80 lines
4.0 KiB
Matlab
80 lines
4.0 KiB
Matlab
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function [ResultStruct] = HarmonicityFrequency(dataAccCut_filt,P,F, StrideFrequency,dF,LowFrequentPowerThresholds,N_Harm,FS,AccVectorLen,ResultStruct)
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%LD 15-04-2021: Solve problem finding fundamental frequency in power
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%spectral density STRIDE - VS STEP frequency.
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% Add sum of power spectra (as a rotation-invariant spectrum)
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P = [P,sum(P,2)];
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PS = sqrt(P);
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% Calculate the measures for the power per separate dimension
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for i=1:size(P,2);
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% Relative cumulative power and frequencies that correspond to these cumulative powers
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PCumRel = cumsum(P(:,i))/sum(P(:,i));
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PSCumRel = cumsum(PS(:,i))/sum(PS(:,i));
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FCumRel = F+0.5*dF;
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% Derive relative cumulative power for threshold frequencies
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Nfreqs = size(LowFrequentPowerThresholds,2);
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LowFrequentPercentage(i,1:Nfreqs) = interp1(FCumRel,PCumRel,LowFrequentPowerThresholds)*100;
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% Calculate relative power of first 10 harmonics, taking the power of each harmonic with a band of + and - 10% of the first
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% harmonic around it
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PHarm = zeros(N_Harm,1);
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PSHarm = zeros(N_Harm,1);
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for Harm = 1:N_Harm,
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FHarmRange = (Harm+[-0.1 0.1])*(StrideFrequency); % In view of possible drift, which could lead to missing or widening peaks, the power spectral density of each peak was calculated within the frequency bands of +0.1 and −0.1 Hz of the peak frequency value.
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PHarm(Harm) = diff(interp1(FCumRel,PCumRel,FHarmRange));
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PSHarm(Harm) = diff(interp1(FCumRel,PSCumRel,FHarmRange));
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end
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% Derive index of harmonicity; which is the spectral power of the basic harmonic divided by the sum of the power of first six harmonics
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if i == 2 % for ML we expect odd instead of even harmonics
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IndexHarmonicity(i) = PHarm(1)/sum(PHarm(1:2:12));
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elseif i == 4
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IndexHarmonicity(i) = sum(PHarm(1:2))/sum(PHarm(1:12));
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else
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IndexHarmonicity(i) = PHarm(2)/sum(PHarm(2:2:12));
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end
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% Calculate the phase speed fluctuations
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StrideFreqFluctuation = nan(N_Harm,1);
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StrSamples = round(FS/StrideFrequency);
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for h=1:N_Harm,
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CutOffs = [StrideFrequency*(h-(1/3)) , StrideFrequency*(h+(1/3))]/(FS/2);
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if all(CutOffs<1) % for Stride frequencies above FS/20/2, the highest harmonics are not represented in the power spectrum
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[b,a] = butter(2,CutOffs);
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if i==4 % Take the vector length as a rotation-invariant signal
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AccFilt = filtfilt(b,a,AccVectorLen);
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else
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AccFilt = filtfilt(b,a,dataAccCut_filt(:,i));
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end
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Phase = unwrap(angle(hilbert(AccFilt)));
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SmoothPhase = sgolayfilt(Phase,1,2*(floor(FS/StrideFrequency/2))+1); % This is in fact identical to a boxcar filter with linear extrapolation at the edges
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StrideFreqFluctuation(h) = std(Phase(1+StrSamples:end,1)-Phase(1:end-StrSamples,1));
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end
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end
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FrequencyVariability(i) = nansum(StrideFreqFluctuation./(1:N_Harm)'.*PHarm)/nansum(PHarm);
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if i<4,
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% Derive harmonic ratio (two variants) --> Ratio of the even and
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% odd harmonics!
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if i == 2 % for ML we expect odd instead of even harmonics
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HarmonicRatio(i) = sum(PSHarm(1:2:end-1))/sum(PSHarm(2:2:end)); % relative to summed 3d spectrum
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HarmonicRatioP(i) = sum(PHarm(1:2:end-1))/sum(PHarm(2:2:end)); % relative to own spectrum
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else
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HarmonicRatio(i) = sum(PSHarm(2:2:end))/sum(PSHarm(1:2:end-1));
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HarmonicRatioP(i) = sum(PHarm(2:2:end))/sum(PHarm(1:2:end-1));
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end
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end
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end
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ResultStruct.IndexHarmonicity_V = IndexHarmonicity(1); % higher smoother more regular patter
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ResultStruct.IndexHarmonicity_ML = IndexHarmonicity(2); % higher smoother more regular patter
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ResultStruct.IndexHarmonicity_AP = IndexHarmonicity(3); % higher smoother more regular patter
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ResultStruct.HarmonicRatio_V = HarmonicRatio(1);
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ResultStruct.HarmonicRatio_AP = HarmonicRatio(3);
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ResultStruct.StrideFrequency = StrideFrequency; |