Abstract
In this article, we describe a general methodology for enhancing sensing accuracy in cyber-physical systems that involve structured human interactions in noisy physical environment. We define structured human interactions as domain-specific workflow. A novel workflow-aware sensing model is proposed to jointly correct unreliable sensor data and keep track of states in a workflow. We also propose a new inference algorithm to handle cases with partially known states and objects as supervision. Our model is evaluated with extensive simulations. As a concrete application, we develop a novel log service called Emergency Transcriber, which can automatically document operational procedures followed by teams of first responders in emergency response scenarios. Evaluation shows that our system has significant improvement over commercial off-the-shelf (COTS) sensors and keeps track of workflow states with high accuracy in noisy physical environment.
- Artin Avanes and Johann-Christoph Freytag. 2008. Adaptive workflow scheduling under resource allocation constraints and network dynamics. Proc. VLDB Endow. 1, 2 (2008), 1631--1637.Google ScholarDigital Library
- Xu Cheng, Ji Xu, Jian Pei, and Jiangchuan Liu. 2010. Hierarchical distributed data classification in wireless sensor networks. Comput. Commun. 33, 12 (2010), 1404--1413. Google ScholarDigital Library
- Martin Cooke, Phil Green, Ljubomir Josifovski, and Ascension Vizinho. 2001. Robust automatic speech recognition with missing and unreliable acoustic data. Speech Commun. 34, 3 (2001), 267--285. Google ScholarDigital Library
- eSpeak. 2016. Speech Synthesizer (2016). Retrieved from http://espeak.sourceforge.net/.Google Scholar
- EvolveMed. 2016. TalkChart (2016). Retrieved from https://www.talkchart.com/.Google Scholar
- Mark Gales and Steve Young. 2008. The application of hidden Markov models in speech recognition. Found. Trends Sign. Process. 1, 3 (2008), 195--304. Google ScholarDigital Library
- Google. 2016. Google Speech API (2016). Retrieved from https://www.google.com/intl/en/chrome/demos/ speech.html.Google Scholar
- Biing Hwang Juang and Laurence R. Rabiner. 1991. Hidden markov models for speech recognition. Technometrics 33, 3 (1991), 251--272. Google ScholarDigital Library
- Michael Kastner, Mohamed Wagdy Saleh, Stefan Wagner, Michael Affenzeller, and Witold Jacak. 2009. Heuristic methods for searching and clustering hierarchical workflows. In Proceedings of the Conference on Computer Aided Systems Theory (EUROCAST’09). Springer, 737--744. Google ScholarDigital Library
- Insup Lee and Oleg Sokolsky. 2010. Medical cyber physical systems. In Proceedings of the 47th Design Automation Conference. ACM, 743--748. Google ScholarDigital Library
- Mark S. Link, Lauren C. Berkow, Peter J. Kudenchuk, Henry R. Halperin, Erik P. Hess, Vivek K. Moitra, Robert W. Neumar, Brian J. O’Neil, James H. Paxton, Scott M. Silvers, et al. 2015. Part 7: Adult advanced cardiovascular life support 2015 american heart association guidelines update for cardiopulmonary resuscitation and emergency cardiovascular care. Circulation 132, 18 suppl. 2 (2015), S444--S464. Google ScholarCross Ref
- Richard P. Lippmann. 1997. Speech recognition by machines and humans. Speech Commun. 22, 1 (1997), 1--15. Google ScholarDigital Library
- Gonzalo Navarro. 2001. A guided tour to approximate string matching. ACM Comput. Surveys (CSUR) 33, 1 (2001), 31--88. Google ScholarDigital Library
- Nuance. 2016. Dragon Medical Speech Recognition Retrieved from http://www.nuance.com/for-healthcare/dragon-medical/index.htm.Google Scholar
- Lawrence R. Rabiner. 1989. A tutorial on hidden Markov models and selected applications in speech recognition. Proc. IEEE 77, 2 (1989), 257--286. Google ScholarCross Ref
- Lu Su, Jing Gao, Yong Yang, Tarek F. Abdelzaher, Bolin Ding, and Jiawei Han. 2011. Hierarchical aggregate classification with limited supervision for data reduction in wireless sensor networks. In Proceedings of the 9th ACM Conference on Embedded Networked Sensor Systems. ACM, 40--53. Google ScholarDigital Library
- Lu Su, Shaohan Hu, Shen Li, Feng Liang, Jing Gao, Tarek F. Abdelzaher, and Jiawei Han. 2012. Quality of information based data selection and transmission in wireless sensor networks. In Proceedings of the 33rd IEEE Real-Time Systems Symposium (RTSS’12). IEEE, 327--338. Google ScholarDigital Library
- Carolyn Talcott. 2008. Cyber-physical systems and events. In Software-Intensive Systems and New Computing Paradigms. Springer, 101--115. Google ScholarDigital Library
- Steve Young. 2008. HMMs and related speech recognition technologies. In Springer Handbook of Speech Processing. Springer, 539--558. Google ScholarCross Ref
Index Terms
- On Exploiting Structured Human Interactions to Enhance Sensing Accuracy in Cyber-physical Systems
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