The adept
package implements ADaptive Empirical Pattern Transformation
(ADEPT) method[1] for pattern segmentation from a time-series. ADEPT
is optimized to perform fast, accurate walking strides segmentation from
high-density data collected with a wearable accelerometer during
walking. The method was validated using data collected with sensors worn
at left wrist, left hip and both ankles.
Install adept
package from GitHub.
# install.packages("devtools")
devtools::install_github("martakarass/adept")
We simulate a time-series x
. We assume that x
is collected at a
frequency of 100 Hz, there is one shape of a pattern within x
, each
pattern lasts 1 second, and there is no noise in collected data.
true.pattern <- cos(seq(0, 2 * pi, length.out = 100))
x <- c(true.pattern[1], replicate(10, true.pattern[-1]))
par(mfrow = c(1,2), cex = 1)
plot(true.pattern, type = "l", xlab = "", ylab = "", main = "Pattern")
plot(x, type = "l", xlab = "", ylab = "", main = "Time-series x")
We segment pattern from data. We assume that a perfect template is
available. We use a grid of potential pattern durations of {0.9, 0.95,
1.03, 1.1} seconds; the grid is imperfect in a sense it does not contain
the duration of the true pattern used in x
simulation.
library(adept)
segmentPattern(
x = x,
x.fs = 100,
template = true.pattern,
pattern.dur.seq = c(0.9, 0.95, 1.03, 1.1),
similarity.measure = "cor",
compute.template.idx = TRUE)
#> tau_i T_i sim_i template_i
#> 1 4 95 0.9987941 1
#> 2 98 103 0.9992482 1
#> 3 202 95 0.9987941 1
#> 4 297 103 0.9992482 1
#> 5 399 95 0.9987941 1
#> 6 495 103 0.9992482 1
#> 7 597 95 0.9987941 1
#> 8 697 95 0.9987941 1
#> 9 792 103 0.9992482 1
#> 10 894 95 0.9987941 1
The segmentation result is a data frame, where each row describes one identified pattern occurrence:
tau_i
- index of x
where pattern starts,T_i
- pattern duration, expressed in x
vector length,sim_i
- similarity between a template and x
,template_i
- index of a template best matched to a time-series x
(here: one template was used, hence all template_i
’s equal 1).We then assume a grid of potential pattern durations which contains the
duration of the true pattern used in data simulation. A perfect match
(sim_i = 1
) between a time-series x
and a template is obtained.
segmentPattern(
x = x,
x.fs = 100,
template = true.pattern,
pattern.dur.seq = c(0.9, 0.95, 1, 1.03, 1.1),
similarity.measure = "cor",
compute.template.idx = TRUE)
#> tau_i T_i sim_i template_i
#> 1 1 100 1 1
#> 2 100 100 1 1
#> 3 199 100 1 1
#> 4 298 100 1 1
#> 5 397 100 1 1
#> 6 496 100 1 1
#> 7 595 100 1 1
#> 8 694 100 1 1
#> 9 793 100 1 1
#> 10 892 100 1 1
We simulate a time-series x
. We assume that x
is collected at a
frequency of 100 Hz, there are two shapes of a pattern within x
,
patterns have various duration, and there is no noise in collected data.
Then, we generate x2
as a noisy version of x
.
true.pattern.1 <- cos(seq(0, 2 * pi, length.out = 200))
true.pattern.2 <- true.pattern.1
true.pattern.2[70:130] <- 2 * true.pattern.2[min(70:130)] + abs(true.pattern.2[70:130])
x <- numeric()
for (vl in seq(70, 130, by = 10)){
true.pattern.1.s <- approx(
seq(0, 1, length.out = 200),
true.pattern.1, xout = seq(0, 1, length.out = vl))$y
true.pattern.2.s <- approx(
seq(0, 1, length.out = 200),
true.pattern.2, xout = seq(0, 1, length.out = vl))$y
x <- c(x, true.pattern.1.s[-1], true.pattern.2.s[-1])
if (vl == 70) x <- c(true.pattern.1.s[1], x)
}
set.seed(1)
x2 <- x + rnorm(length(x), sd = 0.5)
par(mfrow = c(1,2), cex = 1)
plot(true.pattern.1, type = "l", xlab = "", ylab = "", main = "Pattern 1")
plot(true.pattern.2, type = "l", xlab = "", ylab = "", main = "Pattern 2")
par(mfrow = c(1,1), cex = 1)
plot(x, type = "l", xlab = "", ylab = "", main = "Time-series x")
plot(x2, type = "l", xlab = "", ylab = "", main = "Time-series x2")
We segment x
. We assume a perfect grid of potential pattern duration,
{0.7, 0.8, 0.9, 1.0, 1.1, 1.2, 1.3} seconds.
segmentPattern(
x = x,
x.fs = 100,
template = list(true.pattern.1, true.pattern.2),
pattern.dur.seq = seq(0.7, 1.3, by = 0.1),
similarity.measure = "cor",
compute.template.idx = TRUE)
#> tau_i T_i sim_i template_i
#> 1 1 70 1 1
#> 2 70 70 1 2
#> 3 139 80 1 1
#> 4 218 80 1 2
#> 5 297 90 1 1
#> 6 386 90 1 2
#> 7 475 100 1 1
#> 8 574 100 1 2
#> 9 673 110 1 1
#> 10 782 110 1 2
#> 11 891 120 1 1
#> 12 1010 120 1 2
#> 13 1129 130 1 1
#> 14 1258 130 1 2
We segment x2
.
segmentPattern(
x = x2,
x.fs = 100,
template = list(true.pattern.1, true.pattern.2),
pattern.dur.seq = seq(0.7, 1.3, by = 0.1),
similarity.measure = "cor",
compute.template.idx = TRUE)
#> tau_i T_i sim_i template_i
#> 1 1 70 0.8585451 1
#> 2 138 80 0.7624002 1
#> 3 218 80 0.7025577 2
#> 4 297 90 0.8500864 1
#> 5 390 80 0.6931671 2
#> 6 469 110 0.8286013 1
#> 7 579 90 0.6373846 2
#> 8 668 120 0.8027177 1
#> 9 787 100 0.6666713 2
#> 10 888 130 0.7894766 1
#> 11 1017 110 0.6599280 1
#> 12 1129 130 0.7938183 1
#> 13 1267 120 0.7655408 2
We now use x.adept.ma.W
argument to smooth x2
before similarity
matrix computation in the segmentation procedure (see ?segmentPattern
for details). We also assume a more dense grid of potential pattern
duration. We observe that sim_i
values obtained are higher than in the
previous segmentation case.
par(mfrow = c(1,1), cex = 1)
plot(windowSmooth(x = x2, x.fs = 100, W = 0.1),
type = "l", xlab = "", ylab = "", main = "Time-series x2 smoothed")
segmentPattern(
x = x2,
x.fs = 100,
template = list(true.pattern.1, true.pattern.2),
pattern.dur.seq = 70:130 * 0.01,
similarity.measure = "cor",
x.adept.ma.W = 0.1,
compute.template.idx = TRUE)
#> tau_i T_i sim_i template_i
#> 1 1 70 0.9865778 1
#> 2 70 70 0.9533684 2
#> 3 139 79 0.9683054 1
#> 4 217 80 0.9748040 2
#> 5 296 94 0.9802473 1
#> 6 391 82 0.9462213 2
#> 7 472 106 0.9855837 1
#> 8 578 93 0.9608881 2
#> 9 670 115 0.9887225 1
#> 10 784 107 0.9562694 2
#> 11 896 113 0.9734575 1
#> 12 1008 127 0.9703118 1
#> 13 1134 116 0.9606235 1
#> 14 1266 122 0.9593345 2
Vignettes are available to better demonstrate package methods usgae.
Vignette Introduction to adept
package
introduces ADEPT algorithm and demonstrates the usage of
segmentPattern
function which implements ADEPT approach. Here, we
focus on examples with simulated data.
Vignette Walking strides segmentation with
adept
provides an example of segmentation of walking strides (two
consecutive steps) in sub-second accelerometry data with adept
package. The exemplary dataset is a part of the adeptdata
package.
We demonstrate that ADEPT can be used to perform automatic and
precise walking stride segmentation from data collected during a
combination of running, walking and resting exercises. We introduce
how to segment data:
adeptdata
package),Add the following code to your website.
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