我已经从加速度计 x、y、z 捕获数据。我正在做活动识别，并将数据分为坐、跑、跳。
我现在将数据窗口化并提取均值、方差等特征，然后将其保存到 features.txt 文件中，然后将数据导入 Weka 软件进行分类。
我正在寻找有关要导入 Weka 的文件结构的帮助，因为我不确定哪一个适合与 Weka 一起使用。
特征应该存储在一个 txt 文件中还是单独存储？
他们是否需要自己的列：mean_x mean_y mean_z var_x var_y var_z 标签？还是我下面的设置？
维卡示例：
@relation datashimmer
@attribute x numeric
@attribute y numeric
@attribute z numeric
}@attribute class { sitmean, jumpmean , runmean, sitstd, jumpstd, runstd, sitvar, jumpvar, runvar, sitrange, jumprange, runrange
@data
9.0706440e+00 7.8577383e-01 2.4532359e+00 jumpmean
9.6451465e+00 7.8731349e-01 3.1504403e+00 jumpmean
8.1915240e+00 9.9549126e-01 2.4723417e+00 jumpmean
8.6773829e+00 6.3727150e-01 3.0240603e+00 jumpmean
8.6675553e+00 7.3380330e-01 3.0251476e+00 jumpmean
8.6544917e+00 8.4768398e-01 2.8385212e+00 jumpmean
9.5032030e+00 1.1324107e+00 2.1942181e+00 jumpmean
9.5477273e+00 1.0467068e+00 2.0262543e+00 runmean
9.5686235e+00 1.1320179e+00 1.8989209e+00 runmean
9.2875020e+00 8.6683897e-01 1.9064545e+00 runmean
9.3515172e+00 9.2082513e-01 2.2105460e+00 runmean
9.6041949e+00 9.2349286e-01 2.5863699e+00 runmean
9.6029677e+00 7.4794280e-01 2.3176337e+00 runmean
9.1105362e+00 9.0374931e-01 2.1625089e+00 runmean
-5.0683414e-01 -4.4845132e+00 8.8514156e+00 sitmean
-4.9025531e-01 -4.4989652e+00 8.8622800e+00 sitmean
-4.9871921e-01 -4.4879779e+00 8.8922710e+00 sitmean
-5.0798669e-01 -4.4828028e+00 8.8459366e+00 sitmean
-5.4927840e-01 -4.4699890e+00 8.8474149e+00 sitmean
-5.3211037e-01 -4.4591018e+00 8.8433826e+00 sitmean
-5.5223809e-01 -4.4869555e+00 8.8594771e+00 sitmean
-4.7530904e-01 -4.4812295e+00 8.8135292e+00 sitmean
-5.4696903e-01 -4.4788825e+00 8.8484514e+00 sitmean
-5.5149171e-01 -4.4587802e+00 8.8630469e+00 sitmean
-5.3465224e-01 -4.4478393e+00 8.8383810e+00 sitmean
-5.1742062e-01 -4.4637259e+00 8.8325696e+00 sitmean
-5.4154869e-01 -4.4833639e+00 8.8562804e+00 sitmean
-5.7514668e-01 -4.4912760e+00 8.8727185e+00 sitmean
9.3696539e+00 1.1837913e+00 -2.6525999e+00 jumpmean
8.6643644e+00 1.2731911e+00 -2.4803529e+00 jumpmean
8.0837365e+00 1.9774159e+00 -2.8276019e+00 jumpmean
8.0305034e+00 1.2057473e+00 -1.9911895e+00 jumpmean
7.9512264e+00 4.5797049e+00 -8.0403707e-01 jumpmean
8.5823014e+00 3.9339561e+00 -2.6684712e+00 jumpmean
8.2940896e+00 2.1376560e+00 -2.7870077e+00 jumpmean
9.4819739e+00 8.1379138e-01 -3.1084057e+00 runmean
9.0599416e+00 3.9622206e-01 -3.4679528e+00 runmean
9.3782963e+00 9.4021813e-01 -3.3614160e+00 runmean
9.4959636e+00 8.3451458e-01 -3.0700251e+00 runmean
9.3596252e+00 7.6187722e-01 -3.3890264e+00 runmean
9.4984387e+00 1.1694115e+00 -3.3733935e+00 runmean
8.9490648e+00 1.4501386e+00 -3.1231425e+00 runmean
8.3033044e+00 -8.7385271e-01 4.9949720e+00 sitmean
8.2657411e+00 -9.2072139e-01 5.0284777e+00 sitmean
8.2039554e+00 -9.1552295e-01 5.1517434e+00 sitmean
8.2272207e+00 -8.4401430e-01 5.1535285e+00 sitmean
8.2654827e+00 -7.9834045e-01 5.0608142e+00 sitmean
8.2999421e+00 -8.6703733e-01 5.0902732e+00 sitmean
8.2944043e+00 -8.0556494e-01 5.0225056e+00 sitmean
8.5033422e+00 3.0787253e+00 3.5076829e+00 jumpstd
7.6895407e+00 3.1243470e+00 3.0559549e+00 jumpstd
7.5029557e+00 4.0664196e+00 4.1725267e+00 jumpstd
7.5436436e+00 4.3278471e+00 3.8818316e+00 jumpstd
6.1032521e+00 4.6354434e+00 3.5036980e+00 jumpstd
7.4142339e+00 4.0956300e+00 3.7602181e+00 jumpstd
7.9337673e+00 3.2664252e+00 4.2575543e+00 jumpstd
5.0545343e+00 2.8901073e+00 3.5430258e+00 runstd
5.3394208e+00 2.1666505e+00 3.7298383e+00 runstd
4.8233755e+00 3.0923609e+00 3.4364285e+00 runstd
5.3904459e+00 2.6258827e+00 3.3256969e+00 runstd
4.3812033e+00 2.7708500e+00 2.5985276e+00 runstd
4.1402607e+00 4.0070005e+00 2.7039632e+00 runstd
4.2720294e+00 4.6312551e+00 4.4228231e+00 runstd
2.7539715e-01 2.3209201e-01 3.2587558e-01 sitstd
1.5065993e-01 1.7074749e-01 1.7440757e-01 sitstd
1.6740466e-01 1.5952978e-01 1.6872027e-01 sitstd
1.6208795e-01 1.6157018e-01 1.5045205e-01 sitstd
1.8287180e-01 1.7500556e-01 1.9729539e-01 sitstd
1.8928747e-01 1.9478179e-01 1.4550928e-01 sitstd
1.7755868e-01 1.7635119e-01 1.4697588e-01 sitstd
我的matlab代码：
%WindowParams
Fq = 51.2;
%Sample Rate of Shimmer
windowLength = 2;
%Length for each window in seconds
startPos = 1;
startPos2 = 1;
startPos3 = 1;
%Starting Position for 1st win
endPos = startPos + (windowLength * floor(Fq));
endPos2 = startPos2 + (windowLength * floor(Fq));
endPos3= startPos3 + (windowLength * floor(Fq));
%End Position for 1st win
totalWindows = floor(length(martySit)/Fq/windowLength);
totalWindows2 = floor(length(martyJump)/Fq/windowLength);
totalWindows3 = floor(length(martyRun)/Fq/windowLength);
stats = zeros(windowLength,9);
for i = 1:totalWindows
eprange = range(martySit(startPos:endPos,:));%calculate window range
martySitfeatures (i,1:3) = eprange; %X, Y & Z axis values for each stat
%Next window position
startPos = endPos+1;
endPos= startPos + (windowLength * floor(Fq));
martySitfeatures (:,4) = 10;
end
for i = 1:totalWindows2
eprange = range(martyJump(startPos2:endPos2,:));
%calculate window range
%X, Y & Z axis values for each stat
martyJumpfeatures (i,1:3) = eprange;
%Next window position
startPos2 = endPos2+1;
endPos2 = startPos2+ (windowLength * floor(Fq));
martyJumpfeatures (:,4) = 11;
end
for i = 1:totalWindows3
eprange = range(martyRun(startPos3:endPos3,:));
%calculate window range
%X, Y & Z axis values for each stat
martyRunfeatures (i,1:3) = eprange;
%Next window position
startPos3 = endPos3+1;
endPos3 = startPos3+ (windowLength * floor(Fq));
martyRunfeatures (:,4) = 12;
end