Synchronisation of wearable inertial measurement units based on magnetometer data.

event-based synchronisation inertial measurement unit wireless imu synchronisation wireless sensor networks

Journal

Biomedizinische Technik. Biomedical engineering
ISSN: 1862-278X
Titre abrégé: Biomed Tech (Berl)
Pays: Germany
ID NLM: 1262533

Informations de publication

Date de publication:
27 Jun 2023
Historique:
received: 06 10 2021
accepted: 27 12 2022
medline: 8 6 2023
pubmed: 21 1 2023
entrez: 20 1 2023
Statut: epublish

Résumé

Synchronisation of wireless inertial measurement units in human movement analysis is often achieved using event-based synchronisation techniques. However, these techniques lack precise event generation and accuracy. An inaccurate synchronisation could lead to large errors in motion estimation and reconstruction and therefore wrong analysis outputs. We propose a novel event-based synchronisation technique based on a magnetic field, which allows sub-sample accuracy. A setup featuring Shimmer3 inertial measurement units is designed to test the approach. The proposed technique shows to be able to synchronise with a maximum offset of below 2.6 ms with sensors measuring at 100 Hz. The investigated parameters suggest a required synchronisation time of 8 s. The results indicate a reliable event generation and detection for synchronisation of wireless inertial measurement units. Further research should investigate the temperature changes that the sensors are exposed to during human motion analysis and their influence on the internal time measurement of the sensors. In addition, the approach should be tested using inertial measurement units from different manufacturers to investigate an identified constant offset in the accuracy measurements.

Identifiants

pubmed: 36668676
pii: bmt-2021-0329
doi: 10.1515/bmt-2021-0329
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

263-273

Informations de copyright

© 2022 Walter de Gruyter GmbH, Berlin/Boston.

Références

Weygers, I, Kok, M, Konings, M, Hallez, H, Vroey, H, Claeys, K. Inertial sensor-based lower limb joint kinematics: a methodological systematic review. Sensors 2020;20:673. https://doi.org/10.3390/s20030673 .
doi: 10.3390/s20030673
Vienne-Jumeau, A, Quijoux, F, Vidal, PP, Ricard, D. Wearable inertial sensors provide reliable biomarkers of disease severity in multiple sclerosis: a systematic review and meta-analysis. Ann Phys Rehabil Med 2020;63:138–47. https://doi.org/10.1016/j.rehab.2019.07.004 .
doi: 10.1016/j.rehab.2019.07.004
Poitras, I, Dupuis, F, Bielmann, M, Campeau-Lecours, A, Mercier, C, Bouyer, LJ, et al.. Validity and reliability of wearable sensors for joint angle estimation: a systematic review. Sensors 2019;19:1555. https://doi.org/10.3390/s19071555 .
doi: 10.3390/s19071555
O’Reilly, M, Caulfield, B, Ward, T, Johnston, W, Doherty, C. Wearable inertial sensor systems for lower limb exercise detection and evaluation: a systematic review. Sports Med 2018;48:1221–46. https://doi.org/10.1007/s40279-018-0878-4 .
doi: 10.1007/s40279-018-0878-4
Ghislieri, M, Gastaldi, L, Pastorelli, S, Tadano, S, Agostini, V. Wearable inertial sensors to assess standing balance: a systematic review. Sensors 2019;19:4075. https://doi.org/10.3390/s19194075 .
doi: 10.3390/s19194075
Tirado-Andrés, F, Araujo, A. Performance of clock sources and their influence on time synchronization in wireless sensor networks. Int J Distributed Sens Netw 2019;15. https://doi.org/10.1177/1550147719879372 .
doi: 10.1177/1550147719879372
Elson, J, Girod, L, Estrin, D. Fine-grained network time synchronization using reference broadcasts. ACM SIGOPS – Oper Syst Rev 2002;36:147–63. https://doi.org/10.1145/844128.844143 .
doi: 10.1145/844128.844143
Ganeriwal, S, Kumar, R, Srivastava, MB. Timing-sync protocol for sensor networks. In: Proc first int conf embed networked sens syst (SenSys03) . Los Angeles, USA: ACM; 2003.
Maróti, M, Kusy, B, Simon, G, Lédeczi, Á. The flooding time synchronization protocol. In: Proc 2nd int conf embed networked sens syst – sensys ‘04 ; 2004:39–49 pp.
Zhou, L, Fischer, E, Tunca, C, Brahms, CM, Ersoy, C, Granacher, U, et al.. How we found our IMU: guidelines to IMU selection and a comparison of seven IMUs for pervasive healthcare applications. Sensors 2020;20:4090. https://doi.org/10.3390/s20154090 .
doi: 10.3390/s20154090
Sivrikaya, F, Yener, B. Time synchronization in sensor networks: a survey. IEEE Network 2004;18:45–50. https://doi.org/10.1109/mnet.2004.1316761 .
doi: 10.1109/mnet.2004.1316761
Ringwald, M, Romer, K. Practical time synchronization for bluetooth scatternets. In: 2007 int conf broadband commun networks syst broadnets ‘07 ; 2007:337–45 pp.
Bannach, D, Amft, O, Lukowicz, P. Automatic event-based synchronization of multimodal data streams from wearable and ambient sensors. In: Smart sensing and context. 4th European conference, EuroSSC 2009. Guildford, UK, Sep 16–18, 2009.
Gao, Y, Long, Y, Guan, Y, Basu, A, Baggaley, J, Ploetz, T. Towards reliable, automated general movement assessment for perinatal stroke screening in infants using wearable accelerometers. Proc ACM Interact Mob Wearable Ubiquitous Technol 2019;3:1–22. https://doi.org/10.1145/3314399 .
doi: 10.1145/3314399
Rietveld, T, Vegter, RJK, van der Slikke, RMA, Hoekstra, AE, van der Woude, LHV, Groot, S. Wheelchair mobility performance of elite wheelchair tennis players during four field tests: inter-trial reliability and construct validity. PLoS One 2019;14:e0217514. https://doi.org/10.1371/journal.pone.0217514 .
doi: 10.1371/journal.pone.0217514
Paraschiakos, S, Cachucho, R, Moed, M, van Heemst, D, Mooijaart, S, Slagboom, EP, et al.. Activity recognition using wearable sensors for tracking the elderly. User Model User Adapt 2020;30:567–605. https://doi.org/10.1007/s11257-020-09268-2 .
doi: 10.1007/s11257-020-09268-2
Witchel, HJ, Oberndorfer, C, Needham, R, Healy, A, Klucken, J. Thigh-derived inertial sensor metrics to assess the sit-to-stand and stand-to-sit transitions in the timed up and go (TUG) task for quantifying mobility impairment in multiple sclerosis. Front Neurol 2018;9. https://doi.org/10.3389/fneur.2018.00684 .
doi: 10.3389/fneur.2018.00684
Kim, S, Nussbaum, MA. Performance evaluation of a wearable inertial motion capture system for capturing physical exposures during manual material handling tasks. Ergonomics 2013;56:314–26. https://doi.org/10.1080/00140139.2012.742932 .
doi: 10.1080/00140139.2012.742932
Chen, S, Brantley, JS, Kim, T, Ridenour, SA, Lach, J. Characterising and minimising sources of error in inertial body sensor networks. Int J Autonom Adapt Commun Syst 2013;6:253. https://doi.org/10.1504/ijaacs.2013.054828 .
doi: 10.1504/ijaacs.2013.054828
Configuring Systems for High Accuracy . Microsoft docs . Microsoft; 2018. Available from: https://docs.microsoft.com/en-us/windows-server/networking/windows-time-service/configuring-systems-for-high-accuracy [Accessed 17 Mar 2021].

Auteurs

Andreas Spilz (A)

Department of Mechatronics and Medical Engineering, Biomechatronics Research Group, University of Applied Sciences, Ulm, Germany.

Michael Munz (M)

Department of Mechatronics and Medical Engineering, Biomechatronics Research Group, University of Applied Sciences, Ulm, Germany.

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