EMG

library("fda.usc")

## Loading required package: fda

## Loading required package: splines

## Loading required package: Matrix

## 
## Attaching package: 'fda'

## The following object is masked from 'package:graphics':
## 
##     matplot

## Loading required package: MASS

## Loading required package: mgcv

## Loading required package: nlme

## This is mgcv 1.8-24. For overview type 'help("mgcv-package")'.

## Loading required package: rpart

library("chipPCR")



library("signal")

## 
## Attaching package: 'signal'

## The following objects are masked from 'package:stats':
## 
##     filter, poly

setwd("~/Dropbox/Marcos_Biomech/Sherveen S2/EMG")

nombres=list.files(pattern = ".csv",recursive = TRUE)

nombre= substr(nombres,0,7)

nombre2=setdiff(strsplit(nombres,""),strsplit(nombre,""))
nombre2= lapply(nombre2,function(x){paste0(x,sep="",collapse = "")})

datos= read.csv("Par_001/Par_001_HIIT_06.csv",header = FALSE)
datos2= read.csv("Par_001/Par_001_HIIT_06_events.csv")
datos$tiempo= datos[,1]/250
datos$tiemporeal= seq(min(datos$tiempo),length.out=length(datos$tiempo),max(datos$tiempo)+0.004)
 colnames(datos)=c("Frame","VL","VM","RF","BF","GMAX","GMED","time","realtime")

stance= datos2[seq(1,by=2,length=20),]
swing= datos2[seq(2,by=2,length=19),]
pasosstance= 1:19
pasosstance= as.list(pasosstance)
pasosswing= 1:19
pasosswing= as.list(pasosswing)


W=0.1
b1 <- butter(4, W=0.1)

model1= filtfilt(b1,datos$VL)
model2= filtfilt(b1,datos$VM)
model3= filtfilt(b1,datos$RF)
model4= filtfilt(b1,datos$BF)
model5= filtfilt(b1,datos$GMAX)
model6= filtfilt(b1,datos$GMED)


residuos1= (model1-datos$VL)^2
residuos2= (model2-datos$VL)^2
residuos3= (model3-datos$VL)^2
residuos4= (model4-datos$VL)^2
residuos5= (model5-datos$VL)^2
residuos6= (model6-datos$VL)^2



datos$VL= model1
datos$VM= model2
datos$RF= model3
datos$BF= model4
datos$GMAX= model5
datos$GMED= model6




# par(mfrow= c(2,3))
# plot(datos$realtime,residuos1)
# plot(datos$realtime,residuos2)
# plot(datos$realtime,residuos3)
# plot(datos$realtime,residuos4)
# plot(datos$realtime,residuos5)
# plot(datos$realtime,residuos6)

# plot(datos$realtime,model1, type='o', pch=20, col='red')
# 
# par(mfrow= c(2,1))
# 
# plot(datos$realtime[1:1000],model1[1:1000])
# plot(datos$realtime[1:1000],datos$VL[1:1000])

  for(j in 2:7){


  }
    
    
  for(j in 2:7){
    for(i in 1:18){
  pasosstance[[i]]= datos[datos$realtime>=stance[i]&datos$realtime<swing[i],] 
  pasosswing[[i]]=datos[datos$realtime>=swing[i]&datos$realtime<stance[i+1],] 
 # plot(fdata(pasosstance[[i]][,4],argvals =pasosstance[[i]][,8]),main= paste(colnames(datos)[j],"Stance",sep=""))
  
  #  plot(fdata(pasosswing[[i]][,4],argvals =pasosswing[[i]][,8]),main=paste(colnames(datos)[j],"Swing",sep=""))

  
  plot(pasosstance[[i]][,j],main= paste(colnames(datos)[j],"Stance",sep=""))
#  plot(pasosswing[[i]][,j],main= paste(colnames(datos)[j],"Swing",sep=""))
       
    }}

pasosstance[[19]]= datos[datos$realtime>=stance[19]&datos$realtime<swing[19],] 
pasosswing[[19]]=datos[datos$realtime>=swing[19]&datos$realtime<stance[20],] 



plot(fdata(pasosstance[[19]][,4],argvals =pasosstance[[19]][,8]))

    plot(fdata(pasosswing[[19]][,4],argvals =pasosswing[[19]][,8]))

plot(pasosstance[[19]][,4])

  plot(pasosswing[[19]][,4])