Abstract
Wearable devices based on inertial measurement units (IMUs) are now-a-days a de facto standard in the field of human motion analysis. Lower costs, improved quality and enhanced accuracy promote a very fast and diffused adoption of such devices in healthcare and wellness areas. In clinical settings, these technological solutions allow for a quantitative evaluation of functional and clinical tests. This article aimed to present a practical and feasible approach using IMU-based wearable devices and mobile applications to rapidly collect 3D motion information coming from different body segments. The proposed solution was specifically designed for a rapid and precise monitoring of the patient’s status both outdoor and indoor, including home and clinical contexts. The modularity concept in designing the application allows to easily plug specific and customized modules addressing data analysis and patient status assessment. The acquired data are always available to the user to be archived or re-processed. Without loss of generality, the developed system was tested in a real clinical context, addressing the need for assessing the shoulder mobility in order to automatically identify the presence of symptomatic or asymptomatic humerus-scapular dyskinesis. This approach allowed to define a kinematic-based set of novels metrics - called Shoulder Primary Key Indicators. The proposed system demonstrated to be a practical and effective solution in the most clinical context, giving room to the adoption of this kind of approach to a wider range of applications related to the functional assessment of different body segments and joints, such as the knee, the spine or the elbow.
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References
Roetenberg, D.: Inertial and magnetic sensing of human motion [s.n.], S.l. (2006)
Krüger, A., Edelmann-Nusser, J.: Application of a full body inertial measurement system in alpine skiing: a comparison with an optical video based system. J. Appl. Biomech. 26(4), 516–521 (2010). https://doi.org/10.1123/jab.26.4.516
Deppe, O., Dorner, G., König, S., Martin, T., Voigt, S., Zimmermann, S.: MEMS and FOG technologies for tactical and navigation grade inertial sensors-recent improvements and comparison. Sensors 17(3) (2017). https://doi.org/10.3390/s17030567
Avci, A., Bosch, S., Marin Perianu, M., Marin Perianu, R., Havinga, P.J.M.: Activity recognition using inertial sensing for healthcare, wellbeing and sports applications: a survey, pp. 167–176, February 2010
Patel, S., Park, H., Bonato, P., Chan, L., Rodgers, M.: A review of wearable sensors and systems with application in rehabilitation. BioMed. Cent. (2012). https://doi.org/10.1186/1743-0003-9-21
Sazonov, E., Neuman, M.R. (eds.): Wearable Sensors: Fundamentals, Implementation and Applications. Academic Press is an imprint of Elsevier, Amsterdam (2014)
Leonard, K.: Critical success factors relating to healthcare’s adoption of new technology: a guide to increasing the likelihood of successful implementation. Electron. Healthc. 2(4), 72–81 (2004)
Ventola, C.L.: Mobile devices and apps for health care professionals: uses and benefits. P T Peer-Rev. J. Formul. Manag. 39(5), 356–364 (2014)
XSENSE DOT, 07 February 2020. https://www.xsens.com/xsens-dot
Wearnotch, 07 February 2020. https://wearnotch.com
Bencardino, J.T. (ed.): The Shoulder: Imaging Diagnosis with Clinical Implications. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-06240-8
Bronzino, J.D., Peterson, D.R.: Biomedical Engineering Fundamentals. CRC Press (2017)
Rockwood, C.A.: The shoulder. Saunders/Elsevier, Philadelphia (2009)
Ludewig, P.M., Braman, J.P.: Shoulder impingement: biomechanical considerations in rehabilitation. Man. Ther. 16(1), 33–39 (2011). https://doi.org/10.1016/j.math.2010.08.004
Watson, L., Balster, S.M., Finch, C., Dalziel, R.: Measurement of scapula upward rotation: a reliable clinical procedure. Br. J. Sports Med. 39(9), 599–603 (2005). https://doi.org/10.1136/bjsm.2004.013243
McCluskey, G.M., Getz, B.A.: Pathophysiology of anterior shoulder instability. J. Athl. Train. 35(3), 268–272 (2000)
Tate, A.R., McClure, P., Kareha, S., Irwin, D., Barbe, M.F.: A clinical method for identifying scapular dyskinesis, part 2: validity. J. Athl. Train. 44(2), 165–173 (2009). https://doi.org/10.4085/1062-6050-44.2.165
McClure, P., Tate, A.R., Kareha, S., Irwin, D., Zlupko, E.: A clinical method for identifying scapular dyskinesis, part 1: reliability. J. Athl. Train. 44(2), 160–164 (2009). https://doi.org/10.4085/1062-6050-44.2.160
Garofalo, P.: Development of Motion Analysis Protocols Based on Inertial Sensors Healthcare Applications. Lambert Academic Publishing, Saarbrücken (2011)
Körver, R.J.P., Senden, R., Heyligers, I.C., Grimm, B.: Objective outcome evaluation using inertial sensors in subacromial impingement syndrome: a five-year follow-up study. Physiol. Measur. 35(4), 677–686 (2014). https://doi.org/10.1088/0967-3334/35/4/677
Hsu, Y.-L., et al.: A wearable inertial-sensing-based body sensor network for shoulder range of motion assessment. In: 2013 1st International Conference on Orange Technologies (ICOT), Tainan, pp. 328–331, March 2013. https://doi.org/10.1109/ICOT.2013.6521225
van den Noort, J.C., Wiertsema, S.H., Hekman, K.M.C., Schönhuth, C.P., Dekker, J., Harlaar, J.: Measurement of scapular dyskinesis using wireless inertial and magnetic sensors: importance of scapula calibration. J. Biomech. 48(12), 3460–3468 (2015). https://doi.org/10.1016/j.jbiomech.2015.05.036
Inman, V.T., Saunders, J.B., Abbott, L.C.: Observations of the function of the shoulder joint. Clin. Orthop. (330), 3–12 (1996). https://doi.org/10.1097/00003086-199609000-00002
Koo, T.K., Li, M.Y.: A guideline of selecting and reporting intraclass correlation coefficients for reliability research. J. Chiropr. Med. 15(2), 155–163 (2016). https://doi.org/10.1016/j.jcm.2016.02.012
Gallin, J.I., Ognibene, F.P., Johnson, L.L. (eds.): Principles and Practice of Clinical Research, 4th edn. Academic Press, London (2018)
Liu, L.: Biostatistical basis of inference in heart failure study. In: Heart Failure: Epidemiology and Research Methods, pp. 43–82. Elsevier (2018)
Riffenburgh, R.H.: Statistics in Medicine, 3rd edn. Elsevier/AP, Amsterdam (2012)
Hoffman, J.I.E.: Biostatistics for Medical and Biomedical Practitioners. Academic Press, Amsterdam (2015)
Laboratory statistics. Elsevier, Waltham (2017)
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Mosna, P., Luongo, R., Morghen, M., Lopomo, N.F. (2021). Integration of Wearable Inertial Sensors and Mobile Technology for Outpatient Functional Assessment: A Paradigmatic Application to Evaluate Shoulder Stability. In: Perego, P., TaheriNejad, N., Caon, M. (eds) Wearables in Healthcare. ICWH 2020. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 376. Springer, Cham. https://doi.org/10.1007/978-3-030-76066-3_7
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