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Sensor Data Streams

Abstract

It is possible today to collect streams of data from sensors in the environment (e.g., on walls of buildings) or attached to individuals (e.g., badges that record location and with whom one is speaking). The data from these sensors allows researchers to trace people’s behavior with and without various technology interventions or incentives intended to change behavior. These traces can also be used inside technologies, for example to sense when it is a good time to interrupt a person with a message.

Keywords

  • Data Stream
  • Sensor Data
  • Ubiquitous Computing
  • Gesture Recognition
  • Experience Sampling Method

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  • Abowd, G. D., Atkeson, C. G., Hong, J., Long, S., Kooper, R., & Pinkerton, M. (1997, October). CyberGuide: A mobile context-aware tour guide. Wireless Networks, 3(5), 421–433.

    Google Scholar 

  • Bailey, B. P., & Iqbal, S. T. (2008). Understanding changes in mental workload during execution of goal-directed tasks and its application for interruption management. ACM Transactions on Computer-Human Interaction, 14(4), 1–28.

    CrossRef  Google Scholar 

  • Bailey, B. P., & Konstan, J. A. (2006). On the need for attention-aware systems: Measuring effects of interruption on task performance, error rate, and affective state. Computers in Human Behavior, 22(4), 685–708.

    CrossRef  Google Scholar 

  • Bailey, B. P., Konstan, J. A., & Carlis, J. V. (2001). The effects of interruptions on task performance, annoyance, and anxiety in the user interface. In Proceedings of the IFIP TC.13 international conference on human-computer interaction (INTERACT ’01) (pp. 593–601). Amsterdam: Ios Press.

    Google Scholar 

  • Baldauf, M., Dustdar, S., & Rosenberg, F. (2007). A survey on context-aware systems. International Journal of Ad Hoc and Ubiquitous Computing, 2(4), 263–277.

    CrossRef  Google Scholar 

  • Bao, L., & Intille, S. (2004). Activity recognition from user-annotated acceleration data. In Proceedings of the second international conference on pervasive computing (PERVASIVE 2004) (pp. 1–17). Berlin: Springer.

    Google Scholar 

  • Begole, J., & Tang, J. C. (2007). Incorporating human and machine interpretation of unavailability and rhythm awareness into the design of collaborative applications. Human–Computer Interaction, 22(1), 7–45.

    Google Scholar 

  • Begole, J., Tang, J. C., Smith, R. B., & Yankelovich, N. (2002). Work rhythms: Analyzing visualizations of awareness histories of distributed groups. In Proceedings of the 2002 ACM conference on computer supported cooperative work (CSCW ’02) (pp. 334–343). New Orleans, LA: ACM Press.

    CrossRef  Google Scholar 

  • Bolchini, C., Curino, C., Quintarelli, E., Schreiber, F. A., & Tanca, L. (2007). A data-oriented survey of context models. SIGMOD Record, 36(4), 19–26.

    CrossRef  Google Scholar 

  • Brdiczka, O., Su, N. M., & Begole, J. B. (2010). Temporal task footprinting: Identifying routine tasks by their temporal patterns. In Proceedings of the 14th international conference on intelligent user interfaces (IUI ’10) (pp. 281–284). New York, NY: ACM Press.

    CrossRef  Google Scholar 

  • Brumitt, B., Meyers, B., Krumm, J., Kern, A., & Shafer, S. (2000). EasyLiving: Technologies for intelligent environments. In Proceedings of the second international symposium on handheld and ubiquitous computing (HUC ’00) (pp. 97–119). Berlin: Springer.

    Google Scholar 

  • Choudhury, T., Consolvo, S., Harrison, B., Hightower, J., LaMarca, A., LeGrand, L., et al. (2008). The mobile sensing platform: An embedded activity recognition system. IEEE Pervasive Computing, 7(2), 32–41.

    CrossRef  Google Scholar 

  • Choudhury, T., & Pentland, A. (2003). Sensing and modeling human networks using the sociometer. In Proceedings of the seventh IEEE international symposium on wearable computers (ISWC ’03) (pp. 216–222). Los Alamitos, CA: IEEE Computer Society.

    CrossRef  Google Scholar 

  • Cohn, G., Gupta, S., Froehlich, J., Larson, E., & Patel, S. N. (2010). GasSense: Appliance-level, single-point sensing of gas activity in the home. In Proceedings of 8th international conference on pervasive computing (PERVASIVE 2010) (pp. 265–282). Berlin: Springer.

    Google Scholar 

  • Cohn, G., Stuntebeck, E., Pandey, J., Otis, B., Abowd, G. D., & Patel, S. N. (2010). SNUPI: Sensor nodes utilizing powerline infrastructure. In Proceedings of the 12th international conference on ubiquitous computing (UbiComp 2010) (pp. 159–168). New York, NY: ACM Press.

    CrossRef  Google Scholar 

  • Consolvo, S., Klasnja, P., McDonald, D. W., Avrahami, D., Froehlich, J., LeGrand, L., et al. (2008). Flowers or a robot army?: Encouraging awareness & activity with personal, mobile displays. In Proceedings of the 10th international conference on ubiquitous computing (UbiComp ’08) (pp. 54–63). New York, NY: ACM Press.

    CrossRef  Google Scholar 

  • Consolvo, S., McDonald, D. W., Toscos, T., Chen, M. Y., Froehlich, J., Harrison, B., et al. (2008). Activity sensing in the wild: A field trial of ubifit garden. In Proceedings of the SIGCHI conference on human factors in computing systems (CHI ’08) (pp. 1797–1806). New York, NY: ACM Press.

    Google Scholar 

  • Consolvo, S., & Walker, M. (2003). Using the experience sampling method to evaluate ubicomp applications. IEEE Pervasive Computing, 2(2), 24–31.

    CrossRef  Google Scholar 

  • Dahlbäck, N., Jönsson, A., & Ahrenberg, L. (1993). Wizard of Oz studies: Why and how. In Proceedings of the 1st international conference on intelligent user interfaces (IUI ’93) (pp. 193–200). New York, NY: ACM Press.

    CrossRef  Google Scholar 

  • Demšar, J. (2006). Statistical comparisons of classifiers over multiple data sets. The Journal of Machine Learning Research, 7, 1–30.

    MATH  Google Scholar 

  • Dey, A. K., & Abowd, G. D. (2001). A conceptual framework and a toolkit for supporting rapid prototyping of context-aware applications. Human-Computer Interaction, 16(2–4), 7–166.

    Google Scholar 

  • Dey, A., Wac, K., Ferreira, D., Tassini, K., Hong, J., & Ramos, J. (2011). Getting closer: An empirical investigation of the proximity of user to their smart phones. In Proceedings of the 13th international conference on ubiquitous computing (UbiComp ’11) (pp. 163–172). New York, NY: ACM Press.

    CrossRef  Google Scholar 

  • Domingos, P. (2012, October). A few useful things to know about machine learning. Communications of the ACM, 55(10), 78–87.

    Google Scholar 

  • Fan, M., Gravem, D., Cooper, D. M., & Patterson, D. J. (2012). Augmenting gesture recognition with erlang-cox models to identify neurological disorders in premature babies. In Proceedings of the 2012 ACM conference on ubiquitous computing (UbiComp ‘12) (pp. 411–420). New York, NY: ACM Press.

    Google Scholar 

  • Fogarty, J., Hudson, S. E., Atkeson, C. G., Avrahami, D., Forlizzi, J., Kiesler, S., et al. (2005). Predicting human interruptibility with sensors. ACM Transactions on Computer-Human Interaction, 12(1), 119–146.

    CrossRef  Google Scholar 

  • Fox, D., Hightower, J., Kautz, H., Liao, L., & Patterson, D. J. (2003). Bayesian techniques for location estimation. In M. Hazas, J. Scott, & J. Krumm (Eds.), Research paper presented at the 2003 Workshop on Location-Aware Computing, held in conjunction with the Fifth International Conference on Ubiquitous Computing (UbiComp 2003), Seattle, WA, USA, October 12, 2003.

    Google Scholar 

  • Froehlich, J. E., Larson, E., Campbell, T., Haggerty, C., Fogarty, J., & Patel, S. N. (2009). HydroSense: Infrastructure-mediated single-point sensing of whole-home water activity. In Proceedings of the 11th international conference on ubiquitous computing (Ubicomp 2009) (pp. 235–244). New York, NY: ACM Press.

    Google Scholar 

  • Froehlich, J., Larson, E., Saba, E., Campbell, T., Atlas, L., Fogarty, J., et al. (2011). A longitudinal study of pressure sensing to infer real-world water usage events in the home. In Proceedings of the 9th international conference on pervasive computing (PERVASIVE 2011) (pp. 50–69). Berlin: Springer.

    Google Scholar 

  • Greenberg, S., & Fitchett, C. (2001). Phidgets: Each development of physical interfaces through physical widgets. In Proceedings of the 14th annual ACM symposium on user interface software and technology (UIST 2001) (pp. 209–218). New York, NY: ACM Press.

    CrossRef  Google Scholar 

  • Gupta, S., Reynolds, M. S., & Patel, S. N. (2010). ElectriSense: Single-point sensing using EMI for electrical event detection and classification in the home. In Proceedings of the 12th international conference on ubiquitous computing (UbiComp 2010) (pp. 139–148). New York, NY: ACM Press.

    CrossRef  Google Scholar 

  • Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., & Witten, I. H. (2009). The WEKA data mining software: An update. SIGKDD Explorations, 11(1), 10–18.

    CrossRef  Google Scholar 

  • Hightower, J., (2003). From position to place. Research paper presented at the 2003 Workshop on Location-Aware Computing, held in conjunction with the Fifth International Conference on Ubiquitous Computing (UbiComp 2003), Seattle, WA, USA, October 12, 2003.

    Google Scholar 

  • Hightower, J. & Borriello, G. (2001). A survey and taxonomy of location systems for ubiquitous computing. Technical Report UW-CSE-01-08-03, Intel Research Seattle and the University of Washington, Seattle, WA.

    Google Scholar 

  • Hightower, J. & Borriello, G. (2001, August). Location systems for ubiquitous computing. IEEE Computer, 34(8), 57–66.

    Google Scholar 

  • Hong, J.-H., Suh, E., & Kim, S.-J. (2009). Context-aware systems: A literature review and classification. Expert Systems with Applications, 36(4), 8509–8522.

    CrossRef  Google Scholar 

  • Hornecker, E., & Nicol, E. (2012). What do lab-based user studies tell us about in-the-wild behavior? Insights from a study of museum interactives. In Proceedings of the ACM conference on designing interactive systems (DIS ’12) (pp. 358–367). New York, NY: ACM Press.

    CrossRef  Google Scholar 

  • Horvitz, E., Koch, P., & Apacible, J. (2004). BusyBody: Creating and fielding personalized models of the cost of interruption. In Proceedings of the ACM conference on computer supported cooperative work (CSCW ’04) (pp. 507–510). New York, NY: ACM Press.

    Google Scholar 

  • Hutchings, D. R., Smith, G., Meyers, B., Czerwinski, M., & Robertson, G. (2004). Display space usage and window management operation comparisons between single monitor and multiple monitor users. In Proceedings of the working conference on advanced visual interfaces (AVI ’04) (pp. 32–39). New York, NY: ACM Press.

    CrossRef  Google Scholar 

  • Hutchinson, H., Mackay, W., Westerlund, B., Bederson, B. B., Druin, A., Plaisant, C., et al. (2003). Technology probes: Inspiring design for and with families. In Proceedings of the SIGCHI conference on human factors in computing systems (CHI ’03) (pp. 17–24). New York, NY: ACM Press.

    Google Scholar 

  • Intille, S. S. (2002). Designing a home of the future. IEEE Pervasive Computing, 1(2), 76–82.

    CrossRef  Google Scholar 

  • Intille, S., Larson, K., Tapia, E., Beaudin, J., Kaushik, P., Nawyn, J., et al. (2006). Using a live-in laboratory for ubiquitous computing research. In Proceedings of the 4th international conference on pervasive computing (PERVASIVE 2006) (pp. 349–365). Berlin: Springer.

    Google Scholar 

  • Intille, S. S., Rondoni, J., Kukla, C., Ancona, I., & Bao, L. (2003). A context-aware experience sampling tool. In Extended abstracts of the SIGCHI conference on human factors in computing systems (CHI ’03) (pp. 972–973). New York, NY: ACM Press.

    CrossRef  Google Scholar 

  • Isaacman, S., Becker, R., Cáceres, R., Martonosi, M., Rowland, J., Varshavsky, A., et al. (2012). Human mobility modeling at metropolitan scales. In Proceedings of the 10th international conference on mobile systems, applications, and services (MobiSys ’12) (pp. 239–252). New York, NY: ACM Press.

    CrossRef  Google Scholar 

  • Kaptelinin, V. (2003). UMEA: Translating interaction histories into project contexts. In Proceedings of the SIGCHI conference on human factors in computing systems (CHI ’03) (pp. 353–360). New York, NY: ACM Press.

    Google Scholar 

  • Kidd, C., Orr, R., Abowd, G., Atkeson, C., Essa, I., MacIntyre, B., et al. (1999). The aware home: A living laboratory for ubiquitous computing research. In Proceedings of the second international workshop on cooperative buildings (CoBuild ’99) (pp. 191–198). Berlin: Springer.

    Google Scholar 

  • Kientz, J. A., Patel, S. N., Jones, B., Price, E., Mynatt, E. D., & Abowd, G. D. (2008). The Georgia Tech aware home. In Extended abstracts of the SIGCHI conference on human factors in computing systems (CHI 2008) (pp. 3675–3680). New York, NY: ACM Press.

    Google Scholar 

  • Klasnja, P., Consolvo, S., Choudhury, T., Beckwith, R., & Hightower, J. (2009). Exploring privacy concerns about personal sensing. In Proceedings of the 7th international conference on pervasive computing (PERVASIVE 2009) (pp. 176–183). Berlin: Springer.

    Google Scholar 

  • Krumm, J. (2010). Processing sequential sensor data. In J. Krumm (Ed.), Ubiquitous computing fundamentals (pp. 286–319). Boca Raton, FL: CRC Press.

    Google Scholar 

  • LaMarca, A., Chawathe, Y., Consolvo, S., Hightower, J., Smith, I., Scott, J., et al. (2005). Place Lab: Device positioning using radio beacons in the wild. In Proceedings of the third international conference on pervasive computing (PERVASIVE 2005) (pp. 301–306). Berlin: Springer.

    Google Scholar 

  • Langheinrich, M. (2010). Privacy in ubiquitous. In J. Krumm (Ed.), Ubiquitous computing fundamentals (pp. 286–319). Boca Raton, FL: CRC Press.

    Google Scholar 

  • Langley, P. (2000). Crafting papers on machine learning. In Proceedings of the seventeenth international conference on machine learning (ML ’00) (pp. 1207–1211). Stanford, CA: Morgan Kaufmann.

    Google Scholar 

  • Larson, R., & Csikszentmihalyi, M. (1983). The experience sampling method. New Directions for Methodology of Social and Behavioral Science, 15, 41–56.

    Google Scholar 

  • Liao, L., Patterson, D. J., Fox, D., & Kautz, H. (2006, December). Building personal maps from GPS data. Annals of the New York Academy of Sciences, 1093, 249–265.

    Google Scholar 

  • Liao, L., Patterson, D. J., Fox, D., & Kautz, H. (2007, January). Learning and inferring transportation routines. Artificial Intelligence, 171, 311–331.

    Google Scholar 

  • MacIntyre, B., Mynatt, E. D., Voida, S., Hansen, K. M., Tullio, J., & Corso, G. M. (2001). Support for multitasking and background awareness using interactive peripheral displays. In Proceedings of the 14th annual ACM symposium on user interface software and technology (UIST ’01) (pp. 41–50). New York, NY: ACM Press.

    CrossRef  Google Scholar 

  • Mark, G. J., Voida, S., & Cardello, A. V. (2012). “A pace not dictated by electrons”: An empirical study of work without email. In Proceedings of the SIGCHI conference on human factors in computing systems (CHI 2012) (pp. 555–564). New York, NY: ACM Press.

    Google Scholar 

  • Marmasse, N., & Schmandt, C. (2000). Location-aware information delivery with ComMotion. In Proceedings of the 2nd international symposium on handheld and ubiquitous computing (HUC ’00) (pp. 157–171). London: Springer.

    CrossRef  Google Scholar 

  • Miluzzo, E., Lane, N. D., Fodor, K., Peterson, R., Lu, H., Musolesi, M., et al. (2008). Sensing meets mobile social networks: The design, implementation and evaluation of the CenseMe application. In Proceedings of the 6th ACM conference on embedded network sensor systems (SenSys ’08) (pp. 337–350). New York, NY: ACM Press.

    CrossRef  Google Scholar 

  • Monibi, M., & Patterson, D. (2009). Getting places: Collaborative predictions from status. In Proceedings of the European conference on ambient intelligence (AmI 2009) (pp. 60–65). Berlin: Springer.

    CrossRef  Google Scholar 

  • Mynatt, E. D., Rowan, J., Craighill, S., & Jacobs, A. (2001). Digital family portraits: Supporting peace of mind for extended family members. In Proceedings of the SIGCHI conference on human factors in computing systems (CHI 2001) (pp. 333–340). New York, NY: ACM Press.

    CrossRef  Google Scholar 

  • Nair, R., Voida, S., & Mynatt, E. D. (2005). Frequency-based detection of task switches. In Proceedings of the 19th annual conference of the British HCI group (HCI 2005) (pp. 94–99). Berlin: Springer.

    Google Scholar 

  • Olguín, D. O., & Pentland, A. (2008). Social sensors for automatic data collection. In Proceedings of the 14th annual Americas conference on information systems (AMCIS 2008) (p. Paper 171). Atlanta, GA: Association for Information Systems.

    Google Scholar 

  • Olguín, D. O., Waber, B. N., Kim, T., Mohan, A., Ara, K., & Pentland, A. (2009). Sensible organizations: Technology and methodology for automatically measuring organizational behavior. IEEE Trans. On Systems, Man, and Cybernetics – Part B: Cybernetics, 39(1), 12 pages.

    CrossRef  Google Scholar 

  • Orr, R. J., & Abowd, G. D. (1999). The smart floor: A mechanism for natural user identification and tracking. In Extended abstracts of the SIGCHI conference on human factors in computing systems (CHI 1999) (pp. 275–276). New York, NY: ACM Press.

    Google Scholar 

  • Patel, S. N., Kientz, J. A., Hayes, G. R., Bhat, S., & Abowd, G. D. (2006). Farther than you may think: An empirical investigation of the proximity of users to their mobile phones. In Proceedings of the 8th international conference on ubiquitous computing (UbiComp 2006) (pp. 123–140). Berlin: Springer.

    CrossRef  Google Scholar 

  • Patel, S. N., Reynolds, M. S., & Abowd, G. D. (2008). Detecting human movement by differential air pressure sensing in HVAC system ductwork: An exploration in infrastructure mediated sensing. In Proceedings of the 7th international conference on pervasive computing (PERVASIVE 2008) (pp. 1–18). Berlin: Springer.

    Google Scholar 

  • Patel, S. N., Robertson, T., Kientz, J. A., Reynolds, M. S., & Abowd, G. D. (2007). At the flick of a switch: Detecting and classifying unique electrical events on the residential power line. In Proceedings of the 9th international conference on ubiquitous computing (UbiComp 2007) (pp. 271–288). Berlin: Springer.

    CrossRef  Google Scholar 

  • Patel, S. N., Truong, K. N., & Abowd, G. D. (2006). PowerLine positioning: A practical sub-room-level indoor location system for domestic use. In Proceedings of the 8th international conference on ubiquitous computing (UbiComp 2006) (pp. 441–458). Berlin: Springer.

    CrossRef  Google Scholar 

  • Patterson, D. J. (2009). Global priors of place and activity tags. In Proceedings of the AAAI spring symposium on human behavior modeling (pp. 75–79). Palo Alto, CA: AAAI Press.

    Google Scholar 

  • Patterson, D. J., Baker, C., Ding, X., Kaufman, S. J., Liu, K., & Zaldivar, A. (2008). Online everywhere: Evolving mobile instant messaging practices. In Proceedings of the 10th international conference on ubiquitous computing (UbiComp ’08) (pp. 64–73). New York, NY: ACM Press.

    CrossRef  Google Scholar 

  • Patterson, D. J., Ding, X., Kaufman, S. J., Liu, K., & Zaldivar, A. (2009). An ecosystem for learning and using sensor-driven IM status messages. IEEE Pervasive Computing, 8(4), 42–49.

    CrossRef  Google Scholar 

  • Patterson, D. J., Fox, D., Kautz, H., & Philipose, M. (2005). Fine-grained activity recognition by aggregating abstract object usage. In Proceedings of the ninth IEEE international symposium on wearable computers (ISWC ’05) (pp. 44–51). Los Alamitos, CA: IEEE Computer Society.

    CrossRef  Google Scholar 

  • Patterson, D., Liao, L., Fox, D., & Kautz, H. (2003). Inferring high-level behavior from low-level sensors. In Proceedings of the 5th international conference on ubiquitous computing (UbiComp 2003) (pp. 73–89). Berlin: Springer.

    CrossRef  Google Scholar 

  • Perkowitz, M., Philipose, M., Fishkin, K., & Patterson, D. J. (2004). Mining models of human activities from the web. In Proceedings of the 13th international conference on World Wide Web (WWW ’04) (pp. 573–582). New York, NY: ACM Press.

    CrossRef  Google Scholar 

  • Philipose, M., Fishkin, K. P., Perkowitz, M., Patterson, D. J., Fox, D., Kautz, H., et al. (2004). Inferring activities from interactions with objects. IEEE Pervasive Computing, 3(4), 50–57.

    CrossRef  Google Scholar 

  • Poh, M.-Z., Swenson, N. C., & Picard, R. W. (2010). A wearable sensor for unobtrusive, long-term assessment of electrodermal activity. IEEE Transactions on Biomedical Engineering, 57(5), 1243–1252.

    CrossRef  Google Scholar 

  • Rowan, J., & Mynatt, E. D. (2005). Digital family portrait field trial: Support for aging in place. In Proceedings of the SIGCHI conference on human factors in computing systems (CHI 2005) (pp. 521–530). New York, NY: ACM Press.

    CrossRef  Google Scholar 

  • Salber, D., Dey, A. K., & Abowd, G. D. (1999). The context toolkit: Aiding the development of context-aware applications. In Proceedings of the SIGCHI conference on human factors in computing systems (CHI 1999) (pp. 434–441). New York, NY: ACM Press.

    Google Scholar 

  • Sparacino, F. (2003). Sto(ry)chastics: A Bayesian network architecture for user modeling and computational storytelling for interactive spaces. In Proceedings of the 5th international conference on ubiquitous computing (UbiComp 2003) (pp. 54–72). Berlin: Springer.

    CrossRef  Google Scholar 

  • Stone, A. A., Shiffman, S. S., & DeVries, M. W. (1999). Ecological momentary assessment. In D. Kahneman, E. Diener, & N. Schwarz (Eds.), Well-being: The foundations of hedonic psychology (pp. 26–39). New York, NY: Russell Sage.

    Google Scholar 

  • Stumpf, S., Bao, X., Dragunov, A., Dietterich, T. G., Herlocker, J., Johnsrude, K., Li, L., & Shen, J. (2005). Predicting user tasks: I know what you’re doing! Research paper presented at the 20th national conference on artificial intelligence (AAAI-05) workshop on human comprehensible machine learning, Pittsburgh, PA, USA, July 9–13.

    Google Scholar 

  • Tang, J., & Patterson, D. (2010). Twitter, sensors and UI: Robust context modeling for interruption management. In Proceedings of the 18th international conference on user modeling, adaptation, and personalization (UMAP 2010) (pp. 123–134). Berlin: Springer.

    CrossRef  Google Scholar 

  • Tapia, E., Intille, S., & Larson, K. (2004). Activity recognition in the home using simple and ubiquitous sensors. In Proceedings of the second international conference on pervasive computing (PERVASIVE 2004) (pp. 158–175). Berlin: Springer.

    Google Scholar 

  • Varshavsky, A., & Patel, S. (2009). Location in ubiquitous computing. In J. Krumm (Ed.), Ubiquitous computing fundamentals (pp. 286–319). Boca Raton, FL: CRC Press.

    Google Scholar 

  • Villar, N., Scott, J., & Hodges, S. (2011). Prototyping with .NET Gadgeteer. In Proceedings of the 5th international conference on tangible, embedded, and embodied interaction (TEI 2011) (pp. 377–380). New York, NY: ACM Press.

    CrossRef  Google Scholar 

  • Want, R., Hopper, A., Falcão, V., & Gibbons, J. (1992). The active badge location system. ACM Transactions on Information Systems, 10(1), 91–102.

    CrossRef  Google Scholar 

  • Ward, J. A., Lukowicz, P., & Gellersen, H. W. (2011). Performance metrics for activity recognition. ACM Transactions on Intelligent Systems and Technology, 2(1), Article 6.

    CrossRef  Google Scholar 

  • Weiser, M. (1991). The computer for the 21st century. Scientific American, 265(3), 94–104.

    CrossRef  Google Scholar 

  • Westyn, T., Brashear, H., Atrash, A., & Starner, T. (2003). Georgia tech gesture toolkit: Supporting experiments in gesture recognition. In Proceedings of the 5th international conference on multimodal interfaces (ICMI ’03) (pp. 85–92). New York, NY: ACM Press.

    CrossRef  Google Scholar 

  • Wyatt, D., Choudhury, T., Bilmes, J., & Kitts, J. A. (2011). Inferring colocation and conversation networks from privacy-sensitive audio with implications for computational social science. ACM Transactions on Intelligent Systems and Technology, 2(1), Article 7.

    CrossRef  Google Scholar 

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Voida, S., Patterson, D.J., Patel, S.N. (2014). Sensor Data Streams. In: Olson, J., Kellogg, W. (eds) Ways of Knowing in HCI. Springer, New York, NY. https://doi.org/10.1007/978-1-4939-0378-8_12

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