README.md
In patrickreidy/ag501: Carstens AG501 Electromagnetic Articulograph
ag501
An R package for reading and analyzing kinematic data recorded from a Carstens
AG501 electromagnetic articulograph (EMA). Metadata and positional data
(x-, y-, and z-dimensions, and rotation) recorded from EMA sensors are
structured as a nested data frame. Functions are provided for filtering
positional data (e.g., low-pass Butterworth filter) or for calculating derived
time-series (e.g., velocity or acceleration profiles from either forward or
central differencing).
Installation
To install the current development from GitHub:
devtools::install_github("ag501", username = "patrickreidy")
Examples
Metadata for Greek clusters
GreekClusters()
# # A tibble: 4 x 9
# Sweep Word Cluster Vowel Anchor Onset Offset
# <chr> <chr> <chr> <chr> <chr> <dbl> <dbl>
# 1 0021 skavi sk a v 15.328 16.315
# 2 0022 skavi sk a v 14.310 15.356
# 3 0021 spala sp a l 6.455 7.382
# 4 0022 spala sp a l 8.004 8.947
# # ... with 2 more variables: POS <chr>, TXT <chr>
Read sweeps for the data set of Greek clusters
# This sensor array utilizes 7 of the 8 sockets available in a Carstens sensin box:
# 1. HL: sensor attached to the left side of the head-correction glasses
# 2. HR: sensor attached to the right side of the head-correction glasses
# 3. TB: sensor attached to the tongue back (body)
# 4. TD: sensor attached to the tongue dorsum
# 5. TT: sensor attached to the tongue tip
# 6. UL: sensor attached to the upper lip
# 7. LL: sensor attached to the lower lip
# Note: the sensors in sockets 3 through 7 (i.e., TB through LL) were all attached
# within the midsagittal plane, as best as possible.
sensor_array <- c("HL", "HR", "TB", "TD", "TT", "UL", "LL")
sweeps <- ReadSweep(GreekClusters()$POS, GreekClusters()$TXT, sensors = sensor_array)
greek <- dplyr::bind_cols(
dplyr::select(GreekClusters(), Sweep, Word, Cluster, Onset, Offset),
dplyr::select(sweeps, SamplingRate, SensorData)
)
Apply a low-pass 5th-order Butterworth filter to all channels in SensorData
greek %>%
dplyr::mutate(LowPassData = Butterworth(SensorData, SamplingRate,
order = 5, cutoffs = 20, type = "low"))
# # A tibble: 4 x 8
# Sweep Word Cluster Onset Offset SamplingRate SensorData LowPassData
# <chr> <chr> <chr> <dbl> <dbl> <dbl> <list> <list>
# 1 0021 skavi sk 15.328 16.315 250 <tibble [5,021 x 49]> <tibble [5,021 x 49]>
# 2 0022 skavi sk 14.310 15.356 250 <tibble [4,740 x 49]> <tibble [4,740 x 49]>
# 3 0021 spala sp 6.455 7.382 250 <tibble [5,021 x 49]> <tibble [5,021 x 49]>
# 4 0022 spala sp 8.004 8.947 250 <tibble [4,740 x 49]> <tibble [4,740 x 49]>
Time-slice the low-pass filtered data to the interval 0.25 seconds before and after the word
greek %>%
dplyr::mutate(LowPassData = Butterworth(SensorData, SamplingRate,
order = 5, cutoffs = 20, type = "low")) %>%
dplyr::mutate(WordData = TimeSlice(LowPassData, from = Onset-0.25, to = Offset+0.25))
# # A tibble: 4 x 9
# Sweep Word ... SensorData LowPassData WordData
# <chr> <chr> ... <list> <list> <list>
# 1 0021 skavi ... <tibble [5,021 x 49]> <tibble [5,021 x 49]> <tibble [372 x 49]>
# 2 0022 skavi ... <tibble [4,740 x 49]> <tibble [4,740 x 49]> <tibble [387 x 49]>
# 3 0021 spala ... <tibble [5,021 x 49]> <tibble [5,021 x 49]> <tibble [356 x 49]>
# 4 0022 spala ... <tibble [4,740 x 49]> <tibble [4,740 x 49]> <tibble [361 x 49]>
Estimate velocity and acceleration profiles for each channel in WordData
greek %>%
dplyr::mutate(LowPassData = Butterworth(SensorData, SamplingRate,
order = 5, cutoffs = 20, type = "low")) %>%
dplyr::mutate(WordData = TimeSlice(LowPassData, from = Onset-0.25, to = Offset+0.25)) %>%
dplyr::mutate(VelocityData = CentralDifference(WordData, n = 1, order = 1)) %>%
dplyr::mutate(AccelerationData = CentralDifference(WordData, n = 1, order = 2))
# # A tibble: 4 x 11
# Sweep Word ... WordData VelocityData AccelerationData
# <chr> <chr> ... <list> <list> <list>
# 1 0021 skavi ... <tibble [372 x 49]> <tibble [372 x 49]> <tibble [372 x 49]>
# 2 0022 skavi ... <tibble [387 x 49]> <tibble [387 x 49]> <tibble [387 x 49]>
# 3 0021 spala ... <tibble [356 x 49]> <tibble [356 x 49]> <tibble [356 x 49]>
# 4 0022 spala ... <tibble [361 x 49]> <tibble [361 x 49]> <tibble [361 x 49]>
Compute lip aperture in vertical dimension, sagittal plane, and three dimensions
LA <- function(x, ...) {
UseMethod("LA", x)
}
LA.data.frame <- function(x, ...) {
.la <-
dplyr::select(x, Time, ULx, ULy, ULz, LLx, LLy, LLz) %>%
dplyr::mutate(LAz = sqrt((ULz-LLz)^2),
LAxz = sqrt((ULx-LLx)^2 + (ULz-LLz)^2),
LAxyz = sqrt((ULx-LLx)^2 + (ULy-LLy)^2 + (ULz-LLz)^2))
return(.la)
}
LA.list <- function(x, ...) {
.la <- purrr::map(x, LA)
return(.la)
}
greek %>%
dplyr::filter(Cluster == "sp") %>%
dplyr::mutate(LowPassData = Butterworth(SensorData, SamplingRate,
order = 5, cutoffs = 20, type = "low")) %>%
dplyr::mutate(WordData = TimeSlice(LowPassData, from = Onset-0.25, to = Offset+0.25)) %>%
dplyr::mutate(LA = LA(WordData))
# # A tibble: 2 x 10
# Sweep Word ... WordData LA
# <chr> <chr> ... <list> <list>
# 1 0021 spala ... <tibble [356 x 49]> <tibble [356 x 10]>
# 2 0022 spala ... <tibble [361 x 49]> <tibble [361 x 10]>
Compute tongue-tip speed in sagittal (xz) plane, which is relevant for /s/
TT <- function(x, ...) {
UseMethod("TT", x)
}
TT.data.frame <- function(x, samplingRate, ...) {
.tt <-
dplyr::select(x, Time, TTx, TTy, TTz) %>%
dplyr::mutate(TTx_vel = CentralDifference(TTx, samplingRate = samplingRate)) %>%
dplyr::mutate(TTy_vel = CentralDifference(TTy, samplingRate = samplingRate)) %>%
dplyr::mutate(TTz_vel = CentralDifference(TTz, samplingRate = samplingRate)) %>%
dplyr::mutate(TTz_spd = sqrt(TTz_vel^2)) %>%
dplyr::mutate(TTxz_spd = sqrt(TTx_vel^2 + TTz_vel^2)) %>%
dplyr::mutate(TTxyz_spd = sqrt(TTx_vel^2 + TTy_vel^2 + TTz_vel^2))
return(.tt)
}
TT.list <- function(x, samplingRate, ...) {
.tt <- purrr::map2(x, samplingRate, TT)
return(.tt)
}
greek %>%
dplyr::mutate(LowPassData = Butterworth(SensorData, SamplingRate,
order = 5, cutoffs = 20, type = "low")) %>%
dplyr::mutate(WordData = TimeSlice(LowPassData, from = Onset-0.25, to = Offset+0.25)) %>%
dplyr::mutate(TT = TT(WordData, SamplingRate))
# # A tibble: 4 x 10
# Sweep Word ... WordData TT
# <chr> <chr> ... <list> <list>
# 1 0021 skavi ... <tibble [372 x 49]> <tibble [372 x 10]>
# 2 0022 skavi ... <tibble [387 x 49]> <tibble [387 x 10]>
# 3 0021 spala ... <tibble [356 x 49]> <tibble [356 x 10]>
# 4 0022 spala ... <tibble [361 x 49]> <tibble [361 x 10]>
Find gestural landmarks for movement of the TT sensor
# Landmarks in the sagittal plane only
greek %>%
dplyr::mutate(LowPassData = Butterworth(SensorData, SamplingRate,
order = 5, cutoffs = 20, type = "low")) %>%
dplyr::mutate(WordData = TimeSlice(LowPassData, from = Onset-0.25, to = Offset+0.25)) %>%
dplyr::mutate(TT = TT(WordData, SamplingRate)) %>%
dplyr::mutate(LandmarksTT = FindLandmarks(TT, channels = "TTxz_spd", onsetNear = Onset)) %>%
dplyr::select(Sweep, Word, Onset, SamplingRate, LandmarksTT) %>%
tidyr::unnest()
# # A tibble: 16 x 8
# Sweep Word Onset SamplingRate Channel Landmark Time Value
# <chr> <chr> <dbl> <dbl> <chr> <chr> <dbl> <dbl>
# 1 0021 skavi 15.328 250 TTxz_spd GestureOnset 15.328 53.24821
# 2 0021 skavi 15.328 250 TTxz_spd TargetOnset 15.472 37.50784
# 3 0021 skavi 15.328 250 TTxz_spd TargetOffset 15.500 26.18081
# 4 0021 skavi 15.328 250 TTxz_spd GestureOffset 15.696 31.36240
# 5 0022 skavi 14.310 250 TTxz_spd GestureOnset 14.312 45.83411
# 6 0022 skavi 14.310 250 TTxz_spd TargetOnset 14.476 32.10593
# 7 0022 skavi 14.310 250 TTxz_spd TargetOffset 14.512 30.39915
# 8 0022 skavi 14.310 250 TTxz_spd GestureOffset 14.720 32.50779
# 9 0021 spala 6.455 250 TTxz_spd GestureOnset 6.464 54.75046
# 10 0021 spala 6.455 250 TTxz_spd TargetOnset 6.596 42.25295
# 11 0021 spala 6.455 250 TTxz_spd TargetOffset 6.624 38.85320
# 12 0021 spala 6.455 250 TTxz_spd GestureOffset 6.788 36.93888
# 13 0022 spala 8.004 250 TTxz_spd GestureOnset 7.996 62.27941
# 14 0022 spala 8.004 250 TTxz_spd TargetOnset 8.140 36.79614
# 15 0022 spala 8.004 250 TTxz_spd TargetOffset 8.236 30.07731
# 16 0022 spala 8.004 250 TTxz_spd GestureOffset 8.412 56.81165
# Landmarks in vertical dimension, sagittal plane, and three-dimensions
greek %>%
dplyr::mutate(LowPassData = Butterworth(SensorData, SamplingRate,
order = 5, cutoffs = 20, type = "low")) %>%
dplyr::mutate(WordData = TimeSlice(LowPassData, from = Onset-0.25, to = Offset+0.25)) %>%
dplyr::mutate(TT = TT(WordData, SamplingRate)) %>%
dplyr::mutate(LandmarksTT = FindLandmarks(TT, matches = "_spd", onsetNear = Onset)) %>%
dplyr::select(Sweep, Word, Onset, SamplingRate, LandmarksTT) %>%
tidyr::unnest()
# # A tibble: 48 x 8
# Sweep Word Onset SamplingRate Channel Landmark Time Value
# <chr> <chr> <dbl> <dbl> <chr> <chr> <dbl> <dbl>
# 1 0021 skavi 15.328 250 TTz_spd GestureOnset 15.316 27.60756
# 2 0021 skavi 15.328 250 TTz_spd TargetOnset 15.476 23.27646
# 3 0021 skavi 15.328 250 TTz_spd TargetOffset 15.500 23.80788
# 4 0021 skavi 15.328 250 TTz_spd GestureOffset 15.692 22.94749
# 5 0021 skavi 15.328 250 TTxz_spd GestureOnset 15.328 53.24821
# 6 0021 skavi 15.328 250 TTxz_spd TargetOnset 15.472 37.50784
# 7 0021 skavi 15.328 250 TTxz_spd TargetOffset 15.500 26.18081
# 8 0021 skavi 15.328 250 TTxz_spd GestureOffset 15.696 31.36240
# 9 0021 skavi 15.328 250 TTxyz_spd GestureOnset 15.328 53.24979
# 10 0021 skavi 15.328 250 TTxyz_spd TargetOnset 15.472 38.08035
# # ... with 38 more rows
patrickreidy/ag501 documentation built on May 30, 2019, 10:01 p.m.
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Note that we can't provide technical support on individual packages. You should contact the package authors for that.
ag501
An R package for reading and analyzing kinematic data recorded from a Carstens AG501 electromagnetic articulograph (EMA). Metadata and positional data (x-, y-, and z-dimensions, and rotation) recorded from EMA sensors are structured as a nested data frame. Functions are provided for filtering positional data (e.g., low-pass Butterworth filter) or for calculating derived time-series (e.g., velocity or acceleration profiles from either forward or central differencing).
Installation
To install the current development from GitHub:
devtools::install_github("ag501", username = "patrickreidy")
Examples
Metadata for Greek clusters
GreekClusters()
# # A tibble: 4 x 9
# Sweep Word Cluster Vowel Anchor Onset Offset
# <chr> <chr> <chr> <chr> <chr> <dbl> <dbl>
# 1 0021 skavi sk a v 15.328 16.315
# 2 0022 skavi sk a v 14.310 15.356
# 3 0021 spala sp a l 6.455 7.382
# 4 0022 spala sp a l 8.004 8.947
# # ... with 2 more variables: POS <chr>, TXT <chr>
Read sweeps for the data set of Greek clusters
# This sensor array utilizes 7 of the 8 sockets available in a Carstens sensin box:
# 1. HL: sensor attached to the left side of the head-correction glasses
# 2. HR: sensor attached to the right side of the head-correction glasses
# 3. TB: sensor attached to the tongue back (body)
# 4. TD: sensor attached to the tongue dorsum
# 5. TT: sensor attached to the tongue tip
# 6. UL: sensor attached to the upper lip
# 7. LL: sensor attached to the lower lip
# Note: the sensors in sockets 3 through 7 (i.e., TB through LL) were all attached
# within the midsagittal plane, as best as possible.
sensor_array <- c("HL", "HR", "TB", "TD", "TT", "UL", "LL")
sweeps <- ReadSweep(GreekClusters()$POS, GreekClusters()$TXT, sensors = sensor_array)
greek <- dplyr::bind_cols(
dplyr::select(GreekClusters(), Sweep, Word, Cluster, Onset, Offset),
dplyr::select(sweeps, SamplingRate, SensorData)
)
Apply a low-pass 5th-order Butterworth filter to all channels in SensorData
greek %>%
dplyr::mutate(LowPassData = Butterworth(SensorData, SamplingRate,
order = 5, cutoffs = 20, type = "low"))
# # A tibble: 4 x 8
# Sweep Word Cluster Onset Offset SamplingRate SensorData LowPassData
# <chr> <chr> <chr> <dbl> <dbl> <dbl> <list> <list>
# 1 0021 skavi sk 15.328 16.315 250 <tibble [5,021 x 49]> <tibble [5,021 x 49]>
# 2 0022 skavi sk 14.310 15.356 250 <tibble [4,740 x 49]> <tibble [4,740 x 49]>
# 3 0021 spala sp 6.455 7.382 250 <tibble [5,021 x 49]> <tibble [5,021 x 49]>
# 4 0022 spala sp 8.004 8.947 250 <tibble [4,740 x 49]> <tibble [4,740 x 49]>
Time-slice the low-pass filtered data to the interval 0.25 seconds before and after the word
greek %>%
dplyr::mutate(LowPassData = Butterworth(SensorData, SamplingRate,
order = 5, cutoffs = 20, type = "low")) %>%
dplyr::mutate(WordData = TimeSlice(LowPassData, from = Onset-0.25, to = Offset+0.25))
# # A tibble: 4 x 9
# Sweep Word ... SensorData LowPassData WordData
# <chr> <chr> ... <list> <list> <list>
# 1 0021 skavi ... <tibble [5,021 x 49]> <tibble [5,021 x 49]> <tibble [372 x 49]>
# 2 0022 skavi ... <tibble [4,740 x 49]> <tibble [4,740 x 49]> <tibble [387 x 49]>
# 3 0021 spala ... <tibble [5,021 x 49]> <tibble [5,021 x 49]> <tibble [356 x 49]>
# 4 0022 spala ... <tibble [4,740 x 49]> <tibble [4,740 x 49]> <tibble [361 x 49]>
Estimate velocity and acceleration profiles for each channel in WordData
greek %>%
dplyr::mutate(LowPassData = Butterworth(SensorData, SamplingRate,
order = 5, cutoffs = 20, type = "low")) %>%
dplyr::mutate(WordData = TimeSlice(LowPassData, from = Onset-0.25, to = Offset+0.25)) %>%
dplyr::mutate(VelocityData = CentralDifference(WordData, n = 1, order = 1)) %>%
dplyr::mutate(AccelerationData = CentralDifference(WordData, n = 1, order = 2))
# # A tibble: 4 x 11
# Sweep Word ... WordData VelocityData AccelerationData
# <chr> <chr> ... <list> <list> <list>
# 1 0021 skavi ... <tibble [372 x 49]> <tibble [372 x 49]> <tibble [372 x 49]>
# 2 0022 skavi ... <tibble [387 x 49]> <tibble [387 x 49]> <tibble [387 x 49]>
# 3 0021 spala ... <tibble [356 x 49]> <tibble [356 x 49]> <tibble [356 x 49]>
# 4 0022 spala ... <tibble [361 x 49]> <tibble [361 x 49]> <tibble [361 x 49]>
Compute lip aperture in vertical dimension, sagittal plane, and three dimensions
LA <- function(x, ...) {
UseMethod("LA", x)
}
LA.data.frame <- function(x, ...) {
.la <-
dplyr::select(x, Time, ULx, ULy, ULz, LLx, LLy, LLz) %>%
dplyr::mutate(LAz = sqrt((ULz-LLz)^2),
LAxz = sqrt((ULx-LLx)^2 + (ULz-LLz)^2),
LAxyz = sqrt((ULx-LLx)^2 + (ULy-LLy)^2 + (ULz-LLz)^2))
return(.la)
}
LA.list <- function(x, ...) {
.la <- purrr::map(x, LA)
return(.la)
}
greek %>%
dplyr::filter(Cluster == "sp") %>%
dplyr::mutate(LowPassData = Butterworth(SensorData, SamplingRate,
order = 5, cutoffs = 20, type = "low")) %>%
dplyr::mutate(WordData = TimeSlice(LowPassData, from = Onset-0.25, to = Offset+0.25)) %>%
dplyr::mutate(LA = LA(WordData))
# # A tibble: 2 x 10
# Sweep Word ... WordData LA
# <chr> <chr> ... <list> <list>
# 1 0021 spala ... <tibble [356 x 49]> <tibble [356 x 10]>
# 2 0022 spala ... <tibble [361 x 49]> <tibble [361 x 10]>
Compute tongue-tip speed in sagittal (xz) plane, which is relevant for /s/
TT <- function(x, ...) {
UseMethod("TT", x)
}
TT.data.frame <- function(x, samplingRate, ...) {
.tt <-
dplyr::select(x, Time, TTx, TTy, TTz) %>%
dplyr::mutate(TTx_vel = CentralDifference(TTx, samplingRate = samplingRate)) %>%
dplyr::mutate(TTy_vel = CentralDifference(TTy, samplingRate = samplingRate)) %>%
dplyr::mutate(TTz_vel = CentralDifference(TTz, samplingRate = samplingRate)) %>%
dplyr::mutate(TTz_spd = sqrt(TTz_vel^2)) %>%
dplyr::mutate(TTxz_spd = sqrt(TTx_vel^2 + TTz_vel^2)) %>%
dplyr::mutate(TTxyz_spd = sqrt(TTx_vel^2 + TTy_vel^2 + TTz_vel^2))
return(.tt)
}
TT.list <- function(x, samplingRate, ...) {
.tt <- purrr::map2(x, samplingRate, TT)
return(.tt)
}
greek %>%
dplyr::mutate(LowPassData = Butterworth(SensorData, SamplingRate,
order = 5, cutoffs = 20, type = "low")) %>%
dplyr::mutate(WordData = TimeSlice(LowPassData, from = Onset-0.25, to = Offset+0.25)) %>%
dplyr::mutate(TT = TT(WordData, SamplingRate))
# # A tibble: 4 x 10
# Sweep Word ... WordData TT
# <chr> <chr> ... <list> <list>
# 1 0021 skavi ... <tibble [372 x 49]> <tibble [372 x 10]>
# 2 0022 skavi ... <tibble [387 x 49]> <tibble [387 x 10]>
# 3 0021 spala ... <tibble [356 x 49]> <tibble [356 x 10]>
# 4 0022 spala ... <tibble [361 x 49]> <tibble [361 x 10]>
Find gestural landmarks for movement of the TT sensor
# Landmarks in the sagittal plane only
greek %>%
dplyr::mutate(LowPassData = Butterworth(SensorData, SamplingRate,
order = 5, cutoffs = 20, type = "low")) %>%
dplyr::mutate(WordData = TimeSlice(LowPassData, from = Onset-0.25, to = Offset+0.25)) %>%
dplyr::mutate(TT = TT(WordData, SamplingRate)) %>%
dplyr::mutate(LandmarksTT = FindLandmarks(TT, channels = "TTxz_spd", onsetNear = Onset)) %>%
dplyr::select(Sweep, Word, Onset, SamplingRate, LandmarksTT) %>%
tidyr::unnest()
# # A tibble: 16 x 8
# Sweep Word Onset SamplingRate Channel Landmark Time Value
# <chr> <chr> <dbl> <dbl> <chr> <chr> <dbl> <dbl>
# 1 0021 skavi 15.328 250 TTxz_spd GestureOnset 15.328 53.24821
# 2 0021 skavi 15.328 250 TTxz_spd TargetOnset 15.472 37.50784
# 3 0021 skavi 15.328 250 TTxz_spd TargetOffset 15.500 26.18081
# 4 0021 skavi 15.328 250 TTxz_spd GestureOffset 15.696 31.36240
# 5 0022 skavi 14.310 250 TTxz_spd GestureOnset 14.312 45.83411
# 6 0022 skavi 14.310 250 TTxz_spd TargetOnset 14.476 32.10593
# 7 0022 skavi 14.310 250 TTxz_spd TargetOffset 14.512 30.39915
# 8 0022 skavi 14.310 250 TTxz_spd GestureOffset 14.720 32.50779
# 9 0021 spala 6.455 250 TTxz_spd GestureOnset 6.464 54.75046
# 10 0021 spala 6.455 250 TTxz_spd TargetOnset 6.596 42.25295
# 11 0021 spala 6.455 250 TTxz_spd TargetOffset 6.624 38.85320
# 12 0021 spala 6.455 250 TTxz_spd GestureOffset 6.788 36.93888
# 13 0022 spala 8.004 250 TTxz_spd GestureOnset 7.996 62.27941
# 14 0022 spala 8.004 250 TTxz_spd TargetOnset 8.140 36.79614
# 15 0022 spala 8.004 250 TTxz_spd TargetOffset 8.236 30.07731
# 16 0022 spala 8.004 250 TTxz_spd GestureOffset 8.412 56.81165
# Landmarks in vertical dimension, sagittal plane, and three-dimensions
greek %>%
dplyr::mutate(LowPassData = Butterworth(SensorData, SamplingRate,
order = 5, cutoffs = 20, type = "low")) %>%
dplyr::mutate(WordData = TimeSlice(LowPassData, from = Onset-0.25, to = Offset+0.25)) %>%
dplyr::mutate(TT = TT(WordData, SamplingRate)) %>%
dplyr::mutate(LandmarksTT = FindLandmarks(TT, matches = "_spd", onsetNear = Onset)) %>%
dplyr::select(Sweep, Word, Onset, SamplingRate, LandmarksTT) %>%
tidyr::unnest()
# # A tibble: 48 x 8
# Sweep Word Onset SamplingRate Channel Landmark Time Value
# <chr> <chr> <dbl> <dbl> <chr> <chr> <dbl> <dbl>
# 1 0021 skavi 15.328 250 TTz_spd GestureOnset 15.316 27.60756
# 2 0021 skavi 15.328 250 TTz_spd TargetOnset 15.476 23.27646
# 3 0021 skavi 15.328 250 TTz_spd TargetOffset 15.500 23.80788
# 4 0021 skavi 15.328 250 TTz_spd GestureOffset 15.692 22.94749
# 5 0021 skavi 15.328 250 TTxz_spd GestureOnset 15.328 53.24821
# 6 0021 skavi 15.328 250 TTxz_spd TargetOnset 15.472 37.50784
# 7 0021 skavi 15.328 250 TTxz_spd TargetOffset 15.500 26.18081
# 8 0021 skavi 15.328 250 TTxz_spd GestureOffset 15.696 31.36240
# 9 0021 skavi 15.328 250 TTxyz_spd GestureOnset 15.328 53.24979
# 10 0021 skavi 15.328 250 TTxyz_spd TargetOnset 15.472 38.08035
# # ... with 38 more rows
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