# CALCULATE PATIENT STATE ALGORITHM STATE = c("WALK", "UP", "DOWN", "SIT") size_ <- 5 differ_ <- 0.1 POINTS <- data.frame( index = integer(1) ) POINTS["state"] <- 0 POINTS["probability"] <- 0 for(i in 1:size_){ POINTS[LETTERS[i]] <- 0 } for(i in size_:(nrow(DATA)-size_)){ start <- DATA[i,]$Head.y end <- DATA[i+size_,]$Head.y state_ <- NULL if(((start - end) < differ_) && (start - end) > -differ_){ if(start >= mean(DATA$Head.y)){ #WALK (4) state_ <- 1 } else{ #SIT (4) state_ <- 4 } } else if((start - end) < differ_) { #UP (2) state_ <- 2 } else if((start - end) > differ_) { #UP (3) state_ <- 3 } else { state_ <- 1 } for(j in 0:size_-1){ POINTS[i-j,LETTERS[j+1]] <- state_ } POINTS[i,]$index <- DATA[i,]$Time if(i>size_*2 && i<=((nrow(DATA) - size_))){ tmp_ <- ( tbl_df( table( POINTS[i-size_,] %>% unlist(., use.names=FALSE) ) ) %>% arrange( desc(n) ) ) POINTS[i-size_,]$state <- tmp_[1,]$Var1 POINTS[i-size_,]$probability <- ((1 / size_) * tmp_[1,]$n) } DATA[i,]$state = state_ } print("STATE CALCULATION DONE") remove(tmp_) DATA <- DATA[complete.cases(DATA),] rownames(DATA) <- 1:nrow(DATA) DATA$index = as.integer(rownames(DATA)) WALKING <- strtoi(rownames(DATA[DATA$state==1,])) if(!consistent(WALKING)){ stop("Patient not consistently walking, (Maybe he/she fell). Anyway, we can't analyse this data", call. = FALSE) } # CALCULATE STRAIGHT WALKING PATH yPrediction <-lm(Head.y ~ I(index^2)+index, data=DATA[min(WALKING):max(WALKING),]) yPredicted <- as.vector(predict(yPrediction, data.frame(index=WALKING))) WALKBASE <- mean(c(yPredicted[1], tail(yPredicted, n=1))) for(i in WALKING[1]:(length(WALKING) + WALKING[1] - 1)){ DATA[i,]$Head.y <- DATA[i,]$Head.y - (yPredicted[i-WALKING[1]+1] - WALKBASE) } plot(POINTS$probability, type = "l") # http://stats.stackexchange.com/questions/30975/how-to-add-non-linear-trend-line-to-a-scatter-plot-in-r # http://www.mathsisfun.com/geometry/parabola.html