Abstract
Acoustic Doppler shift estimation is a cost-effective way to implement Human-Computer Interaction applications across existing smart devices such as smart phones and smart spekaers. However, due to the inherent uncertainty principle in the traditional time-frequency analysis, it remains challenging to profile motions accurately and timely. In this paper, phase offset in acoustic OFDM signal is leveraged for developing Phascope, a fine-grained, fast and flexible motion profiling scheme. We evaluate Phascope using simulation and experiment on COTS devices. Sub-millisecond response time is achieved for Phascope in our experiment. Besides, with optimal subcarrier selection and SNR of 30 dB over all subcarriers, Phascope can estimate motion speed of 0.1 m/s with 6.75% root-mean-square error (RMSE) compared to optimized FFT method.
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References
Abdelnasser H, Youssef M, Harras KA (2015) Wigest: a ubiquitous wifi-based gesture recognition system. In: 2015 IEEE Conference on computer communications (INFOCOM). IEEE, pp 1472–1480
Ali K, Liu AX, Wang W, Shahzad M (2015) Keystroke recognition using wifi signals. In: Proceedings of the 21st annual international conference on mobile computing and networking. ACM, pp 90–102
Ashbrook D, Ashbrook D, Lee SH, Lee SH, Patel S (2014) Airlink: sharing files between multiple devices using in-air gestures. In: ACM International Joint Conference on Pervasive and Ubiquitous Computing, pp. 565–569
Aumi MTI, Gupta S, Goel M, Larson E, Patel S (2013) Doplink: using the doppler effect for multi-device interaction. In: Proceedings of the 2013 ACM international joint conference on pervasive and ubiquitous computing. ACM, pp 583–586
Chen H, Li F, Wang Y (2017) Echotrack: acoustic device-free hand tracking on smart phones. In: INFOCOM 2017-IEEE conference on computer communications, IEEE. IEEE, pp 1–9
Cho YS, Kim J, Yang WY, Kang CG (2010) MIMO-OFDM wireless communications with MATLAB. Wiley
Cohen L (1995) Time-frequency analysis, vol 778. Prentice Hall, Englewood Cliffs
Fu B, Gangatharan DV, Kuijper A, Kirchbuchner F, Braun A (2017) Exercise monitoring on consumer smart phones using ultrasonic sensing. In: Proceedings of the 4th international workshop on sensor-based activity recognition and interaction. ACM, p 9
Gao H, Xu X, Yu J, Chen Y, Zhu Y, Xue G, Li M Er: early recognition of inattentive driving leveraging audio devices on smartphones
Golay M (1961) Complementary series. IRE Trans Inf Theory 7(2):82–87
Gupta P, Dallas T (2014) Feature selection and activity recognition system using a single triaxial accelerometer. IEEE Trans Biomed Eng 61(6):1780–1786
Gupta S, Morris D, Patel S, Tan D (2012) Soundwave: using the doppler effect to sense gestures. In: Proceedings of the SIGCHI conference on human factors in computing systems. ACM, pp 1911–1914
Han SH, Lee JH (2005) An overview of peak-to-average power ratio reduction techniques for multicarrier transmission. IEEE Wireless Commun 12(2):56–65
Lara OD, Labrador MA (2013) A survey on human activity recognition using wearable sensors. IEEE Commun Surv Tutor 15(3):1192–1209
Lien J, Gillian N, Karagozler ME, Amihood P, Schwesig C, Olson E, Raja H, Poupyrev I (2016) Soli: ubiquitous gesture sensing with millimeter wave radar. ACM Trans Graph (TOG) 35(4):142
Mannini A, Intille SS, Rosenberger M, Sabatini AM, Haskell W (2013) Activity recognition using a single accelerometer placed at the wrist or ankle. Med Sci Sports Exer 45(11):2193
Mao W, He J, Qiu L (2016) Cat: high-precision acoustic motion tracking. In: International conference on mobile computing and networking, pp 69–81
Nandakumar R, Gollakota S, Watson N (2015) Contactless sleep apnea detection on smartphones. In: International conference on mobile systems, applications, and services, pp 45–57
Nandakumar R, Iyer V, Tan D, Gollakota S (2016) Fingerio: using active sonar for fine-grained finger tracking. In: Proceedings of the 2016 CHI conference on human factors in computing systems. ACM, pp 1515–1525
Panwar M, Dyuthi SR, Prakash KC, Biswas D, Acharyya A, Maharatna K, Gautam A, Naik GR (2017) Cnn based approach for activity recognition using a wrist-worn accelerometer. In: 2017 39th Annual international conference of the IEEE engineering in medicine and biology society (EMBC). IEEE, pp 2438–2441
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 & networking. ACM, pp 27–38
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: ACM international joint conference on pervasive and ubiquitous computing, pp 474–485
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. ACM, pp 77–89
Sun Z, Purohit A, Bose R, Zhang P (2013) Spartacus: spatially-aware interaction for mobile devices through energy-efficient audio sensing. In: Proceeding of the international conference on mobile systems, applications, and services, pp 263–276
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. ACM, pp 65–76
Wang W, Liu AX, Sun K (2016) Device-free gesture tracking using acoustic signals. In: International conference on mobile computing and networking, pp 82–94
Wang X, Huang R, Mao S (2017) Sonarbeat: sonar phase for breathing beat monitoring with smartphones. In: 2017 14th Annual IEEE international conference on sensing, communication, and networking (SECON). IEEE, pp 1–2
Wei T, Zhang X. (2015) mtrack: high-precision passive tracking using millimeter wave radios. In: Proceedings of the 21st annual international conference on mobile computing and networking. ACM, pp 117–129
Yang J, Sidhom S, Chandrasekaran G, Vu T, Liu H, Cecan N, Chen Y, Gruteser M, Martin RP (2011) Detecting driver phone use leveraging car speakers. In: Proceedings of the 17th annual international conference on mobile computing and networking. ACM, pp 97–108
Yang Q, Hao T, Zhao X, Yin L, Zhang S (2015) Dolphin: ultrasonic-based gesture recognition on smartphone platform. In: IEEE International conference on computational science and engineering, pp 1461–1468
Yun S, Chen YC, Qiu L (2015) Turning a mobile device into a mouse in the air. In: International conference on mobile systems, applications, and services, pp 15–29
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. ACM, pp 15–28
Zhang H, Du W, Zhou P, Li M, Mohapatra P (2016) Dopenc: acoustic-based encounter profiling using smartphones. In: International conference on mobile computing and networking, pp 294–307
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Wang, L., Riedel, T., Scholz, M. et al. Phascope: Fine-Grained, Fast, Flexible Motion Profiling based on Phase Offset in Acoustic OFDM Signal. Mobile Netw Appl 25, 537–550 (2020). https://doi.org/10.1007/s11036-019-01370-z
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DOI: https://doi.org/10.1007/s11036-019-01370-z