-
Notifications
You must be signed in to change notification settings - Fork 62
Commit
This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository.
- Loading branch information
1 parent
f120be0
commit dcbbfa3
Showing
5 changed files
with
203 additions
and
5 deletions.
There are no files selected for viewing
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,193 @@ | ||
| # This script was originally copied from | ||
| # https://github.com/ShimmerEngineering/Verisense-Toolbox/tree/master/Verisense_step_algorithm | ||
| # where it included the following software license: | ||
|
|
||
| # Copyright (c) 2020 Shimmer | ||
| # | ||
| # Permission is hereby granted, free of charge, to any person obtaining a copy | ||
| # of this software and associated documentation files (the "Software"), to deal | ||
| # in the Software without restriction, including without limitation the rights | ||
| # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell | ||
| # copies of the Software, and to permit persons to whom the Software is | ||
| # furnished to do so, subject to the following conditions: | ||
| # | ||
| # The above copyright notice and this permission notice shall be included in all | ||
| # copies or substantial portions of the Software. | ||
| # | ||
| # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR | ||
| # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, | ||
| # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE | ||
| # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER | ||
| # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, | ||
| # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE | ||
| # SOFTWARE. | ||
|
|
||
|
|
||
| verisense_count_steps <- function(input_data = runif(500, min = -1.5, max = 1.5), | ||
| coeffs = c(0, 0, 0)) { | ||
| # by Matthew R Patterson, mpatterson@shimmersensing.com | ||
| ## Find peaks of RMS acceleration signal according to Gu et al, 2017 method | ||
| # This method is based off finding peaks in the summed and squared acceleration signal | ||
| # and then using multiple thresholds to determine if each peak is a step or an artefact. | ||
| # An additional magnitude threshold was added to the algorithm to prevent false positives | ||
| # in free living data. | ||
| # | ||
|
|
||
| # # TO BE USED WITH GGIR AS FOLLOWS: | ||
| # | ||
| # source("your_file_path/verisense_count_steps.R") | ||
| # myfun = list(FUN = verisense_count_steps, | ||
| # parameters = c(4, 4, 20, -1.0, 4, 4, 0.01, 1.25), # updated based on Rowlands et al Stepping up with GGIR 2022 | ||
| # expected_sample_rate = 15, | ||
| # expected_unit = "g", | ||
| # colnames = c("step_count"), | ||
| # outputres = 1, | ||
| # minlength = 1, | ||
| # outputtype = "numeric", | ||
| # aggfunction = sum, | ||
| # timestamp = F, | ||
| # reporttype = "event") | ||
| # | ||
| # GGIR(myfun = myfun, ...) | ||
| # | ||
| # See also https://wadpac.github.io/GGIR/articles/ExternalFunction.html | ||
|
|
||
| # returns sample location of each step | ||
| fs = 15 # temporary for now, this is manually set | ||
| acc <- sqrt(input_data[, 1]^2 + input_data[, 2]^2 + input_data[, 3]^2) | ||
|
|
||
| if (sd(acc) < 0.025) { | ||
| # acceleration too low, no steps | ||
| num_seconds = round(length(acc) / fs) | ||
| steps_per_sec = rep(0, num_seconds) | ||
| } else { | ||
| # Search for steps | ||
| # Thresholds | ||
| k <- coeffs[[1]] | ||
| period_min <- coeffs[[2]] | ||
| period_max <- coeffs[[3]] | ||
| sim_thres <- coeffs[[4]] # similarity threshold | ||
| cont_win_size <- coeffs[[5]] # continuity window size | ||
| cont_thres <- coeffs[[6]] # continuity threshold | ||
| var_thres <- coeffs[[7]] # variance threshold | ||
| mag_thres <- coeffs[[8]] | ||
|
|
||
| # find the peak rms value is every range of k | ||
| half_k <- round(k / 2) | ||
| segments <- floor(length(acc) / k) | ||
| peak_info <- matrix(NA, nrow = segments, ncol = 5) | ||
| # peak_info[,1] - peak location | ||
| # peak_info[,2] - acc magnitude | ||
| # peak_info[,3] - periodicity (samples) | ||
| # peak_info[,4] - similarity | ||
| # peak_info[,5] - continuity | ||
|
|
||
| # for each segment find the peak location | ||
| for (i in 1:segments) { | ||
| start_idx <- (i - 1) * k + 1 | ||
| end_idx <- start_idx + (k - 1) | ||
| tmp_loc_a <- which.max(acc[start_idx:end_idx]) | ||
| tmp_loc_b <- (i - 1) * k + tmp_loc_a | ||
| # only save if this is a peak value in range of -k/2:+K/2 | ||
| start_idx_ctr <- tmp_loc_b - half_k | ||
| if (start_idx_ctr < 1) { | ||
| start_idx_ctr <- 1 | ||
| } | ||
| end_idx_ctr <- tmp_loc_b + half_k | ||
| if (end_idx_ctr > length(acc)) { | ||
| end_idx_ctr <- length(acc) | ||
| } | ||
| check_loc <- which.max(acc[start_idx_ctr:end_idx_ctr]) | ||
| if (check_loc == (half_k + 1)) { | ||
| peak_info[i, 1] <- tmp_loc_b | ||
| peak_info[i, 2] <- max(acc[start_idx:end_idx]) | ||
| } | ||
| } | ||
| peak_info <- peak_info[is.na(peak_info[, 1]) != TRUE, ] # get rid of na rows | ||
|
|
||
| # filter peak_info[,2] based on mag_thres | ||
| peak_info <- peak_info[peak_info[, 2] > mag_thres, ] | ||
| if (length(peak_info) > 10) { | ||
| # there must be at least two steps | ||
| num_peaks <- length(peak_info[, 1]) | ||
|
|
||
| no_steps = FALSE | ||
| if (num_peaks > 2) { | ||
| # Calculate Features (periodicity, similarity, continuity) | ||
| peak_info[1:(num_peaks - 1), 3] <- diff(peak_info[, 1]) # calculate periodicity | ||
| peak_info <- peak_info[peak_info[, 3] > period_min, ] # filter peaks based on period_min | ||
| peak_info <- peak_info[peak_info[, 3] < period_max, ] # filter peaks based on period_max | ||
| } else { | ||
| no_steps = TRUE | ||
| } | ||
| } else { | ||
| no_steps = TRUE | ||
| } | ||
|
|
||
| if (length(peak_info) == 0 || | ||
| length(peak_info) == sum(is.na(peak_info)) || no_steps == TRUE) { | ||
| # no steps found | ||
| num_seconds = round(length(acc) / fs) | ||
| steps_per_sec = rep(0, num_seconds) | ||
| } else { | ||
| # calculate similarity | ||
| num_peaks <- length(peak_info[, 1]) | ||
| peak_info[1:(num_peaks - 2), 4] <- -abs(diff(peak_info[, 2], 2)) # calculate similarity | ||
| peak_info <- peak_info[peak_info[, 4] > sim_thres, , drop = FALSE] # filter based on sim_thres | ||
| peak_info <- peak_info[is.na(peak_info[, 1]) != TRUE, , drop = FALSE] # previous statement can result in an NA in col-1 | ||
|
|
||
| # calculate continuity | ||
| if (length(peak_info[, 3]) > 5) { | ||
| end_for <- length(peak_info[, 3]) - 1 | ||
| for (i in cont_thres:end_for) { | ||
| # for each bw peak period calculate acc var | ||
| v_count <- 0 # count how many windows were over the variance threshold | ||
| for (x in 1:cont_thres) { | ||
| if (var(acc[peak_info[i - x + 1, 1]:peak_info[i - x + 2, 1]]) > var_thres) { | ||
| v_count = v_count + 1 | ||
| } | ||
| } | ||
| if (v_count >= cont_win_size) { | ||
| peak_info[i, 5] <- 1 # set continuity to 1, otherwise, 0 | ||
| } else { | ||
| peak_info[i, 5] <- 0 | ||
| } | ||
| } | ||
| } | ||
| peak_info <- peak_info[peak_info[, 5] == 1, 1] # continuity test - only keep locations after this | ||
| peak_info <- peak_info[is.na(peak_info) != TRUE] # previous statement can result in an NA in col-1 | ||
|
|
||
| if (length(peak_info) == 0) { | ||
| # no steps found | ||
| num_seconds = round(length(acc) / fs) | ||
| steps_per_sec = rep(0, num_seconds) | ||
| } else { | ||
| # debug plot | ||
| # is_plot = F | ||
| # if (is_plot) { | ||
| # library(ggplot2) | ||
| # library(plotly) | ||
| # acc.df <- data.frame(acc=acc, det_step=integer(length(acc))) | ||
| # acc.df$det_step[peak_info] <- 1 # to plot annotations, prepare a 0/1 column on dataframe | ||
| # acc.df$idx <- as.numeric(row.names(acc.df)) | ||
| # pl <- ggplot(data=acc.df,aes(x=idx,y=acc)) | ||
| # pl2 <- pl + geom_line() | ||
| # pl3 <- pl2 + geom_point(data=subset(acc.df,det_step==1),aes(x=idx,y=acc),color='red',size=1,alpha=0.7) | ||
| # pl4 <- ggplotly(pl3) | ||
| # print(pl4) | ||
| # } | ||
|
|
||
| # for GGIR, output the number of steps in 1 second chunks | ||
| start_idx_vec <- seq(from = 1, | ||
| to = length(acc), | ||
| by = fs) | ||
| steps_per_sec <- table(factor( | ||
| findInterval(peak_info, start_idx_vec), | ||
| levels = seq_along(start_idx_vec) | ||
| )) | ||
| steps_per_sec <- as.numeric(steps_per_sec) | ||
| } | ||
| } | ||
| } | ||
| return(steps_per_sec) | ||
| } |
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters