Abstract
Seniors are the fastest growing segment of populations not only in many parts of Europe, but also in Japan and the United States. ICT technologies are not very popular among many elderly and also are not designed around their cultural necessities and ergonomic needs. The risk is that in the very near future this growing segment will be digitally isolated, in a society that is more and more based on ICT as infrastructure for service, and communications.
Easy Reach Project proposes an ergonomic application to break social isolation through social interaction to help the elderly to overcome barrier of the digital divide. This paper focuses its attention on the development of the technology and algorithms used as Human Computer Interface of the Easy Reach Project, that exploits inertial sensors to detect gestures.
Many experimental algorithms for gesture recognition have been developed using inertial sensors in conjunction with other sensors or devices, or by themselves, but they have not been thoroughly tested in real situations, they are not devoted to adapt to the elderly and their way of executing gestures. The elderly are not used to modern interfaces and devices, and – due to aging – they can face problems in executing even very simple gestures.
Our algorithm based on Pearson index and Hamming distance for gestures recognition has been tested both with young and elderly, and was shown to be resilient to changes in velocity and individual differences, still maintaining great accuracy of recognition (97.4% in user independent mode; 98.79% in user dependent mode). The algorithm has been adopted by the Easy Reach consortium (2009-2013) to pilot the human machine gesture-based interface.
Keywords
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Bisiani, R., Merico, D., Pinardi, S., Dominoni, M., Cesta, A., Orlandini, A., Rasconi, R., Suriano, M., Umbrico, A., Sabuncu, O., Schaub, T., D’Aloisi, D., Nicolussi, R., Papa, F., Bouglas, V., Giakas, G., Kavatzikidis, T., Bonfiglio, S.: Fostering Social Interaction of Home-Bound Elderly People: The EasyReach System. In: IEA/AIE 2013, Amsterdam (2013)
Hans-Helmut, K., Dirk, H., Thomas, L., Steffi, G.R.-H., Matthias, C.A., Herbert, M., Vilagut, G., Ronny, B., Josep, M.H., Giovanni, D.G., Ron, D.G., Viviane, K., Jordi, A.: Health status of the advanced elderly in six european countries: Results from a representative survey using EQ-5D and SF-12. Health and Quality of Life Outcomes 2010 143(8), 143 (2010)
Hoffman, F.G., Heyer, P., Hommel, G.: Velocity Profile Based Recognition of Dynamic Gestures with Discrete Hidden Markov Models (1996)
Mäntylä, V.M., Mäntyjärvi, J., Seppänen, T., Tuulari, E.: Hand gesture recognition of a mobile device user. IEEE (2000)
Schlömer, T., Poppinga, B., Henze, N., Boll, S.: Gesture Recognition with a Wii Controller. In: Proceedings of the Second International Conference on Tangible and Embedded Interaction, Bonn (2008)
Prekopcsák, Z.: Accelerometer Based Real-Time Gesture Recognition. Poster (2008)
Cho, S.J., Oh, J.K., Bang, W.C., Chang, W., Choi, E., Jing, Y., Cho, J., Kim, D.Y.: Magic Wand: A Hand-Drawn Gesture Input Device in 3-D Space with Inertial Sensors. In: Proceedings of the 9th Int’l Workshop on Frontiers in Handwriting Recognition (2004)
Wu, J., Pan, G., Zhang, D., Qi, G., Li, S.: Gesture Recognition with a 3-D Accelerometer. In: Zhang, D., Portmann, M., Tan, A.-H., Indulska, J. (eds.) UIC 2009. LNCS, vol. 5585, pp. 25–38. Springer, Heidelberg (2009)
Kratz, S., Rohs, M.: A $3 Gesture Recognizer – Simple Gesture Recognition for Devices Equipped with 3D Acceleration Sensors. ACM (2010)
Chen, M., AlRegib, G., Juang, B.: A new 6D motion gesture database and the benchmark results of feature-based statistical recognition (2011)
Pinardi, S., Bisiani, R.: Movements Recognition with Intelligent Multisensor Analysis, A Lexical Approach. In: Proceedings of the 6th Int. Conf. on Intelligent Environments, Kuala Lumpur (2010)
Gupta, S., Morris, D., Patel, S.N., Desney, T.: SoundWave: Using the Doppler Effect to Sense Gestures. Redmond (2012)
Xu, R., Zhou, S., Li, W.J.: MEMS Accelerometer Based Nonspecific-User Hand Gesture Recognition. IEEE Sensors Journal (May 5, 2012)
Kratz, L., Saponas, T.S., Morris, D.: Making Gestural Input from Arm-Worn Inertial Sensors More Practical. ACM (2012)
XSens, XM-B Technical Documentation (2009)
STMicroelectronics, LIS331DLH - MEMS digital output motion sensor ultra low-power high performance 3-axes “nano” accelerometer (2009)
Zhou, S., Dong, Z., Li, W.J., Kwong, C.P.: Hand-Written Character Recognition Using MEMS Motion Sensing Technology. In: Proceedings of the 2008 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (2008)
Keir, P., Elgoyhen, J., Naef, M., Payne, J., Horner, M., Anderson, P.: Gesture-recognition with Non-referenced Tracking. In: Proceedings of the 2006 IEEE Symposium on 3D User interfaces (2006)
Fihl, P., Holte, M., Moeslund, T., Reng, L.: Action Recognition using Motion Primitives and Probabilistic Edit Distance (2006)
Pylvänäinen, T.: Accelerometer Based Gesture Recognition Using Continuous HMMs. In: Marques, J.S., Pérez de la Blanca, N., Pina, P. (eds.) IbPRIA 2005. LNCS, vol. 3522, pp. 639–646. Springer, Heidelberg (2005)
Tuulari, E., Ylisaukko-oja, A.: SoapBox: A Platform for Ubiquitous Computing Research and Applications. In: Mattern, F., Naghshineh, M. (eds.) PERVASIVE 2002. LNCS, vol. 2414, pp. 125–138. Springer, Heidelberg (2002)
Vogler, C., Sun, H., Metaxas, D.: A Framework for Motion Recognition with Applications to American Sign Language and Gait Recognition. IEEE (2000)
Gavrila, D.M.: The Visual Analisys of Human Movement: A Survey. Academic Press (1998)
Wang, J.S., Chuang, F.C.: An Accelerometer-Based Digital Pen With a Trajectory Recognition Algorithm for Handwritten Digit and Gesture Recognition. IEEE (2011)
Choi, E., Bang, W., Cho, S., Yang, J., Kim, D., Kim, S.: Beatbox Music Phone: Gesture-based Interactive Mobile Phone using a Tri-axis Accelerometer. IEEE (2005)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Pinardi, S., Dominoni, M. (2014). Gestures as Interface for a Home TV Digital Divide Solutions through Inertial Sensors. In: Ali, M., Pan, JS., Chen, SM., Horng, MF. (eds) Modern Advances in Applied Intelligence. IEA/AIE 2014. Lecture Notes in Computer Science(), vol 8482. Springer, Cham. https://doi.org/10.1007/978-3-319-07467-2_38
Download citation
DOI: https://doi.org/10.1007/978-3-319-07467-2_38
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-07466-5
Online ISBN: 978-3-319-07467-2
eBook Packages: Computer ScienceComputer Science (R0)