Find peaks in the acceleration signal.
Arguments
- data
An
impactr_dataobject, as obtained with read_acc().- vector
A character string indicating in which acceleration vector to find the peaks. Can be "resultant", "vertical" or "all".
- min_height
The minimum height of the peaks (in g).
- min_dist
The minimum horizontal distance between peaks (in seconds).
Details
The default values of the filter parameters are matching the filter
used in the paper by Veras et al. that developed the mechanical loading
prediction equations (see References).
When the vector parameter is set to "all", there may contain
NA values in the resultant_peak_acc and/or
vertical_peak_acc at the timestamps in which a peak value for that
vector could not be identified.
The default values of min_height and min_dist are
matching the criteria used in the paper by Veras et al. that developed the
mechanical loading prediction equations (see References)
References
Veras L, Diniz-Sousa F, Boppre G, Devezas V, Santos-Sousa H, Preto J, Machado L, Vilas- Boas JP, Oliveira J, Fonseca H. Accelerometer-based prediction of skeletal mechanical loading during walking in normal weight to severely obese subjects. Osteoporosis International. 2020. 31(7):1239- 1250. doi:10.1007/s00198-020-05295-2 .
Examples
data <- read_acc(impactr_example("hip-raw.csv"))
data <- use_resultant(data)
find_peaks(data, vector = "resultant")
#> # Start time: 2021-04-06 15:43:00
#> # Sampling frequency: 100Hz
#> # Accelerometer placement: Non-specified
#> # Subject body mass: Non-specified
#> # Filter: No filter applied
#> # Data dimensions: 303 × 2
#> timestamp resultant_peak_acc
#> <dttm> <dbl>
#> 1 2021-04-06 15:43:00 2.24
#> 2 2021-04-06 15:43:00 1.43
#> 3 2021-04-06 15:43:02 1.49
#> 4 2021-04-06 15:43:03 1.68
#> 5 2021-04-06 15:43:04 1.49
#> 6 2021-04-06 15:43:04 1.30
#> 7 2021-04-06 15:43:05 2.13
#> 8 2021-04-06 15:43:05 1.34
#> 9 2021-04-06 15:43:06 1.39
#> 10 2021-04-06 15:43:07 1.46
#> # … with 293 more rows
#> # ℹ Use `print(n = ...)` to see more rows