 12.4\% $ of the joint range of motion [ROM]) and strong correlation coefficients ( $ {r}^2>0.89 $ ). However, some deviations were observed during complex motion cycle intervals, highlighting opportunities for further refinement. These findings suggest that the proposed approach presents a promising alternative for human joint orientation estimation in industrial settings with magnetic distortions.https://www.cambridge.org/core/product/identifier/S2631717625100030/type/journal_articleinertial measurement unitssensor fusionmagnetometer-free orientation estimationupper limb kinematics |
| spellingShingle | Souha Baklouti Taysir Rezgui Abdelbadia Chaker Anis Sahbani Sami Bennour Novel magnetometer-free inertial-measurement-unit-based orientation estimation approach for measuring upper limb kinematics Wearable Technologies inertial measurement units sensor fusion magnetometer-free orientation estimation upper limb kinematics |
| title | Novel magnetometer-free inertial-measurement-unit-based orientation estimation approach for measuring upper limb kinematics |
| title_full | Novel magnetometer-free inertial-measurement-unit-based orientation estimation approach for measuring upper limb kinematics |
| title_fullStr | Novel magnetometer-free inertial-measurement-unit-based orientation estimation approach for measuring upper limb kinematics |
| title_full_unstemmed | Novel magnetometer-free inertial-measurement-unit-based orientation estimation approach for measuring upper limb kinematics |
| title_short | Novel magnetometer-free inertial-measurement-unit-based orientation estimation approach for measuring upper limb kinematics |
| title_sort | novel magnetometer free inertial measurement unit based orientation estimation approach for measuring upper limb kinematics |
| topic | inertial measurement units sensor fusion magnetometer-free orientation estimation upper limb kinematics |
| url | https://www.cambridge.org/core/product/identifier/S2631717625100030/type/journal_article |
| work_keys_str_mv | AT souhabaklouti novelmagnetometerfreeinertialmeasurementunitbasedorientationestimationapproachformeasuringupperlimbkinematics AT taysirrezgui novelmagnetometerfreeinertialmeasurementunitbasedorientationestimationapproachformeasuringupperlimbkinematics AT abdelbadiachaker novelmagnetometerfreeinertialmeasurementunitbasedorientationestimationapproachformeasuringupperlimbkinematics AT anissahbani novelmagnetometerfreeinertialmeasurementunitbasedorientationestimationapproachformeasuringupperlimbkinematics AT samibennour novelmagnetometerfreeinertialmeasurementunitbasedorientationestimationapproachformeasuringupperlimbkinematics |