Question

我有一个加速计数据，即AccX，AccY和AccZ。

我正在寻找一种算法来根据这些数据计算功率谱密度。我知道以下内容：

library(caret)
library(nlme)

data(Orthodont)
head(Orthodont)
subjects <- as.character(unique(Orthodont$Subject))

## figure out folds at the subject level

set.seed(134)
sub_folds <- createFolds(y = subjects, list = TRUE, returnTrain = TRUE)

## now create the mappings to which *rows* are in the training set
## based on which subjects are left in or out

in_train <- holdout <- vector(mode = "list", length = length(sub_folds))

row_index <- 1:nrow(Orthodont)

for(i in seq(along = sub_folds)) {
  ## Which subjects are in fold i
  sub_in <- subjects[sub_folds[[i]]]
  ## which rows of the data correspond to those subjects
  in_train[[i]] <- row_index[Orthodont$Subject %in% sub_in]
  holdout[[i]]  <- row_index[!(Orthodont$Subject %in% sub_in)]  
}

names(in_train) <- names(holdout) <- names(sub_folds)

ctrl <- trainControl(method = "cv",
                     savePredictions = TRUE,
                     index = in_train,
                     indexOut = holdout)

mod <- train(distance ~ (age+Sex)^2, data = Orthodont,
             method = "lm", 
             trControl = ctrl)

first_fold <- subset(mod$pred, Resample == "Fold01")

## These were used to fit the model
table(Orthodont$Subject[-first_fold$rowIndex])
## These were heldout:
table(Orthodont$Subject[first_fold$rowIndex])

其中＆＃34; s＆＃34;是输入信号，fft是快速傅立叶变换。

F = fft (s);

我需要知道这个PSD = (1/length(s)) * F * conj(F);是加速时间序列还是位置时间序列？

Answer 1

这取决于您感兴趣的内容。如果您想要加速时间序列的功率谱密度，那么s必须是加速时间序列本身而不是位置时间序列。但请注意，基于您编写的简单算法（称为＆＃34;周期图＆＃34;）的PSD估计在许多情况下可能不足以获得真实PSD的实际估计。话题很多，文献广泛。你可以从Wikipedia开始，或者，如果你想要一本关于好（但相当艰难）的书的建议，Percival and Walden。为了提供更详细的信息，我们需要从物理角度更详细地了解您必须做的事情。

来自加速度计数据的功率谱密度

1 个答案: