shorts (version 3.2.0)

predict_kinematics: Kinematics prediction functions

Description

Predicts kinematic from known MSS and MAC parameters

Usage

predict_velocity_at_time(time, MSS, MAC)
predict_distance_at_time(time, MSS, MAC)
predict_acceleration_at_time(time, MSS, MAC)
predict_time_at_distance(distance, MSS, MAC)
predict_time_at_distance_FV(
  distance,
  F0,
  V0,
  bodymass = 75,
  inertia = 0,
  resistance = 0,
  ...
)
predict_velocity_at_distance(distance, MSS, MAC)
predict_acceleration_at_distance(distance, MSS, MAC)
predict_acceleration_at_velocity(velocity, MSS, MAC)
predict_air_resistance_at_time(time, MSS, MAC, ...)
predict_air_resistance_at_distance(distance, MSS, MAC, ...)
predict_force_at_velocity(
  velocity,
  MSS,
  MAC,
  bodymass = 75,
  inertia = 0,
  resistance = 0,
  ...
)
predict_force_at_time(
  time,
  MSS,
  MAC,
  bodymass = 75,
  inertia = 0,
  resistance = 0,
  ...
)
predict_force_at_distance(
  distance,
  MSS,
  MAC,
  bodymass = 75,
  inertia = 0,
  resistance = 0,
  ...
)
predict_power_at_distance(
  distance,
  MSS,
  MAC,
  bodymass = 75,
  inertia = 0,
  resistance = 0,
  ...
)
predict_power_at_time(
  time,
  MSS,
  MAC,
  bodymass = 75,
  inertia = 0,
  resistance = 0,
  ...
)
predict_relative_power_at_distance(
  distance,
  MSS,
  MAC,
  bodymass = 75,
  inertia = 0,
  resistance = 0,
  ...
)
predict_relative_power_at_time(
  time,
  MSS,
  MAC,
  bodymass = 75,
  inertia = 0,
  resistance = 0,
  ...
)
predict_work_till_time(time, ...)
predict_work_till_distance(distance, ...)
predict_kinematics(
  object = NULL,
  MSS,
  MAC,
  max_time = 6,
  frequency = 100,
  bodymass = 75,
  inertia = 0,
  resistance = 0,
  add_inertia_to_vertical = TRUE,
  ...
)

Value

Numeric vector

Data frame with kinetic and kinematic variables

Arguments

time, distance, velocity

Numeric vectors

MSS, MAC

Numeric vectors. Model parameters

F0, V0

Numeric vectors. FV profile parameters

bodymass

Body mass in kg. Used to calculate relative power and forwarded to get_air_resistance

inertia

External inertia in kg (for example a weight vest, or a sled). Not included in the air resistance calculation

resistance

External horizontal resistance in Newtons (for example tether device or a sled friction resistance)

...

Arguments passed on to get_air_resistance

bodyheight

In meters (m). Default is 1.75m

barometric_pressure

In Torrs. Default is 760Torrs

air_temperature

In Celzius (C). Default is 25C

wind_velocity

In meters per second (m/s). Use negative number as head wind, and positive number as back wind. Default is 0m/s (no wind)

object

If shorts_model object is provided, estimated parameters will be used. Otherwise provide MSS and MAC parameters

max_time

Predict from 0 to max_time. Default is 6seconds

frequency

Number of samples within one second. Default is 100Hz

add_inertia_to_vertical

Should inertia be added to bodymass when calculating vertical force? Use TRUE (Default) when using weight vest, and FALSE when dragging sled

References

Haugen TA, Tønnessen E, Seiler SK. 2012. The Difference Is in the Start: Impact of Timing and Start Procedure on Sprint Running Performance: Journal of Strength and Conditioning Research 26:473–479. DOI: 10.1519/JSC.0b013e318226030b.

Jovanović, M., Vescovi, J.D. (2020). shorts: An R Package for Modeling Short Sprints. Preprint available at SportRxiv. https://doi.org/10.31236/osf.io/4jw62

Samozino P. 2018. A Simple Method for Measuring Force, Velocity and Power Capabilities and Mechanical Effectiveness During Sprint Running. In: Morin J-B, Samozino P eds. Biomechanics of Training and Testing. Cham: Springer International Publishing, 237–267. DOI: 10.1007/978-3-319-05633-3_11.

Examples

MSS <- 8
MAC <- 9

time_seq <- seq(0, 6, length.out = 10)

df <- data.frame(
  time = time_seq,
  distance_at_time = predict_distance_at_time(time_seq, MSS, MAC),
  velocity_at_time = predict_velocity_at_time(time_seq, MSS, MAC),
  acceleration_at_time = predict_acceleration_at_time(time_seq, MSS, MAC)
)

df$time_at_distance <- predict_time_at_distance(df$distance_at_time, MSS, MAC)
df$velocity_at_distance <- predict_velocity_at_distance(df$distance_at_time, MSS, MAC)
df$acceleration_at_distance <- predict_acceleration_at_distance(df$distance_at_time, MSS, MAC)
df$acceleration_at_velocity <- predict_acceleration_at_velocity(df$velocity_at_time, MSS, MAC)

# Power calculation uses shorts::get_air_resistance function and its defaults
# values to calculate power. Use the ... to setup your own parameters for power
# calculations
df$power_at_time <- predict_power_at_time(
  time = df$time, MSS = MSS, MAC = MAC,
  # Check shorts::get_air_resistance for available params
  bodymass = 100, bodyheight = 1.85
)

df

# Example for predict_kinematics
split_times <- data.frame(
  distance = c(5, 10, 20, 30, 35),
  time = c(1.20, 1.96, 3.36, 4.71, 5.35)
)

# Simple model
simple_model <- with(
  split_times,
  model_timing_gates(distance, time)
)

predict_kinematics(simple_model)