Abstract
This article introduces a gesture-based user interface for hospital beds. This interface enables caregivers to focus on their patients and have both hands available for mobilizing and transferring them. Gestures are detected either via static (96% sensitivity) or dynamic gestures (67.5% sensitivity) and might be corrected by an extra repetition. Once gestures are correctly detected, caregivers can trigger bed movements via a foot switch as a hands-free operation, which as well functions as a dead man button. The evaluation of the usability through interviews with caregivers highlighted the system’s general applicability, but as well some future challenges that have to be solved in order to achieve a system for every-day use.
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References
Bradski, G.: OpenCV. Dr. Dobb’s J. Softw. Tools (2000)
Celebi, S., Aydin, A.S., Temiz, T.T., Arici, T.: Gesture recognition using skeleton data with weighted dynamic time warping. In: Battiato, S., Braz, J. (eds.) VISAPP 2013—Proceedings of the International Conference on Computer Vision Theory and Applications, vol. 1, Barcelona, Spain, 21–24 February 2013, pp. 620–625 (2013)
Dhawan, A., Honrao, V.: Implementation of hand detection based techniques for human computer interaction. Int. J. Comput. Appl. 72(17) (2013)
Gallo, L., Placitelli, A., Ciampi, M.: Controller-free exploration of medical image data: experiencing the kinect. In: 2011 24th International Symposium on Computer-Based Medical Systems (CBMS), pp. 1–6 (2011). doi:10.1109/CBMS.2011.5999138
Gerdes, S., Redlich, C., Yilmaz, M.: 4. Gesundheitsbericht 2015: Norovirus kompakt (2015). http://www.hannover.de/content/download/542635/12405677/file/Gesundheitsbericht_2015_Norovirus.pdf. Accessed 13 Nov 2015
Hasan, H., Abdul-Kareem, S.: Static hand gesture recognition using neural networks. Artif. Intell. Rev. 41(2), 147–181 (2014). doi:10.1007/s10462-011-9303-1
Karam, M.: A Framework for Gesture-Based Human Computer Interactions. VDM Verlag, Saarbrücken (2009)
Keiser, T., Höß, O., Klein, B., Neuhüttler, J., Schneider, H., Vetter, T.: Gestensteuerung im Pflegeumfeld—Das Projekt GeniAAL: Grundlagen, Anwendungsfelder, Technologien und Erfahrungen. Books on Demand (2015). https://books.google.de/books?id=qerVBgAAQBAJ
Keskin, C., Kra, F., Kara, Y., Akarun, L.: Real time hand pose estimation using depth sensors. In: Fossati, A., Gall, J., Grabner, H., Ren, X., Konolige, K. (eds.) Consumer depth cameras for computer vision, advances in computer vision and pattern recognition, pp. 119–137. Springer, London (2013). doi:10.1007/978-1-4471-4640-7_7
Kühnel, C., Westermann, T., Hemmert, F., Kratz, S., Möller, S.: I’m home: defining and evaluating a gesture set for smart-home control. Int. J. Hum. Comput. Stud. 69(11), 693–704 (2011). doi:10.1016/j.ijhcs.2011.04.005
Liwicki, S., Everingham, M.: Automatic recognition of finger spelled words in british sign language. In: Proceedings of the 2nd IEEE Workshop on CVPR for Human Communicative Behavior Analysis (CVPR4HB’09). In conjunction with CVPR2009, pp. 50–57. IEEE Computer Society, Los Alamitos, CA, USA (2009)
Microsoft Developer Network: Skeletal tracking (2015). https://msdn.microsoft.com/en-us/library/hh973074.aspx. Accessed 13 Nov 2015
Müller, M.: Information Retrieval for Music and Motion. Springer, New York (2007). doi:10.1007/978-3-540-74048-3
Music, D., Eghbal, D., Vargas, S.: User interface and identification in a medical device system and method (2010). https://www.google.com/patents/US7706896. US Patent 7,706,896
Park, S., Yu, S., Kim, J., Kim, S., Lee, S.: 3d hand tracking using kalman filter in depth space. EURASIP J. Adv. Signal Proces. 2012(1), 36 (2012). doi:10.1186/1687-6180-2012-36
Pham, C.H., Le, Q.K., Le, T.H.: Human action recognition using dynamic time warping and voting algorithm. VNU J. Sci. Comput. Sci. Commun. Eng. 30(3), 22–30 (2014)
Pohl, C.: Der zukünftige Bedarf an Pflegearbeitskräften in Deutschland: Modellrechnungen für die Bundesländer bis zum Jahr 2020. Comparative Population Studies—Zeitschrift für Bevölkerungswissenschaft 35(2), 357–378 (2010)
Preim, B., Dachselt, R.: Interaktive Systeme: Band 2: User Interface Engineering, 3D-Interaktion, Natural User Interfaces, vol. 2. Springer Vieweg (2015). doi:10.1007/978-3-642-45247-5
Quigley, M., Conley, K., Gerkey, B., Faust, J., Foote, T.B., Leibs, J., Wheeler, R., Ng, A.Y.: ROS: an open-source robot operating system. In: ICRA Workshop on Open Source Software (2009)
Rautaray, S., Agrawal, A.: Vision based hand gesture recognition for human computer interaction: a survey. Artif. Intell. Rev. 43(1), 1–54 (2015). doi:10.1007/s10462-012-9356-9
Rehrl, T., Blume, J., Bannat, A., Rigoll, G., Wallhoff, F.: On-line learning of dynamic gestures for human-robot interaction. In: 35th German Conference on Artificial Intelligence, KI 2012, Saarbrücken, Germany (2012)
Ren, Y., Zhang, F.: Hand gesture recognition based on meb-svm. In: International Conference on Embedded Software and Systems, 2009. ICESS ’09, pp. 344–349 (2009). doi:10.1109/ICESS.2009.21
Ren, Z., Yuan, J., Zhang, Z.: Robust hand gesture recognition based on finger-earth mover’s distance with a commodity depth camera. In: Proceedings of the 19th ACM International Conference on Multimedia, MM ’11, pp. 1093–1096. ACM, New York (2011). doi:10.1145/2072298.2071946
Schramm, R., Jung, R.C., Miranda, E.R.: Dynamic time warping for music conducting gestures evaluation. IEEE Trans. Multimedia 17(2), 243–255 (2015). doi:10.1109/TMM.2014.2377553
Sklansky, J.: Finding the convex hull of a simple polygon. Pattern Recognition Letters 1(2), 79–83 (1982). doi:10.1016/0167-8655(82)90016-2
Suzuki, S., Abe, K.: Topological structural analysis of digitized binary images by border following. CVGIP—Graph. Mod. Image Proces. 30(1), 32–46 (1985)
Trigueiros, P., Ribeiro, F., Reis, L.: Hand gesture recognition for human computer interaction: a comparative study of different image features. In: Filipe, J., Fred, A. (eds.) Agents and Artificial Intelligence, Communications in Computer and Information Science, vol. 449, pp. 162–178. Springer, Berlin (2014). doi:10.1007/978-3-662-44440-5_10
Wahl, F.M.: Digitale Bildsignalverarbeitung: Grundlagen, Verfahren. Springer, Beispiele (1984)
Winther, B., McCue, K., Ashe, K., Rubino, J., Hendley, O.: Contamination of environmental surfaces during normal daily activities of hotel guests with rhinovirus colds. In: 46th Annual ICAAC—Interscience Conference on Antimicrobial Agents and Chemotherapy, September 27–30, 2006, San Francisco (2006)
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Fudickar, S., Flessner, J., Volkening, N., Steen, EE., Isken, M., Hein, A. (2017). Gesture Controlled Hospital Beds for Home Care. In: Wichert, R., Mand, B. (eds) Ambient Assisted Living. Advanced Technologies and Societal Change. Springer, Cham. https://doi.org/10.1007/978-3-319-52322-4_7
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