An Introduction to EMG Signal Processing Using MatLab and Microsoft Excel

2014 ◽  
pp. 95-112
Author(s):  
Daniel Robbins
Keyword(s):  
Signal Processing ◽  
Fourier Transform ◽  
Fast Fourier Transform ◽  
Frequency Analysis ◽  
Mean Square ◽  
Microsoft Excel ◽  
Signal Filtering ◽  
Emg Signal ◽  
Low Pass ◽  
High Pass

This chapter provides the reader with an introduction to the fundamentals of biological signal analysis and processing, using EMG signals to illustrate the process. The areas covered within the chapter include: frequency analysis using the Fast Fourier Transform, identifying noise within a signal, signal smoothing via root mean square (RMS) processing and signal filtering with both low-pass and high-pass filters. Guidelines for the application of the processes covered are included in conjunction with step by step examples using both MathWorks MatLab and Microsoft Excel software. Following the examples therefore allows the reader to practice the processes described to promote and reinforce their learning.

scholarly journals PENGEMBANGAN SISTEM DETEKSI KELELAHAN PADA PENGEMUDI MOBIL BERBASIS SINYAL ELECTROMYOGRAPHY (EMG)

2016 ◽  
Vol 3 (1) ◽  
pp. 18
Author(s):  
Sukma Firdaus ◽  
Marlia Adriana
Keyword(s):  
Fourier Transform ◽  
Fast Fourier Transform ◽  
Surface Emg ◽  
Mean Power ◽  
Mean Power Frequency ◽  
Pass Filter ◽  
Low Pass Filter ◽  
Low Pass ◽  
Power Frequency ◽  
High Pass

Peningkatan alat trasportasi khususnya kendaraan roda empat (mobil) mengakibatkan peningkatan volume kendaraan di jalan. Hal ini berdampak pada meningkatnya kemacetan. Selain kemacetan, peningkatan volume kendaraan berdampak juga pada peningkatan kecelakan lalu lintas. Salah satu penyebab kecelakan lalu lintas adalah faktor pengendaranya, yaitu kelelahan. Penelitian ini, merancang sistem pendeteksi kelelahan pengemudi, berdasarkan sinyal biologis pengemudi yaitu sinyal biologis kondisi otot lengan. Sinyal tersebut direkam dengan menggunakan metode surface EMG. Elektroda yang ditempelkan disebelah kanan lengan dihubungkan dengan penguat instrumentasi dan digitalisasi melalui unit pemproses sinyal untuk dapat disimpan kedalam komputer. Kegiatan pengambilan data dilakukan sebanyak 8 kali dengan jarak tempuh pengemudi sebesar 80 km dari Kota Banjarmasin menuju Kota Pelaihari. Akuisisi data menggunakan frekuensi sampling sebesar 4 KHz dan diproses secara filter analog untuk High Pass Filter sebesar 2 KHz dan Low Pass Filter sebesar 500 Hz. Setelah data direkam, sinyal dilakukan proses downsampling menjadi 1 KHz. Pada proess digital, dilakukan lagi proses pemfilteran secara Low Pass Filter sebesar 500 Hz. Proses digital selanjutnya adalah melakukan analisis pada domain frekuensi menggunakan transformasi fourier dengan memakai algoritma fast fourier transform (fft). Hasil dari transformasi fourier diidentifikasi berdasarkan nilai Mean Power Frequency (MPF). Berdasarkan hasil perhitungan MPF yang telah dilakukan, diperoleh saat awal berkendara nilai rata-rata MPF nya adalah sebesar 25,2 Hz sedangkan pada akhir berkendara bernilai sebesar 33,3 Hz. Dengan hasil ini dapat tergambarkan kondisi pengemudi pada awal mengemudi dan tidak terjadi kelelahan maka nilai frekuensi yang dominan cenderung lebih rendah jika dibandingkan dengan setelah berkendara atau saat kelelahan.

scholarly journals Metode High-Pass Filter Dan Fast Fourier Transform Untuk Perbaikan Citra Telapak Tangan

Techno Com ◽  
2021 ◽  
Vol 20 (4) ◽  
pp. 601-612
Author(s):  
Mhd Furqan ◽  
- Sriani ◽  
Muhammad Akbar Ramadhan Tanjung
Keyword(s):  
Fourier Transform ◽  
Fast Fourier Transform ◽  
Mean Square Error ◽  
Signal To Noise Ratio ◽  
Mean Square ◽  
Signal To Noise ◽  
Pass Filter ◽  
High Pass Filter ◽  
Noise Ratio ◽  
High Pass

Telapak tangan sering digunakan sebagai sumber penelitian dibidang sistem biometrik karena mempunyai karakteristik seperti sidik jari. Selain itu, telapak tangan juga mudah didapatkan dan dapat diperoleh dari citra yang memiliki resolusi rendah. Namun, selain itu juga sebuah citra telapak tangan akan dapat mengalami penurunan terhadap kualitasnya. Untuk itu dilakukanlah sebuah tahap yang dikenal dengan perbaikan kualitas citra, dimana bidang ini merupakan tahap awal dari pengolahan citra digital. Dalam penelitian ini penggunaan metode dalam perbaikan citra difokuskan untuk menajamkan citra telapak tangan dengan menggunakan high pass filter dan filter fast fourier transform, dimana sebelumnya citra tersebut telah diolah dengan menggunakan histogram ekualisasi untuk meningkatkan kontras citra telapak tangan. Setelah dilakukan pengujian terhadap 30 sampel citra. Dengan menilai error pada MSE (Mean Square Error) dan PSNR (Peak Signal to Noise Ratio) dari citra hasil rekonstruksi, hasil pengujian menunjukkan bahwa penggunaan high pass filter dengan koefisien=1 menghasilkan citra yang lebih baik dimana nilai rata-rata MSE=7,064544(dB) dan PSNR=40,01314(dB) daripada menggunakan high-pass filter dengan koefisien=0. Sedangkan pada fast fourier transform dengan menggunakan Ideal High-Pass Filter (IHPF) mampu menghasilkan citra rekonstruksi yang lebih baik dengan rerata MSE=9,354056(dB) dan PSNR=38,537046(dB) dari pada menggunakan butterworth high-pass filter (BHPF) dan gaussian high-pass filter (GHPF)

The whole can be very much less than the sum of its parts

2019 ◽  
Vol 103 (556) ◽  
pp. 117-127
Author(s):  
Peter Shiu
Keyword(s):  
Signal Processing ◽  
Fourier Transform ◽  
Mr Imaging ◽  
Fast Fourier Transform ◽  
Data Compression ◽  
Mobile Phones ◽  
Processing Data ◽  
Speed Up ◽  
Indispensable Tool ◽  
Computing Machines

This Article is on the discrete Fourier transform (DFT) and the fast Fourier transform (FFT). As we shall see, FFT is a slight misnomer, causing confusion to beginners. The idiosyncratic title will be clarified in §4.Computing machines are highly efficient nowadays, and much of the efficiency is based on the use of the FFT to speed up calculations in ultrahigh precision arithmetic. The algorithm is now an indispensable tool for solving problems that involve a large amount of computation, resulting in many useful and important applications: for example, in signal processing, data compression and photo-images in general, and WiFi, mobile phones, CT scanners and MR imaging in particular.

The fast Fourier transform

2020 ◽  
pp. 134-189
Author(s):  
Rob H. Bisseling
Keyword(s):  
Signal Processing ◽  
Fourier Transform ◽  
Fast Fourier Transform ◽  
Weather Forecasting ◽  
Data Access ◽  
Regular Pattern ◽  
Matrix Products ◽  
Data Redistribution ◽  
Fft Algorithms ◽  
Matrix Vector

This chapter demonstrates the use of different data distributions in different phases of a parallel fast Fourier transform (FFT), which is a regular computation with a predictable but challenging data access pattern. Both the block and cyclic distributions are used and also intermediates between them. Each required data redistribution is a permutation that involves communication. By making careful choices, the number of such redistributions can be kept to a minimum. FFT algorithms can be concisely expressed using matrix/vector notation and Kronecker matrix products. This notation is also used here. The chapter then shows how permutations with a regular pattern can be implemented more efficiently by packing the data. The parallelization techniques discussed for the specific case of the FFT are also applicable to other related computations, for instance in signal processing and weather forecasting.

Application of Chirp-z Transform in Doppler Weather Radar

2014 ◽  
Vol 513-517 ◽  
pp. 4265-4268
Author(s):  
Jun Zhu ◽  
Xiao Jia Lu ◽  
Xiang Liu
Keyword(s):  
Signal Processing ◽  
Fourier Transform ◽  
Fast Fourier Transform ◽  
Measurement Accuracy ◽  
Weather Radar ◽  
Doppler Weather Radar ◽  
Transform Method ◽  
Fast Fourier Transform Method ◽  
Sampling Points ◽  
Signal Processing Methods

Among the signal processing methods of Doppler weather radar, the FFT (Fast Fourier Transform) method is widely used. If the measurement accuracy needs to be improved, the number of FFT points also needs to be increased. As a result, the amount of computation increases exponentially. Chirp-z transform can directly refine certain spectrum in the spectrum of weather echoes. In the case that the sampling points and the amount of computation increase fewer, the measurement accuracy can be greatly advanced.

scholarly journals The Role of 2D Fast Fourier Transform and High Pass Filter in Regional and Residual Anomaly Separation in Gravity

2021 ◽  
Vol 873 (1) ◽  
pp. 012017
Author(s):  
I R Palupi ◽  
W Raharjo ◽  
S Kiswanti
Keyword(s):  
Fourier Transform ◽  
Power Spectrum ◽  
Fast Fourier Transform ◽  
Pass Filter ◽  
Wavenumber Domain ◽  
High Pass Filter ◽  
Separation Result ◽  
Fitting In ◽  
2D Fft ◽  
High Pass

Abstract Regional and residual Separation anomaly is one thing that must do in gravity processing data. It is important before calculating the depth of anomaly by power spectrum. There are several ways to do this, one of them is using 2D Fast Fourier Transform (FFT). 2D FFT will calculate the two-dimensional power of the gravity map (Bouger anomaly) to change the spatial domain into the wavenumber domain. 2D FFT result has no unit because it works in the wavenumber domain. Power spectrum do in wavenumber domain map. Besides that, to make the wavenumber map in the frequency domain, it should be convolved with some filter (high–pass filter) and then inverse to separate the regional and the residual map. The design of the filter matrix depends on the number of the data and the location of anomalies will be enhanced. It will influence the separation result. The best result gets from the trial and error process. 2D FFT is act like Upward Continuation or Polynomial Fitting in the gravity method with the simple process. In this paper, the process fully done in Python. Python is an effective and simple language programming because it has many modules to support the processing and covering the big data. It also gives the flexibility to the researcher to determine the specific location that will be enhanced

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