access icon free Denoising MEMS accelerometer sensors based on L2-norm total variation algorithm

A method for denoising accelerometer data based on the L2-norm total variation (LTV) algorithm is presented. In order to collect accelerometer data, a wireless accelerometer sensor was developed that is directly connected to a central node. By benefiting from the LTV algorithm, the obtained signals from the accelerometer are denoised. The proposed method is tested by denoising in different accelerometer signals and the results are evaluated by signal-to-noise ratio and power spectral density functions of the signals. The obtained results reveal that noise reduction has been implemented satisfactorily. Hence, the measurement accuracy of accelerometer signals for the proposed method have improved ∼4–10% than other the three low-pass filters including Savitzky–Golay, equiripple-pass-band and Butterworth.

Inspec keywords: health care; accelerometers; wireless sensor networks; microsensors; signal denoising

Other keywords: noise reduction; wireless accelerometer sensor; power spectral density function; equiripple-pass-band low pass filter; Butterworth low pass filter; L2-norm total variation algorithm; human movement assessment; Savitzky-Golay low pass filter; denoising MEMS accelerometer sensor; signal-to-noise ratio; accelerometer signal denoising; healthcare application; LTV algorithm

Subjects: Sensing and detecting devices; Signal processing and detection; MEMS and NEMS device technology; Microsensors and nanosensors; Velocity, acceleration and rotation measurement; Micromechanical and nanomechanical devices and systems; Velocity, acceleration and rotation measurement

References

    1. 1)
    2. 2)
    3. 3)
    4. 4)
    5. 5)
      • 7. Abbasi-kesbi, R., Nikfarjam, A.: ‘A mini wearable wireless sensor for rehabilitation applications’. Third RSI Int. Conf. on Robotics and Mechatronics (ICROM), Tehran, Iran, October 2015, pp. 618622.
    6. 6)
    7. 7)
    8. 8)
      • 11. Zhongshen, L.: ‘The design of Butterworth lowpass filter based on MATLAB’, Heilongjiang Electron. Technol., 2003, 3, p. 017.
    9. 9)
    10. 10)
    11. 11)
    12. 12)
    13. 13)
http://iet.metastore.ingenta.com/content/journals/10.1049/el.2016.3811
Loading

Related content

content/journals/10.1049/el.2016.3811
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading