Abstract
Wi-Fi and acoustic signal-based human–computer interaction (HCI) methods have received growing attention in academia. However, there still are issues to be addressed despite their flourishing. In this work, we evaluate the practicality of the state-of-the-art signal-based HCI research in terms of the following six aspects—granularity, robustness, usability, efficiency, stability, and deployability. The paper presents our analysis results, observations and prospective research directions. We believe that this work will serve as a standard for future signal-based HCI research for assessing the practicality of newly developed methods.
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References
Abdelnasser H, Harras KA, Youssef M (2015) Ubibreathe: A ubiquitous non-invasive WiFi-based breathing estimator. In: Proceedings of the 16th ACM international symposium on mobile ad hoc networking and computing, pp 277–286, https://doi.org/10.1145/2746285.2755969
Abdelnasser H, Youssef M, Harras KA (2015) WiGest: a ubiquitous WiFi-based gesture recognition system. In: Proceedings of the 2015 IEEE conference on computer communications (INFOCOM). https://doi.org/10.1109/INFOCOM.2015.7218525
Adib F, Katabi D (2013) See through walls with WiFi!. SIGCOMM Comput Commun Rev 43(4):75–86. https://doi.org/10.1145/2534169.2486039
Adib F, Hsu CY, Mao H, Katabi D, Durand F (2015) Capturing the human figure through the wall. ACM Trans Graph (TOG). https://doi.org/10.1145/2816795.2818072
Ali K, Liu AX, Wang W, Shahzad M (2015) Understanding and modeling of WiFi signal based human activity recognition. In: Proceedings of the 21st annual international conference on mobile computing and networking, pp 90–102. https://doi.org/10.1145/2789168.2790109
Bahl P, Padmanabhan VN (2000) Radar: an in-building RF-based user location and tracking system. In: Proceedings of the IEEE INFOCOM 2000 conference on computer communications. 19th Annual joint conference of the IEEE computer and communications societies (Cat. No. 00CH37064), vol 2, pp 775–784 vol 2. https://doi.org/10.1109/INFCOM.2000.832252
Chem L, Hoey J, Nugent CD, Cook DJ, Yu Z (2012) Sensor-based activity recognition. IEEE Trans Syst Man Cybern Part C (Appl Rev) 42:790–808. https://doi.org/10.1109/TSMCC.2012.2198883
Chen VC, Li F, Ho SS, Wechsler H (2006) Micro-doppler effect in radar: phenomenon, model, and simulation study. IEEE Trans Aerosp Electron Syst 42:2–21. https://doi.org/10.1109/TAES.2006.1603402
IEEE (2009) Part 11: Wireless LAN medium access control (MAC) and physical layer (PHY) specification. IEEE Std 80211n-2009 pp 1–565, https://doi.org/10.1109/IEEESTD.2009.5307322
Halperin D, Hu W, Sheth A, Wetherall D (2011) Tool release: gathering 802.11n traces with channel state information. SIGCOMM Comput Commun Rev 41(1):53–53. https://doi.org/10.1145/1925861.1925870
Hassenzahl M, Burmester M, Koller F (2003) Attrakdiff: Ein fragebogen zur messung wahrgenommener hedonischer und pragmatischer qualität. In: Mensch & computer, pp 187–96. https://doi.org/10.1007/978-3-322-80058-9_19
Huang D, Nandakumar R, Gollakota S (2014) Feasibility and limits of Wi-Fi imaging. In: Proceedings of the 12th ACM conference on embedded network sensor systems, ACM, New York, NY, USA, SENSYS ’14, pp 266–279. https://doi.org/10.1145/2668332.2668344
ISO (1999) ISO 9241. Ergonomics of human system interaction, international organization for standardization
ISO, IEC, (2001) ISO/IEC 9126. Software engineering—Product quality, ISO/IEC
Li M, Meng Y, Liu J, Zhu H, Liu Y, Ruan N (2016) When CSI meets public WiFi: inferring your mobile phone passwords via WiFi signals. In: Proceedings of the 2016 ACM SIGSAC conference on computer and communications security, pp 1068–1079. https://doi.org/10.1145/2976749.2978397
Liu H, Darabi H, Banerjee P, Liu J (2007) Survey of wireless indoor positioning techniques and systems. IEEE Trans Syst Man Cybern Part C (Appl Rev) 37(6):1067–1080. https://doi.org/10.1109/TSMCC.2007.905750
Liu J, Wang Y, Kar G, Chen Y, Yang J, Gruteser M (2015) Snooping keystrokes with mm-level audio ranging on a single phone. In: Proceedings of the 21st Annual international conference on mobile computing and networking, pp 142–154. https://doi.org/10.1145/2789168.2790122
Ma J, Wang H, Zhang D, Wang Y, Wang Y (2016) A survey on Wi-Fi based contactless activity recognition. In: Proceedings of the 2016 International IEEE conference on ubiquitous intelligence and computing, advanced and trusted computing, scalable computing and communications, cloud and big data computing, internet of people, and smart world congress (UIC/ATC/ScalCom/CBDCom/IoP/SmartWorld), pp 1086–1091. https://doi.org/10.1109/UIC-ATC-ScalCom-CBDCom-IoP-SmartWorld.2016.0170
McNeill D (2006) Gesture and communication. In: Brown K (ed) Encyclopedia of language & linguistics, 2nd edn. Elsevier, Oxford, pp 58–66
Melgarejo P, Zhang X, Ramanathan P, Chu D (2014) Leveraging directional antenna capabilities for fine-grained gesture recognition. In: Proceedings of the 2014 ACM International joint conference on pervasive and ubiquitous computing, pp 541–551. https://doi.org/10.1145/2632048.2632095
Moeslund TB, Granum E (2001) A survey of computer vision-based human motion capture. Comput Vis Image Underst 81(3):231–268. https://doi.org/10.1006/cviu.2000.0897
Nandakumar R, Iyer V, Tan D, Gollakota S (2016) Fingerio: using active sonar for fine-grained finger tracking. In: Proceedings of the 34th Annual ACM conference on human factors in computing systems, pp 1515–1525. https://doi.org/10.1145/2858036.2858580
Pu Q, Gupta S, Gollakota S, Patel S (2013) Whole-home gesture recognition using wireless signals. In: Proceedings of the 19th annual international conference on mobile computing and networking, pp 27–38. https://doi.org/10.1145/2500423.2500436
Ruan W, Sheng QZ, Yang L, Gu T, Xu P, Shangguan L (2016) Audiogest: Enabling fine-grained hand gesture detection by decoding echo signal. In: Proceedings of the 2016 ACM international joint conference on pervasive and ubiquitous computing, pp 474–485. https://doi.org/10.1145/2971648.2971736
Schmidt RO (1989) Multiple emitter location and signal parameter estimation. IEEE Trans Antennas Propag 34:276–280. https://doi.org/10.1109/TAP.1986.1143830
Shannon CE (1949) Communication in the presence of noise. Proc IRE 37(1):10–21. https://doi.org/10.1109/JRPROC.1949.232969
Strohmeier P, Burstyn J, Vertegaal R (2015) Effects of display sizes on a scrolling task using a cylindrical smartwatch. In: Proceedings of the 17th international conference on human-computer interaction with mobile devices and services adjunct, ACM, New York, USA, MobileHCI ’15, pp 846–853. https://doi.org/10.1145/2786567.2793710
Sun L, Sen S, Koutsonikolas D, Kim KH (2015) Widraw: Enabling hands-free drawing in the air on commodity WiFi devices. In: Proceedings of the 21st annual international conference on mobile computing and networking, pp 77–89. https://doi.org/10.1145/2789168.2790129
Wang H, Zhang D, Ma J, Wang y, Wang Y, Wu D, Gu T, Xie B (2016) Human respiration detection with commodity WiFi devices: do user location and body orientation matter? In: Proceedings of the 2016 ACM international joint conference on pervasive and ubiquitous computing, pp 363–373. https://doi.org/10.1145/2971648.2971670
Wang J, Zhao K, Zhang X, Peng C (2014) Ubiquitous keyboard for small mobile devices: harnessing multipath fading for fine-grained keystroke localization. In: Proceedings of the 12th annual international conference on Mobile systems, applications, and services, pp 14–27. https://doi.org/10.1145/2594368.2594384
Wang W, Liu AX, Sun K (2016) Device-free gesture tracking using acoustic signals. In: Proceedings of the 22nd annual international conference on mobile computing and networking, pp 82–94. https://doi.org/10.1145/2973750.2973764
Wang W, Liu AX, Shahzad M, Ling K, Lu S (2015) Understanding and modeling of WiFi signal based human activity recognition. In: Proceedings of the 21st annual international conference on mobile computing and networking, pp 65–76. https://doi.org/10.1145/2789168.2790093
Wang W, Liu AX, Shahzad M (2016) Gait recognition using WiFi signals. In: Proceedings of the 2016 ACM international joint conference on pervasive and ubiquitous computing, pp 25–36. https://doi.org/10.1145/2971648.2971744
Wang Y, Liu J, Chen Y, Gruteser M, Yang J, Liu H (2014) E-eyes: device-free location-oriented activity identification using fine-grained WiFi signatures. In: Proceedings of the 20th annual international conference on Mobile computing and networking, pp 617–628. https://doi.org/10.1145/2639108.2639143
Wireless Open-Access Research Platform (WARP) (2013) 802.11 reference design for WARP v3. http://warpproject.org/trac/wiki/802.11
Xi W, Zhao J, Li XY, Zhao K, Tang S, Liu X, Jiang Z (2014) Electronic frog eye: counting crowd using WiFi. In: Proceedings of the 2014 IEEE international conference on computer communications, pp 361–369. https://doi.org/10.1109/INFOCOM.2014.6847958
Xiao J, Wu K, Yi Y, Wang L, Ni LM (2012) Fimd: Fine-grained device-free motion detection. In: 2012 IEEE 18th International conference on parallel and distributed systems, pp 229–235. https://doi.org/10.1109/ICPADS.2012.40
Xie Y, Li Z, Li M (2015) Precise power delay profiling with commodity WiFi. In: Proceedings of the 21st annual international conference on mobile computing and networking, ACM, New York, NY, USA, MobiCom ’15, pp 53–64. https://doi.org/10.1145/2789168.2790124
Xin T, Guo B, Wang Z, Li M, Yu Z, Zhou X (2016) Freesense: Indoor human identification with Wi-Fi signals. In: Proceedings of the 2016 IEEE global communications conference. https://doi.org/10.1109/GLOCOM.2016.7841847
Yousefi S, Narui H, Dayal S, Ermon S, Valaee S (2017) A survey on behavior recognition using wifi channel state information. IEEE Commun Mag 55(10):98–104. https://doi.org/10.1109/MCOM.2017.1700082
Yun S, Chen YC, Zheng H, Qiu L, Mao W (2017) Strata: fine-grained acoustic-based device-free tracking. In: Proceedings of the 15th annual international conference on mobile systems, applications, and services, pp 15–28. https://doi.org/10.1145/3081333.3081356
Zhu T, Ma Q, Zhang S, Liu Y (2014) Context-free attacks using keyboard acoustic emanations. In: Proceedings of the 2014 ACM SIGSAC conference on computer and communications security, pp 453–464. https://doi.org/10.1145/2660267.2660296
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This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIP) (No. 2017R1A2B4010914).
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Jeong, H., Kang, T., Choi, J. et al. A comparative assessment of Wi-Fi and acoustic signal-based HCI methods on the practicality. J Multimodal User Interfaces 14, 123–137 (2020). https://doi.org/10.1007/s12193-019-00315-w
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DOI: https://doi.org/10.1007/s12193-019-00315-w