Sensor Data Analysis Using Moving Average Filter and 256-Point FFT for Wireless Sensor Networks

Chauhan, M. L.; Thorwe, Pratik; Mukherjee, Monodeep J.; Rao, Y. S.

doi:10.1109/icccnt.2018.8493974

Cited by 1 publication

(2 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, the date of the change and the nature of the modifications are proprietary. Mack et al (2017) evaluated moving average (MA) (Chauhan et al, 2018), Savitzky-Golay (SG) (Savitzky & Golay, 1964) and low-pass Butterworth, BW filters (Butterworth, 1930). Both the MA filter and the SG filter are FIR-style filters (Chatterjee & Roy, 2018;Kalman, 1960;Savitzky & Golay, 1964).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Filtering eye-tracking data from an EyeLink 1000: Comparing heuristic, savitzky-golay, IIR and FIR digital filters

Raju,

Friedman,

Bouman

et al. 2023

JEMR

View full text Add to dashboard Cite

In a prior report (Raju et al., 2023) we concluded that, if the goal was to preserve events such as saccades, microsaccades, and smooth pursuit in eye-tracking recordings, data with sine wave frequencies less than 75 Hz were the signal and data above 75 Hz were noise. Here, we compare five filters in their ability to preserve signal and remove noise. We compared the proprietary STD and EXTRA heuristic filters provided by our EyeLink 1000 (SR-Research, Ottawa, Canada), a Savitzky-Golay (SG) filter, an infinite impulse response (IIR) filter (low-pass Butterworth), and a finite impulse filter (FIR). For each of the non-heuristic filters, we systematically searched for optimal parameters. Both the IIR and the FIR filters were zero-phase filters. All filters were evaluated on 216 fixation segments (256 samples), from nine subjects. Mean frequency response profiles and amplitude spectra for all five filters are provided. Also, we examined the effect of our filters on a noisy recording. Our FIR filter had the sharpest roll-off of any filter. Therefore, it maintained the signal and removed noise more effectively than any other filter. On this basis, we recommend the use of our FIR filter. We also report on the effect of these filters on temporal autocorrelation.

show abstract

Section: Introductionmentioning

confidence: 99%

“…Mack et al ( 13 ) evaluated moving average (MA) ( 6 ), Savitzky-Golay (SG) ( 19 ) and low-pass Butterworth, BW filters ( 4 ). Both the MA filter and the SG filter are FIR-style filters ( 5 ; 11 ; 19 ).…”

Section: Introductionmentioning

confidence: 99%