Abstract
The aim of the paper is to compare two methods of motion data acquisition using: (1) a mobile device, and (2) an optical reference system. The paper presents a mobile application developed by the authors for the Android platform which was used for motion registration process. The application reads the data from the embedded accelerometer and magnetometer sensors in three dimensions (along X, Y and Z axis). The application performance was evaluated with the help of a passive motion capture system, which was used as a reference. The presented analysis indicates how precise the mobile registration method is in relation to the reference system. Distance and speed are the parameters that were taken into the consideration. Motion was registered by a mobile device attached to the participant’s arm. Retroreflective markers, required by the reference system were attached to the phone and mounting bands. The participant performed the following activities: walking and running. The results obtained using mobile devices were not precise and have been found to strongly depend on the mobile device used. They may be useful for gathering coarse motion statistics.
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
Agrawal, S., Constandache, I., Gaonkar, S., Choudhury, R.R.: Phonepoint pen: using mobile phones to write in air. In: 2009 MobiHeld, Barcelona, pp. 1–6 (2009)
Badurowicz, M., Cieplak, T., Montusiewicz, J.: The cloud computing stream analysis system for road artefacts detection. In: Gaj, P., Kwiecień, A., Stera, P. (eds.) Computer Networks, CCIS, pp. 360–369. Springer, Cham (2016)
Bourouis, A., Zerdazi, A., Feham, M., Bouchachia, A.: M-health: skin disease analysis system using smartphone’s camera. Procedia Comput. Sci. 19, 1116–1120 (2013)
Elhoushi, M., Georgy, J., Korenberg, M., Noureldin, A.: Robust motion mode recognition for portable navigation independent on device usage. In: PLANS 2014, Monterey, pp. 158–163 (2014)
Gebruers, N., Vanroy, C., Truijen, S., Engelborghs, S., De Deyn, P.P.: Monitoring of physical activity after stroke: a systematic review of accelerometry-based measures. Arch. Phys. Med. Rehabil. 91(2), 288–297 (2017)
Gehring, J.: GraphView - open source graph plotting library for Android. http://www.android-graphview.org/
Google: Android API. https://developer.android.com/reference/classes.html
Google: Motion Sensors. https://developer.android.com/guide/topics/sensors/sensors_motion.html
Kopniak, P., Kaminski, M.: Neural network and kalman filter use for improvement of inertial distance determination. In: Gruca, A., Brachman, A., Kozielski, S., Czachórski, T. (eds.) Man-Machine Interactions 4, AISC, pp. 103–114. Springer International Publishing, Switzerland (2016)
Kwapisz, J.R., Weiss, G.M., Moore, S.A.: Activity recognition using cell phone accelerometers. SensorKDD 2010, 10–18 (2010)
Lakens, D.: Using a smartphone to measure heart rate changes during relived happiness and anger. IEEE Trans. Affect. Comput. 4(2), 238–241 (2013)
Lu, H., Frauendorfer, D., Rabbi, M., Mast, M.S., Chittaranjan, G.T., Campbell, A.T., Gatica-Perez, D., Choudhury, T.: Stresssense: detecting stress in unconstrained acoustic environments using smartphones. In: UbiComp 2012, Pittsburgh, pp. 351–360 (2012)
Macias, E., Suarez, A., Lloret, J.: Mobile sensing systems. Sensors 13(12), 17292–17321 (2013)
Moazen, D., Sajjadi, S.A., Nahapetian, A.: AirDraw: leveraging smart watch motion sensors for mobile human computer interactions. In: CCNC 2016, Las Vegas, pp. 442–446 (2016)
Parate, A., Chiu, M.C., Chadowitz, C., Ganesan, D., Kalogerakis, E.: RisQ: recognizing smoking gestures with inertial sensors on a wristband. In: MobiSys 2014, New York, pp. 149–161 (2014)
Parra, L., Sendra, S., Jiménez, J.M., Lloret, J.: Multimedia sensors embedded in smartphones for ambient assisted living and e-health. Multimedia Tools Appl. 75(21), 13271–13297 (2016)
Rahman, M., Topkara, U., Carbunar, B.: Movee: video liveness verification for mobile devices using built-in motion sensors. IEEE Trans. Mob. Comput. 15(5), 1197–1210 (2016)
Seifert, K., Camacho, O.: Implementing positioning algorithms using accelerometers (2007)
Smołka, J., Łukasik, E.: The rigid body gap filling algorithm. In: HSI 2016, Portsmouth, pp. 337–343 (2016)
Smołka, J., Skublewska-Paszkowska, M.: A method for collision detection using mobile devices. In: HSI 2016, Portsmouth, pp. 126–321. IEEE (2016)
Smołka, J., Troć, P., Skublewska-Paszkowska, M., Łukasik, E.: Improved motion type identification method using mobile device sensors. Informatyka Automatyka Pomiary W Gospodarce i Ochronie środowiska 2, 13–18 (2016)
Susi, M., Renaudin, V., Lachapelle, G.: Motion mode recognition and step detection algorithms for mobile phone users. Sensors 13(2), 1539–1562 (2013)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG
About this paper
Cite this paper
Skublewska-Paszkowska, M., Smolka, J., Liwiak, M., Mroz, A. (2018). Mobile Application Using Embedded Sensors as a Three Dimensional Motion Registration Method. In: Gruca, A., Czachórski, T., Harezlak, K., Kozielski, S., Piotrowska, A. (eds) Man-Machine Interactions 5. ICMMI 2017. Advances in Intelligent Systems and Computing, vol 659. Springer, Cham. https://doi.org/10.1007/978-3-319-67792-7_10
Download citation
DOI: https://doi.org/10.1007/978-3-319-67792-7_10
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-67791-0
Online ISBN: 978-3-319-67792-7
eBook Packages: EngineeringEngineering (R0)