Abstract
We present algorithms for gesture recognition using in-network processing in distributed sensor arrays embedded within systems such as tactile input devices, sensing skins for robotic applications, and smart walls. We describe three distributed gesture-recognition algorithms that are designed to function on sensor arrays with minimal computational power, limited memory, limited bandwidth, and possibly unreliable communication. These constraints cause storage of gesture templates within the system and distributed consensus algorithms for recognizing gestures to be difficult. Building up on a chain vector encoding algorithm commonly used for gesture recognition on a central computer, we approach this problem by dividing the gesture dataset between nodes such that each node has access to the complete dataset via its neighbors. Nodes share gesture information among each other, then each node tries to identify the gesture. In order to distribute the computational load among all nodes, we also investigate an alternative algorithm, in which each node that detects a motion will apply a recognition algorithm to part of the input gesture, then share its data with all other motion nodes. Next, we show that a hybrid algorithm that distributes both computation and template storage can address trade-offs between memory and computational efficiency.
- C. Bahlmann, B. Haasdonk, and H. Burkhardt. 2002. Online handwriting recognition with support vector machines-a kernel approach. In Proceedings of the 8th International Workshop on Frontiers in Handwriting Recognition, 2002. IEEE, 49--54. Google Scholar
Digital Library
- W. Butera. 2002. Programming a Paintable Computer. Ph.D. dissertation. Program in Media Arts and Sciences, School of Architecture and Planning, MIT, Cambridge, MA. Google Scholar
Digital Library
- J. M. Carmona and J. Climent. 2012. A performance evaluation of HMM and DTW for gesture recognition (Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications), L. lvarez, M. Mejail, L. Gmez Dniz, and J. C. Jacobo (Eds.). Springer, New York, NY, 236--243.Google Scholar
- W. J. Confer and R. O. Chapman. 2004. System and method of handwritten character recognition. (April 2004). Patent No. 6,721,452. Filed September 12, 2002. Issued April 13, 2004.Google Scholar
- R. H. Davis and J. Lyall. 1986. Recognition of handwritten characters—a review. Image and Vision Computing 4, 4, 208--218. Google Scholar
Digital Library
- M. Elmezain, A. Al-hamadi, and B. Michaelis. 2009. A hidden Markov model-based isolated and meaningful hand gesture recognition. International Journal of Electronical, Computer, and Systems Engineering 3, 3.Google Scholar
- N. Farrow, N. Sivagnanadasan, and N. Correll. 2014. Gesture based distributed user interaction system for a reconfigurable self-organizing smart wall. In Proceedings of the 8th International Conference on Tangible, Embedded and Embodied Interaction (TEI). ACM, New York, NY, 245--246. Google Scholar
Digital Library
- J. Hawkins. 2002. Graffiti (Palm OS). https://en.wikipedia.org/wiki/Graffiti_(Palm_OS).Google Scholar
- H. Hosseinmardi, N. Correll, and R. Han. 2012. Bloom filter-based ad hoc multicast communication in cyber-physical systems and computational materials. Wireless Algorithms, Systems, and Applications 595--606.Google Scholar
- D. Hughes and N. Correll. 2014. A soft, amorphous skin that can sense and localize texture. In IEEE International Conference on Robotics and Automation (ICRA). Hong Kong, 1844--1851. Retrieved July 28, 2015 from http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber==6907101.Google Scholar
- H. K. Lee and J. H. Kim. 1999. An HMM-based threshold model approach for gesture recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence 21, 10, 961--973. Google Scholar
Digital Library
- J. Lifton, D. Seetharam, M. Broxton, and J. Paradiso. 2002. Pushpin computing system overview: A platform for distributed, embedded, ubiquitous sensor networks. Pervasive Computing 139--151. Google Scholar
Digital Library
- V. J. Lumelsky, M. S. Shur, and S. Wagner. 2001. Sensitive skin. IEEE Sensors Journal 1, 1, 41--51.Google Scholar
Cross Ref
- S. Ma, H. Hosseinmardi, N. Farrow, R. Han, and N. Correll. 2012. Establishing multi-cast groups in computational robotic materials. In IEEE International Conference on Cyber, Physical and Social Computing. Besancon, France. Google Scholar
Digital Library
- M. A. McEvoy and N. Correll. 2014. Thermoplastic variable stiffness composites with embedded, networked sensing, actuation, and control. Journal of Composite Materials. Retrieved July 28, 2015 from http://jcm.sagepub.com/content/early/2014/03/07/0021998314525982.Google Scholar
- M. A. McEvoy and N. Correll. 2015. Materials that couple sensing, actuation, computation and communication. Science 347, 6228. Retrieved July 28, 2015 from http://www.sciencemag.org/lookup/doi/10.1126/science.1261689.Google Scholar
Cross Ref
- M. A. McEvoy, N. Farrow, and N. Correll. 2013. Robotic materials with controllable stiffness. In Proceedings of the 19th International Conference on Composite Materials (ICCM).Google Scholar
- S. Mitra and T. Acharya. 2007. Gesture recognition: A survey. IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews 37, 3, 311--324. Google Scholar
Digital Library
- K. Murakami and H. Taguchi. 1991. Gesture recognition using recurrent neural networks. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems: Reaching through Technology. ACM, 237--242. Google Scholar
Digital Library
- J. Nagi, H. Ngo, A. Giusti, Luca M. Gambardella, J. Schmidhuber, and G. A. Di Caro. 2012. Incremental learning using partial feedback for gesture-based human-swarm interaction. In RO-MAN. IEEE, 898--905.Google Scholar
- Z. Nakad, M. Jones, and T. Martin. 2003. Communications in electronic textile systems. In Proceedings of the 2003 International Conference on Communications in Computing. 37--43.Google Scholar
- O. F. Ozer, O. Ozun, C. O. Tuzel, V. Atalay, and A. E. Cetin. 2001. Vision-based single-stroke character recognition for wearable computing. Intelligent Systems, IEEE 16, 3, 33--37. Google Scholar
Digital Library
- J. A. Paradiso, J. Lifton, and M. Broxton. 2004. Sensate media multimodal electronic skins as dense sensor networks. BT Technology Journal 22, 4, 32--44. Google Scholar
Digital Library
- H. Profita, N. Farrow, and N. Correll. 2015. Flutter: An exploration of an assistive garment using distributed sensing, computation and actuation. In Proceedings of the 9th International Conference on Tangible, Embedded, and Embodied Interaction (TEI’15). ACM, New York, NY, 359--362. DOI:http://dx.doi.org/10.1145/2677199.2680586 Google Scholar
Digital Library
- H. Sakoe and S. Chiba. 1990. Dynamic Programming Algorithm Optimization for Spoken Word Recognition. In A. Waibel and K.-F. Lee (Eds.). Readings in Speech Recognition. Morgan Kaufmann Publishers Inc., San Francisco, CA. 159--165. Google Scholar
Digital Library
- T. Schlömer, B. Poppinga, N. Henze, and S. Boll. 2008. Gesture recognition with a Wii controller. In Proceedings of the 2nd International Conference on Tangible and Embedded Interaction. ACM, New York, NY, 11--14. Google Scholar
Digital Library
- A. D. Wilson and A. F. Bobick. 1999. Parametric hidden Markov models for gesture recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 21, 9, 884--900. Google Scholar
Digital Library
- Y. Wu and T. Huang. 1999. Vision-based gesture recognition: A review. Gesture-based Communication in Human-Computer Interaction, Lecture Notes in Computer Science, Vol. 1739, Springer, Berlin, 103--115. Google Scholar
Digital Library
Index Terms
Distributed Spatiotemporal Gesture Recognition in Sensor Arrays
Recommendations
A Method to Recognize Eyeball Movement Gesture using Infrared Distance Sensor Array on Eyewear
iiWAS2021: The 23rd International Conference on Information Integration and Web IntelligenceSensing technology for eyeball movement (i.e., gaze movement) has enabled various applications, e.g., hands-free input interfaces and provided information that helps us understand human beings in various fields. However, the existing method, which ...
Multi-scenario gesture recognition using Kinect
CGAMES '12: Proceedings of the 2012 17th International Conference on Computer Games: AI, Animation, Mobile, Interactive Multimedia, Educational & Serious Games (CGAMES)Hand gesture recognition (HGR) is an important research topic because some situations require silent communication with sign languages. Computational HGR systems assist silent communication, and help people learn a sign language. In this article, a ...
Recent methods and databases in vision-based hand gesture recognition
The paper surveys RGB and RGB-D sensors based hand gesture recognition methods.Dynamic as well as static gesture (posture/pose) recognition methods are reviewed.Qualitative as well as quantitative comparison of algorithms is provided.Twenty-six publicly ...






Comments