Abstract
In this article, the Kalman filter, Mahony filter and Madgwick filter are implemented to estimate the orientation from inertial data, using an IMU called 9 × 3 of the MoMoPa3 project which contain various sensors including a gyroscope, an accelerometer and a magnetometer, each one of them, equipped with three perpendicular axes, in the magnetometer the measurement was modified to correct the distortions by hard metals, demonstrating improvements in the accuracy of the orientation estimates. In addition, the Kinovea video analyzer software is used as reference and gold standard to calculate the Root-Mean-Square Error (RMSE) with each filter. When comparing the angles estimated by the filters with those obtained from Kinovea, it was observed that one of the filters was better in performance. The information obtained in this article can be involved in several fields of science, one of the most important in the field of medicine, helping to control Parkinson’s disease since it allows to evaluate and recognize when a patient suffers a fall or presents Freezing of the gait (FOG).
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Brynjolfsson, E., Mcafee, A.: Race Against the Machine. Digital Frontier Press, Lexington (2011)
Singh, M., Murthy, V., Ramassamy, C.: Neuroprotective mechanisms of the standardized extract of Bacopa monniera in a paraquat/diquat-mediated acute toxicity. Neurochem. Int. 62, 530–539 (2013). https://doi.org/10.1016/j.neuint.2013.01.030
Bloem, B.R., Hausdorff, J.M., Visser, J.E., Giladi, N.: Falls and freezing of gait in Parkinson’s disease: a review of two interconnected, episodic phenomena. Mov. Disord. 19(8), 871–884 (2004)
Okuma, Y.: Freezing of gait in Parkinson’s disease. J. Neurol. 253(Supplement 7), vii27–vii32 (2006)
European Parkinson’s Disease Association. http://www.epda.eu.com/about-parkinsons/symptoms/motor-symptoms/rigidity/
Rahmatian, S., Torija, G.: Enfermedad de Parkinson, Últimos Avances en el Tratamiento (2017)
Rodríguez-Martín, D., et al.: A waist-worn inertial measurement unit for long-term monitoring of Parkinson’s disease patients. Sensors 17, 827 (2017). https://doi.org/10.3390/s17040827
Barrientos, A., Del Cerro, J., Gutiérrez, P., San Martín, R., Martínez, A., Rossi, C.: Vehículos aéreos no tripulados para uso civil. Tecnología y aplicaciones, pp. 1–29. Grup. Robótica y Cibernética, Univ. Politécnica Madrid (2009)
Benini, A., Mancini, A., Longhi, S.: An IMU/UWB/vision-based extended Kalman filter for mini-UAV localization in indoor environment using 802.15.4a wireless sensor network. J. Intell. Robot. Syst. Theory Appl. 70, 461–476 (2013). https://doi.org/10.1007/s10846-012-9742-1
Bigy, A.A.M., Banitsas, K., Badii, A., Cosmas, J.: Recognition of postures and Freezing of Gait in Parkinson’s disease patients using Microsoft Kinect sensor. In: International IEEE/EMBS Conference on Neural Engineering, NER, pp. 731–734. IEEE (2015)
Chávez Cardona, A.M., Rodríguez Spitia, F., Baradica López, A.: Exoskeletons to enhance human capabilities and support rehabilitation: a state of the art. Revista Ingeniería Biomédica. 4, 63–73 (2010)
Pons, J., Moreno, J., Brunetti, F., Rocon, E.: Lower-limb wearable exoskeleton. Rehabil. Robot. 3, 471–498 (2007). https://doi.org/10.5772/5176
Sabatini, A.M., Member, S.: Quaternion-based extended Kalman filter for determining orientation by inertial and magnetic sensing. IEEE Trans. Biomed. Eng. 53, 1346–1356 (2006)
Sabatini, A.M.: Inertiel sensing in biomechanics: a survey of computational techniques bridging motion analysis and personal navigation. Comput. Intell. Mov. Sci. 70–100 (2006). https://doi.org/10.4018/978-1-59140-836-9
González Jiménez, J., Baturone, A.O.: Estimación de la Posición de un Robot Móvil. Automatica 29, 3–18 (1996)
ST-Microelectronics: LSM9DS1 iNEMO inertial module, pp. 1–74. STMicroelectronics, Ginebra (2013). DocID02476
Madgwick, S.O.H.: An efficient orientation filter for inertial and inertial/magnetic sensor arrays, p. 32. Report x-io and University of Bristol, Bristol, UK (2010). https://doi.org/10.1109/icorr.2011.5975346
Brigante, C.M.N., Abbate, N., Basile, A., Faulisi, A.C., Sessa, S.: Towards miniaturization of a MEMS-based wearable motion capture system. IEEE Trans. Ind. Electron. 58, 3234–3241 (2011). https://doi.org/10.1109/TIE.2011.2148671
Macmillan, S.: Earth’s magnetic field. Geophys. Geochem. (2013)
National Geophysical Data Center: Magnetic Field Calculators. https://www.ngdc.noaa.gov/geomag-web/#igrfwmm
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Segarra, D., Caballeros, J., Aguilar, W.G., Samà, A., Rodríguez-Martín, D. (2019). Orientation Estimation Using Filter-Based Inertial Data Fusion for Posture Recognition. In: Gilbert, S., Hughes, D., Krishnamachari, B. (eds) Algorithms for Sensor Systems. ALGOSENSORS 2018. Lecture Notes in Computer Science(), vol 11410. Springer, Cham. https://doi.org/10.1007/978-3-030-14094-6_15
Download citation
DOI: https://doi.org/10.1007/978-3-030-14094-6_15
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-14093-9
Online ISBN: 978-3-030-14094-6
eBook Packages: Computer ScienceComputer Science (R0)