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Event Reconstructing Adaptive Spectral Evaluation (ERASE) Approach to Removing Noise in Structural Acceleration Signals

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Abstract

Floor vibrations for event localization has gained attention recently for its human-related applications such as footstep tracking. However, noise can corrupt signals, reduce signal-to-noise ratios (SNR), and lead to imprecise estimations of the event’s amplitude and force. Techniques to remove noise have been developed such as bandpass filters, which eliminate noise without regard to overlapping event frequency components. These methods can corrupt the signal, removing important information about the event. The authors propose adapting a common speech processing technique, called spectral subtraction using half wave rectification, to remove only the noise’s contribution. The Event Reconstructing Adaptive Spectral Evaluation (ERASE) approach is compared to unfiltered and Butterworth-filtered data in impact localization and force estimation through the Force Estimation and Event Localization (FEEL) Algorithm. A total of 810 impacts from ball drops of five different heights and impulse hammers across eighteen locations were utilized for testing. Signals were corrupted by noise from different sources. ERASE demonstrated 93.9% average impact localization accuracy and -2.40% ± 1.85% force magnitude error on a 99% confidence interval, improving the SNR verse the other filtering techniques.

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Data Availability

All data, models, or code generated or used during the study are proprietary or confidential in nature.

References

  1. Zhang J, Lutes R G, Liu G, Brambley M R (2013) Energy savings for occupancy-based control (obc) of variable-air-volume (vav) systems. Technical report, Pacific Northwest National Lab.(PNNL), Richland, WA (United States)

  2. Pan S, Yu T, Mirshekari M, Fagert J, Bonde A, Mengshoel O J, Noh H Y, Zhang P (2017) Footprintid: indoor pedestrian identification through ambient structural vibration sensing. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 1(3):1–31

    Article  Google Scholar 

  3. Oguchi K, Iwago M (2018) Human localization in the home by using floor-mounted acceleration sensors. In: 2018 IEEE SENSORS. IEEE, pp 1–4

  4. Ekimov A, Sabatier J M (2006) Vibration and sound signatures of human footsteps in buildings. J Acoust Soc Amer 118(3):2021–768

    Google Scholar 

  5. Li F, Clemente J, Valero M, Tse Z, Li S, Song W (2019) Smart home monitoring system via footstep induced vibrations. IEEE Syst J

  6. Mirshekari M, Pan S, Zhang P, Noh H Y (2016) Characterizing wave propagation to improve indoor step-level person localization using floor vibration. In: Sensors and smart structures technologies for civil, mechanical, and aerospace systems 2016, vol 9803. International Society for Optics and Photonics, p 980305

  7. Bagalà F, Becker C, Cappello A, Chiari L, Aminian K, Hausdorff J M, Zijlstra W, Klenk J (2012) Evaluation of accelerometer-based fall detection algorithms on real-world falls. PloS one 7 (5):e37062

    Article  Google Scholar 

  8. Bourke AK, Van de Ven P, Gamble M, O’Connor R, Murphy K, Bogan E, McQuade E, Finucane P, Olaighin G, Nelson J (2010) Evaluation of waist-mounted tri-axial accelerometer based fall-detection algorithms during scripted and continuous unscripted activities. J Biomech 43(15):3051–3057

    Article  CAS  Google Scholar 

  9. Aziz O, Musngi M, Park E J, Mori G, Robinovitch S N (2017) A comparison of accuracy of fall detection algorithms (threshold-based vs. machine learning) using waist-mounted tri-axial accelerometer signals from a comprehensive set of falls and non-fall trials. Med Biol Eng Comput 55(1):45–55

    Article  Google Scholar 

  10. Perry J T, Kellog S, Vaidya S M, Youn J-H, Ali H, Sharif H (2009) Survey and evaluation of real-time fall detection approaches. In: 2009 6th International symposium on high capacity optical networks and enabling technologies (HONET). IEEE, pp 158–164

  11. Ren L, Peng Y (2019) Research of fall detection and fall prevention technologies: a systematic review. IEEE Access 7:77702–77722

    Article  Google Scholar 

  12. Dong Y, Zou J J, Liu J, Fagert J, Mirshekari M, Lowes L, Iammarino M, Zhang P, Noh H Y (2020) Md-vibe: physics-informed analysis of patient-induced structural vibration data for monitoring gait health in individuals with muscular dystrophy. In: Adjunct proceedings of the 2020 ACM international joint conference on pervasive and ubiquitous computing and proceedings of the 2020 ACM international symposium on wearable computers, pp 525–531

  13. Fagert J, Mirshekari M, Pan S, Zhang P, Noh H Y (2020) Structural property guided gait parameter estimation using footstep-induced floor vibrations, vol 2. Springer, pp 191–194

  14. MejiaCruz Y, Franco J, Hainline G, Fritz S, Jiang Z, Caicedo J M, Davis B, Hirth V (2021) Walking speed measurement technology: a review. Current Geriatrics Reports, 1–10

  15. Davis B T, Caicedo J M, Hirth V A (2020) Force estimation and event localization (feel) of impacts using structural vibrations. J Eng Mech. https://doi.org/10.1061/(ASCE)EM.1943-7889.0001890, In Press

  16. MejiaCruz Y, Jiang Z, Caicedo J M, Franco J M (2021) Probabilistic force estimation and event localization (pfeel) algorithm. Eng Struct. https://doi.org/10.1016/j.engstruct.2021.113535

  17. Alajlouni S, Tarazaga P (2019) A new fast and calibration-free method for footstep impact localization in an instrumented floor. J Vib Control 25(10):1629–1638

    Article  Google Scholar 

  18. Davis B T (2016) Characterization of human-induced vibrations. Ph.D. Thesis, University of South Carolina

  19. Giurgiutiu V (2014) Structural health monitoring: with piezoelectric wafer active sensors, 2nd edn. Elsevier

  20. Selesnick I W, Burrus C S (1998) Generalized digital butterworth filter design. IEEE Trans Signal Process 46(6):1688–1694

    Article  Google Scholar 

  21. Abdelgawad A, Mahmud M A, Yelamarthi K (2016) Butterworth filter application for structural health monitoring. Int J Handheld Comput Res (IJHCR) 7(4):15–29

    Article  Google Scholar 

  22. Ravizza G, Ferrari R, Rizzi E, Dertimanis V, Chatzi E N (2019) Denoising corrupted structural vibration response: critical comparison and assessment of related methods. In: 7th International conference on computational methods in structural dynamics and earthquake engineering (COMPDYN 2019), an ECCOMAS thematic conference, an IACM special interest conference. 3. Institute of Structural Analysis and Antiseismic Research, pp 1–12

  23. Antoniadis A, Bigot J, Sapatinas T (2001) Wavelet estimators in nonparametric regression: a comparative simulation study. J Stat Softw 6:pp–1

    Article  Google Scholar 

  24. Jiang X, Mahadevan S, Adeli H (2007) Bayesian wavelet packet denoising for structural system identification. Structural Control and Health Monitoring: The Official Journal of the International Association for Structural Control and Monitoring and of the European Association for the Control of Structures 14 (2):333–356

    Article  Google Scholar 

  25. Nason G P, Silverman B W (1995) The stationary wavelet transform and some statistical applications. Springer, pp 281–299

  26. Coifman R R, Donoho D L (1995) Translation-invariant de-noising. Springer, pp 125–150

  27. Grewal M S, Andrews A P (2014) John Wiley & Sons

  28. Chatzi E N, Fuggini C (2015) Online correction of drift in structural identification using artificial white noise observations and an unscented Kalman filter. Smart Struct Syst 16(2):295–328

    Article  Google Scholar 

  29. Marselli C, Daudet D, Amann H P, Pellandini F (1998) Application of kalman filtering to noisereduction on microsensor signals. In: Proceedings du Colloque interdisciplinaire en instrumentation, C2I, 18-19 novembre 98, pp 443–450. Ecole Normale Supérieure de Cachan, France, pp 443–450

  30. Zhao M, Jia X (2017) A novel strategy for signal denoising using reweighted svd and its applications to weak fault feature enhancement of rotating machinery. Mech Syst Signal Process 94:129–147

    Article  Google Scholar 

  31. Knockaert L, De Backer B, De Zutter D (1999) Svd compression, unitary transforms, and computational complexity. IEEE Trans Signal Process 47(10):2724–2729

    Article  Google Scholar 

  32. Yang W-X, Peter W T (2003) Development of an advanced noise reduction method for vibration analysis based on singular value decomposition. Ndt & E International 36(6):419–432

    Article  Google Scholar 

  33. Gustafsson H, Nordholm S, Claesson I (1999) Spectral subtraction with adaptive averaging of the gain function. In: Sixth European conference on speech communication and technology, pp 10–25

  34. Li S, Wang J-Q, Niu M, Jing X-J, Liu T, et al. (2010) Iterative spectral subtraction method for millimeter-wave conducted speech enhancement. J Biomed Sci Eng 3(02):187

    Article  Google Scholar 

  35. Lu Y, Loizou P C (2008) A geometric approach to spectral subtraction. Speech Commun 50 (6):453–466

    Article  Google Scholar 

  36. Upadhyay N, Karmakar A (2015) Speech enhancement using spectral subtraction-type algorithms: a comparison and simulation study. Procedia Comput Sci 54:574–584

    Article  Google Scholar 

  37. Yamashita K, Ogata S, Shimamura T (2007) Improved spectral subtraction utilizing iterative processing. Electron Commun Japan (Part III: Fund Electron Sci) 90(4):39–51

    Article  Google Scholar 

  38. Lim J S, Oppenheim A V (1979) Enhancement and bandwidth compression of noisy speech. Proc IEEE 67(12):1586–1604

    Article  Google Scholar 

  39. Abd El-Fattah MA, Dessouky M I, Diab S M, Abd El-Samie F E-S (2008) Speech enhancement using an adaptive wiener filtering approach. Progress Electromagn Res 4:167–184

    Article  Google Scholar 

  40. Berouti M, Schwartz R, Makhoul J (1979) Enhancement of speech corrupted by acoustic noise. In: ICASSP’79. IEEE International conference on acoustics, speech, and signal processing, vol 4. IEEE, pp 208–211

  41. Kamath S, Loizou P, et al. (2002) A multi-band spectral subtraction method for enhancing speech corrupted by colored noise.. In: ICASSP, vol 4. Citeseer, pp 44164–44164

  42. Lim J S (1990) Two-dimensional signal and image processing. Englewood Cliffs

  43. Chen J, Benesty J, Huang Y, Doclo S (2006) New insights into the noise reduction wiener filter. IEEE Trans Audio Speech Lang Process 14(4):1218–1234

    Article  Google Scholar 

  44. Zhang Y, Pan S, Fagert J, Mirshekari M, Noh H Y, Zhang P, Zhang L (2018) Occupant activity level estimation using floor vibration. In: Proceedings of the 2018 ACM international joint conference and 2018 international symposium on pervasive and ubiquitous computing and wearable computers, pp 1355–1363

  45. Huston S P (2010) Structural health monitoring of a high speed naval vessel using ambient vibrations. Ph.D. Thesis, Georgia Institute of Technology

  46. Gunawan F E (2016) Impact force reconstruction using the regularized wiener filter method. Inverse Probl Sci Eng 24(7):1107–1132

    Article  Google Scholar 

  47. Ogata S, Shimamura T (2001) Reinforced spectral subtraction method to enhance speech signal. In: Proceedings of IEEE region 10 international conference on electrical and electronic technology. TENCON 2001 (Cat. No. 01CH37239), vol 1. IEEE, pp 242–245

  48. Na S, Li W, Liu Y (2016) An improved spectral subtraction algorithm for speech enhancement system. In: 6th International conference on information engineering for mechanics and materials. Atlantis Press

  49. Boll S (1979) Suppression of acoustic noise in speech using spectral subtraction. IEEE Trans Acoust Speech Signal Process 27(2):113–120

    Article  Google Scholar 

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Acknowledgments

The authors would like to thank Parker Kelly and Brianna Bryant for assisting in collecting the data used in this work.

Funding

Research reported in this document was supported by the National Institute on Aging (NIA) of the National Institutes of Health (NIH) under Award Number R41AG059475. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institute of Health.

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Authors and Affiliations

  1. Department of Civil and Environmental Engineering, University of South Carolina, 300 Main St, Columbia, 29208, SC, United States

    Y. MejiaCruz

  2. Advanced Smart Systems and Evaluation Technologies (ASSET), LLC, 400 Laurel Street, Suite 1B, Columbia, 29201, SC, United States

    Y. MejiaCruz & B.T. Davis

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  1. Y. MejiaCruz
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Correspondence to Y. MejiaCruz.

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MejiaCruz, Y., Davis, B. Event Reconstructing Adaptive Spectral Evaluation (ERASE) Approach to Removing Noise in Structural Acceleration Signals. Exp Tech 47, 827–837 (2023). https://doi.org/10.1007/s40799-022-00598-x

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