A smart watch-based gesture recognition system for assisting people with visual impairments

IMMPD 2013 - Proceedings of the 3rd ACM International Workshop on Interactive Multimedia on Mobile and Portable Devices, Co-located with ACM Multimedia 2013

Volumn , Issue , 2013, Pages 19-24

  Porzi, Lorenzo ^a,b   Messelodi, Stefano ^a   Modena, Carla Mara ^a   Ricci, Elisa ^b  

^a FONDAZIONE BRUNO KESSLER   (Italy)

^b UNIVERSITY OF PERUGIA   (Italy)

Author keywords

accelerometer based gesture recognition; dynamic time warping; smart watch; visual impairments

Indexed keywords

AUTOMATIC RECOGNITION; DAILY LIFE ACTIVITIES; DYNAMIC TIME WARPING; GESTURE RECOGNITION SYSTEM; NOISY ENVIRONMENT; USER-INDEPENDENT RECOGNITION; VISUAL IMPAIRMENT; VISUALLY IMPAIRED;

AUDITION; CELLULAR TELEPHONES; GESTURE RECOGNITION; HANDICAPPED PERSONS; INTERACTIVE COMPUTER SYSTEMS; MOBILE DEVICES; MOBILE PHONES; MULTIMEDIA SYSTEMS; PORTABLE EQUIPMENT;

WATCHES;

EID: 84887201798     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2505483.2505487     Document Type: Conference Paper

References (20)

1. http://developer.sonymobile.com/services/open-smartwatch-project/ smartwatch-hacker-guide/.
2. http://myfreevox.com/en/.
3. A. Akl and S. Valaee. Accelerometer-based gesture recognition via dynamic-time warping, affinity propagation, & compressive sensing. In ICASSP, pages 2270-2273, 2010.
4. R. Amar, S. Dow, R. Gordon, M. R. Hamid, and C. Sellers. Mobile advice: an accessible device for visually impaired capability enhancement. In CHI '03 Extended Abstracts on Human Factors in Computing Systems, CHI EA '03, pages 918-919, New York, NY, USA, 2003. ACM.
5. G. Bieber, T. Kirste, and B. Urban. Ambient interaction by smart watches. In Proceedings of the 5th International Conference on PErvasive Technologies Related to Assistive Environments, PETRA '12, pages 39:1-39:6, New York, NY, USA, 2012. ACM.
6. M. Cuturi. Fast global alignment kernels. In ICML, pages 929-936, 2011.
7. C. Joder, S. Essid, and G. Richard. Temporal integration for audio classification with application to musical instrument classification. Audio, Speech, and Language Processing, IEEE Transactions on, 17(1):174-186, 2009.
8. H. Junker, O. Amft, P. Lukowicz, and G. Tröster. Gesture spotting with body-worn inertial sensors to detect user activities. Pattern Recognition, 41(6):2010-2024, 2008.
9. J. Kela, P. Korpipää, J. Mäntyjärvi, S. Kallio, G. Savino, L. Jozzo, and D. Marca. Accelerometer-based gesture control for a design environment. Personal and Ubiquitous Computing, 10(5):285-299, 2006.
10. M. Khan, S. Ahamed, M. Rahman, and J.-J. Yang. Gesthaar: An accelerometer-based gesture recognition method and its application in nui driven pervasive healthcare. In Emerging Signal Processing Applications (ESPA), 2012 IEEE International Conference on, pages 163-166, 2012.
11. F. C. Y. Li, D. Dearman, and K. N. Truong. Leveraging proprioception to make mobile phones more accessible to users with visual impairments. In ASSETS, pages 187-194, 2010.
12. J. Liu, L. Zhong, J. Wickramasuriya, and V. Vasudevan. uwave: Accelerometer-based personalized gesture recognition and its applications. Pervasive and Mobile Computing, 5(6):657-675, 2009.
13. R. Manduchi and J. M. Coughlan. (computer) vision without sight. Commun. ACM, 55(1):96-104, 2012.
14. S. Mitra and T. Acharya. Gesture recognition: A survey. Systems, Man, and Cybernetics, Part C: Applications and Reviews, IEEE Transactions on, 37(3):311-324, 2007.
15. R. Perfetti, E. Ricci. Reduced complexity RBF classifiers with support vector centres and dynamic decay adjustment. Neurocomputing, 69(16-18):2446-2450, 2006.
16. T. Pylvänäinen. Accelerometer based gesture recognition using continuous hmms. In IbPRIA (1), pages 639-646, 2005.
17. G. Raffa, J. Lee, L. Nachman, and J. Song. Don't slow me down: Bringing energy efficiency to continuous gesture recognition. In ISWC, pages 1-8, 2010.
18. E. Ricci, F. Tobia, and G. Zen. Learning pedestrian trajectories with kernels. In ICPR, pages 149-152, 2010.
19. Sung-Jung. Two-stage recognition of raw acceleration signals for 3-D Gesture-Understanding cell phones. In Tenth International Workshop on Frontiers in Handwriting Recognition, 2006.
20. J. Wu, G. Pan, D. Zhang, G. Qi, and S. Li. Gesture recognition with a 3-d accelerometer. In UIC, pages 25-38, 2009.