Package 'PhysioMoCap'

Description

Align EMG to motion-capture sampling grid

Usage

alignEMGtoMoCap(
  emg,
  emg_sampling_rate,
  mocap_length,
  mocap_sampling_rate,
  method = "linear"
)

Arguments

Value

Matrix of aligned EMG data with mocap_length rows.

References

Merletti R, Parker PA (2004). "Electromyography: Physiology, Engineering, and Non-Invasive Applications." IEEE Press/Wiley.

See Also

integrateEMGMoCap() for full EMG-MoCap integration, resampleSignal() for general signal resampling.

Examples

emg <- matrix(rnorm(2000), ncol = 2)
emg_aligned <- alignEMGtoMoCap(emg, 1000, mocap_length = 200,
                               mocap_sampling_rate = 100)

Description

Convenience wrapper that filters GRF components and computes COP, loading rate, and impulse summaries.

Usage

analyzeForcePlate(
  forces,
  moments = NULL,
  sampling_rate,
  cutoff = 20,
  threshold = 20,
  order = 4,
  filter_method = c("butterworth", "moving_average"),
  origin = c(0, 0, 0)
)

Arguments

Value

A list with elements: filtered_forces, cop, loading_rate, impulse, and summary.

References

Winter DA (2009). "Biomechanics and Motor Control of Human Movement." 4th ed. John Wiley & Sons.

See Also

filterGRF() for ground reaction force filtering, calculateCOP() for center of pressure computation, computeLoadingRate() for loading rate analysis, analyzeForcePlatePE() for PhysioExperiment-based analysis.

Examples

f <- cbind(Fx = rep(0, 1000), Fy = rep(0, 1000),
           Fz = c(rep(0, 200), abs(sin(seq(0, pi, length.out = 600))) * 800,
                  rep(0, 200)))
out <- analyzeForcePlate(f, sampling_rate = 1000)

Description

Convenience wrapper for analyzeForcePlate() when force and moment components are stored in PhysioExperiment assays.

Usage

analyzeForcePlatePE(
  pe,
  force_assay = "grf",
  moment_assay = NULL,
  plate_index = 1L,
  sampling_rate = NULL,
  ...
)

Arguments

Value

A forceplate_analysis object (single plate) or forceplate_analysis_multi (when plate_index = "all").

References

Winter DA (2009). "Biomechanics and Motor Control of Human Movement." 4th ed. John Wiley & Sons.

See Also

analyzeForcePlate() for matrix-based force plate analysis, print.forceplate_analysis() for displaying results.

Examples

## Not run: 
pe <- readC3D("trial.c3d", include_analog = TRUE)
out <- analyzeForcePlatePE(pe)

## End(Not run)

Description

Performs a compact quality and metadata checklist so beginners can quickly identify missing requirements before running kinematics or kinetics.

Usage

assessMoCapReadiness(
  pe,
  required_assays = c("position_x", "position_y", "position_z"),
  min_frames = 100,
  min_markers = 5,
  min_sampling_rate = 50,
  max_missing_rate = 0.05
)

Arguments

Value

An S3 object of class "mocap_readiness" with score, grade, checks, and summary metrics.

References

Winter DA (2009). "Biomechanics and Motor Control of Human Movement." 4th ed. John Wiley & Sons.

See Also

print.mocap_readiness() for formatted display of readiness results, quickStartMoCap() for complete getting-started workflow, demoMoCapData() for generating demo data.

Examples

demo <- demoMoCapData(seed = 1)
report <- assessMoCapReadiness(demo$mocap)
report

Description

Loads an OpenSim session and performs batch movement analysis using a TaskSchema for event detection and normalization.

Usage

batch_analyze_opensim(
  session_dir,
  schema,
  signal_mapping = list(),
  trials = NULL,
  normalize_method = NULL,
  ...
)

Arguments

Value

A list containing:

• trials: List of normalized trial data

• events: List of detected events per trial

• phases: List of phase timing per trial

• summary: Summary statistics across trials

References

Delp SL, Anderson FC, Arnold AS, Loan P, Habib A, John CT, Guendelman E, Thelen DG (2007). "OpenSim: Open-Source Software to Create and Analyze Dynamic Simulations of Movement." IEEE Transactions on Biomedical Engineering, 54(11), 1940-1950.

See Also

create_schema_from_opensim() for creating task schemas from OpenSim models.

Examples

## Not run: 
library(signalIO)

# Load session and analyze
results <- batch_analyze_opensim(
  "subject01/",
  schema = schema_gait,
  signal_mapping = list(vGRF = "ground_force_vy_r")
)

# Access normalized trials
trial1 <- results$trials[[1]]

# View summary
print(results$summary)

## End(Not run)

Description

Normalizes multiple trials and returns them in a consistent format.

Usage

batchNormalize(
  trials,
  method = "cycle",
  norm_length = 101L,
  schema = NULL,
  ...
)

Arguments

Value

3D array (time x channels x trials) or list

References

Winter DA (2009). "Biomechanics and Motor Control of Human Movement." 4th ed. Wiley.

See Also

normalizeMovement(), normalizedTimeAxis(), combineTrials()

Description

Computes per-variable metrics (RMSE, MAE, bias, correlation, R2, ICC, Bland-Altman LoA width) between prediction and reference data.

Usage

benchmarkAgreement(
  prediction,
  reference,
  trial_id = "trial_1",
  thresholds = defaultBenchmarkThresholds("balanced"),
  alignment = c("truncate", "resample")
)

Arguments

Value

Object of class "benchmark_agreement" with metrics, summary, and thresholds.

References

Shrout PE, Fleiss JL (1979). "Intraclass Correlations: Uses in Assessing Rater Reliability." Psychological Bulletin, 86(2), 420-428.

Bland JM, Altman DG (1986). "Statistical Methods for Assessing Agreement Between Two Methods of Clinical Measurement." Lancet, 327(8476), 307-310.

See Also

defaultBenchmarkThresholds() for threshold configuration, runBenchmarkSuite() for multi-trial benchmarking, blandAltman() for Bland-Altman agreement analysis.

Examples

ref <- data.frame(a = sin(seq(0, 1, length.out = 100)))
pred <- ref + rnorm(100, sd = 0.01)
out <- benchmarkAgreement(pred, ref, trial_id = "demo")
out$summary

Description

Produces a template data.frame describing benchmark trials and file paths.

Usage

benchmarkManifestTemplate(n = 1L)

Arguments

Value

A data.frame manifest template.

References

Bland JM, Altman DG (1986). "Statistical Methods for Assessing Agreement Between Two Methods of Clinical Measurement." Lancet, 327(8476), 307-310.

See Also

writeBenchmarkManifest() for writing the template to CSV, validateBenchmarkManifest() for validating manifest structure.

Examples

benchmarkManifestTemplate(2)

Description

Performs a Bland-Altman analysis comparing two measurement methods or two time points. Computes the bias (mean difference), limits of agreement, and confidence interval for the bias.

Usage

blandAltman(x, y, confidence = 0.95)

Arguments

Details

The Bland-Altman method assesses agreement between two measurements by plotting their difference against their mean. The limits of agreement are:

$LoA = \bar{d} \pm z \times SD_d$

where $\bar{d}$ is the mean difference (bias) and $SD_d$ is the SD of differences.

Value

A list with components:

bias

Mean difference (x - y)

lower_loa

Lower limit of agreement

upper_loa

Upper limit of agreement

sd_diff

Standard deviation of differences

ci_bias

Two-element vector with lower and upper CI for the bias

References

Bland JM, Altman DG (1986). Statistical methods for assessing agreement between two methods of clinical measurement. Lancet, 327(8476), 307-310.

See Also

icc() for intraclass correlation reliability analysis, benchmarkAgreement() for benchmark validation agreement metrics.

Examples

set.seed(42)
method1 <- rnorm(30, mean = 50, sd = 10)
method2 <- method1 + rnorm(30, mean = 0, sd = 3)
result <- blandAltman(method1, method2)
result$bias
result$lower_loa
result$upper_loa

Description

Designs a Butterworth IIR filter and applies it using zero-phase filtering (forward-backward) to avoid phase distortion.

Usage

butterworthFilter(
  x,
  cutoff,
  sampling_rate,
  type = c("lowpass", "highpass", "bandpass", "bandstop"),
  order = 4
)

Arguments

Details

Requires the signal package. NAs in the input are temporarily interpolated before filtering and restored afterward.

Value

Filtered data with the same dimensions as x.

References

Butterworth S (1930). "On the Theory of Filter Amplifiers." Wireless Engineer, 7, 536-541.

See Also

filterSignals() for filtering PhysioExperiment objects, savgolFilter() for Savitzky-Golay polynomial smoothing.

Examples

## Not run: 
# Filter a single vector
x <- sin(2 * pi * 5 * seq(0, 1, length.out = 1000)) +
     sin(2 * pi * 50 * seq(0, 1, length.out = 1000))
x_filt <- butterworthFilter(x, cutoff = 20, sampling_rate = 1000, type = "lowpass")

## End(Not run)

Description

Computes the whole-body center of mass (COM) at each time frame from 3D (or 2D) marker position data and body segment inertial parameters.

Usage

calculateCOM(pe, body_mass, skeleton = NULL, bsip = NULL, marker_map = NULL)

Arguments

Details

The algorithm proceeds as follows:

1. For each body segment, compute the segment COM position as:

   $COM_{seg} = P_{prox} + f \times (P_{dist} - P_{prox})$

   where $f$ is the COM proximal fraction from the BSIP table.

2. Compute the whole-body COM as the mass-weighted average of all segment COMs:

   $COM_{body} = \frac{\sum_i m_i \times COM_i}{M}$

   where $m_i = M \times f_i$ is the segment mass and $M$ is the total body mass.

Value

A PhysioExperiment object with additional assays com_x, com_y, and (if 3D) com_z. Each assay is a single-column matrix with column name "COM" and the same number of rows as the input.

References

Winter DA (2009). "Biomechanics and Motor Control of Human Movement." 4th ed. John Wiley & Sons.

Zatsiorsky VM (2002). "Kinetics of Human Motion." Human Kinetics.

See Also

segmentParameters() for body segment inertial parameters, calculateSegmentCOM() for individual segment center of mass, symmetryIndex() for bilateral symmetry assessment.

Examples

## Not run: 
pe <- readTRC("markers.trc")
result <- calculateCOM(pe, body_mass = 75)
com_x <- SummarizedExperiment::assay(result, "com_x")

## End(Not run)

Description

Computes COP coordinates from force and moment components using standard force-plate equations (assuming a vertical Z axis).

Usage

calculateCOP(forces, moments, origin = c(0, 0, 0), min_vertical_force = 20)

Arguments

Value

A data.frame with columns cop_x, cop_y, cop_z, free_moment, and vertical_force.

References

Winter DA (2009). "Biomechanics and Motor Control of Human Movement." 4th ed. John Wiley & Sons.

See Also

analyzeForcePlate() for comprehensive force plate analysis, calculateCOM() for whole-body center of mass computation.

Examples

f <- cbind(Fx = rep(0, 10), Fy = rep(0, 10), Fz = rep(1000, 10))
m <- cbind(Mx = rep(100, 10), My = rep(-200, 10), Mz = rep(0, 10))
cop <- calculateCOP(f, m)

Description

Computes temporal and spatial gait parameters from detected gait events. Temporal parameters include stride time, step time, stance/swing durations, double support time, and cadence. Spatial parameters (step length, stride length, step width, walking speed) require position data in the PhysioExperiment assays.

Usage

calculateGaitParameters(
  pe,
  events,
  body_height = NULL,
  side = c("right", "left", "both")
)

Arguments

Details

Events must use one of the recognised naming conventions:

• "HS_R", "HS_L" / "TO_R", "TO_L"

• "right_heel_strike", "left_heel_strike" / "right_toe_off", "left_toe_off"

The events data.frame may use either event_name or event for the event name column, and either time (in sample indices) or time_sec (in seconds) for timing.

Value

An S3 object of class "gait_parameters" inheriting from data.frame. Each row is one stride cycle. Columns include:

side

Side label ("right" or "left")

stride

Stride number (sequential within side)

stride_time

Time between consecutive ipsilateral heel strikes (s)

step_time

Time from contralateral to ipsilateral heel strike (s)

stance_time

Heel strike to toe off on the same side (s)

swing_time

Toe off to next heel strike on the same side (s)

stance_percent

Stance as percentage of stride time

swing_percent

Swing as percentage of stride time

double_support_time

Time with both feet on the ground (s)

cadence

Steps per minute (60 / step_time)

step_length

AP distance between feet at consecutive heel strikes (m)

stride_length

AP distance between ipsilateral heel strikes (m)

step_width

ML distance between feet at heel strike (m)

walking_speed

Stride length / stride time (m/s)

References

Winter DA (2009). "Biomechanics and Motor Control of Human Movement." 4th ed. John Wiley & Sons.

Perry J, Burnfield JM (2010). "Gait Analysis: Normal and Pathological Function." 2nd ed. SLACK Incorporated.

See Also

calculateStepSymmetry() for symmetry analysis of gait parameters, summarizeGaitParameters() for descriptive statistics of gait data, plotGaitCycle() for gait cycle visualization.

Examples

# See test file for worked examples with synthetic data

Description

Computes joint angles from 3D (or 2D) position data stored in a PhysioExperiment object. Each joint is defined by three points: proximal, joint (vertex), and distal. The angle at the vertex is computed using the specified convention.

Usage

calculateJointAngles(pe, joints, convention = c("3point"), degrees = TRUE)

Arguments

Details

For the "3point" convention, the angle is computed at the vertex (joint point) between the proximal-to-joint and distal-to-joint direction vectors:

$\theta = \arccos\left(\frac{v_1 \cdot v_2}{|v_1| |v_2|}\right)$

where $v_1 = \text{proximal} - \text{joint}$ and $v_2 = \text{distal} - \text{joint}$.

The result is clamped to $[0, \pi]$ (or $[0, 180]$ in degrees). If any of the three points contain NA for a given frame, the angle for that frame is NA.

Value

A PhysioExperiment object with an additional "joint_angles" assay containing the computed angles (n_frames x n_joints). The column names of the assay correspond to the names of the joints list. The colData is updated with joint angle metadata.

References

Winter DA (2009). "Biomechanics and Motor Control of Human Movement." 4th ed. Wiley.

Grood ES, Suntay WJ (1983). "A joint coordinate system for the clinical description of three-dimensional motions: application to the knee." Journal of Biomechanical Engineering, 105(2), 136-144.

See Also

vectorAngle(), quaternionToEuler(), eulerToQuaternion()

Examples

## Not run: 
pe <- readTRC("markers.trc")
joints <- list(
  right_elbow = list(
    proximal = "RShoulder",
    joint    = "RElbow",
    distal   = "RWrist"
  ),
  right_knee = list(
    proximal = "RHip",
    joint    = "RKnee",
    distal   = "RAnkle"
  )
)
pe_angles <- calculateJointAngles(pe, joints)
SummarizedExperiment::assay(pe_angles, "joint_angles")

## End(Not run)

Description

Lower-level function that computes the center of mass for individual body segments from marker position data.

Usage

calculateSegmentCOM(pe, proximal_markers, distal_markers, com_fractions)

Arguments

Value

A named list of matrices, one per segment. Each matrix has dimensions (n_frames x 2) for 2D data or (n_frames x 3) for 3D data, with columns named "x", "y", and (optionally) "z".

References

Winter DA (2009). "Biomechanics and Motor Control of Human Movement." 4th ed. John Wiley & Sons.

See Also

calculateCOM() for whole-body center of mass, segmentParameters() for body segment inertial parameters.

Examples

## Not run: 
pe <- readTRC("markers.trc")
seg_coms <- calculateSegmentCOM(
  pe,
  proximal_markers = c("RHip", "LHip"),
  distal_markers   = c("RKnee", "LKnee"),
  com_fractions    = c(0.4095, 0.4095)
)

## End(Not run)

Description

Computes symmetry metrics comparing left and right sides. Includes the Robinson Symmetry Index and the left/right ratio for each parameter.

Usage

calculateStepSymmetry(gait_params)

Arguments

Details

A Symmetry Index (SI) of 0 indicates perfect symmetry. Values above 10 are generally considered clinically asymmetric.

Value

A data.frame with columns:

parameter

Name of the gait parameter

left_mean

Mean value for the left side

right_mean

Mean value for the right side

SI

Robinson Symmetry Index: |L - R| / (0.5 * (L + R)) * 100

ratio

Left / Right ratio

References

Winter DA (2009). "Biomechanics and Motor Control of Human Movement." 4th ed. John Wiley & Sons.

See Also

calculateGaitParameters() for computing gait temporal-spatial parameters, symmetryIndex() for general symmetry index computation, plotSymmetry() for symmetry visualization.

Examples

# See test file for worked examples

Description

Estimates accelerometer and gyroscope biases from data recorded during a static (motionless) period. The accelerometer bias is computed relative to the expected gravity vector, and the gyroscope bias is the mean angular velocity during the static period.

Usage

calibrateIMU(accel_static, gyro_static)

Arguments

Details

The function assumes the sensor is stationary. The accelerometer bias is estimated by subtracting the expected gravity contribution from the mean accelerometer reading. Gravity direction is inferred from the mean acceleration direction, and its expected magnitude is 9.81 m/s^2.

The gyroscope bias is simply the mean of the gyroscope readings during the static period (should be near zero for a stationary sensor).

Value

A list with components:

accel_bias

Numeric vector of length 3. Mean accelerometer bias for each axis. For a perfectly calibrated sensor at rest, this would be (0, 0, 0) after accounting for gravity.

gyro_bias

Numeric vector of length 3. Mean gyroscope bias for each axis (rad/s). A stationary sensor should read zero; any offset is the bias.

References

Madgwick SOH, Harrison AJL, Vaidyanathan R (2011). "Estimation of IMU and MARG orientation using a gradient descent algorithm." IEEE International Conference on Rehabilitation Robotics.

See Also

estimateOrientation(), removeGravity()

Examples

# Simulate static IMU data with known biases
n <- 500
accel_bias_true <- c(0.05, -0.03, 0.02)
gyro_bias_true <- c(0.001, -0.002, 0.0015)
accel <- matrix(rep(c(0, 0, -9.81), each = n), ncol = 3) +
  matrix(rep(accel_bias_true, each = n), ncol = 3)
gyro <- matrix(rep(gyro_bias_true, each = n), ncol = 3)
cal <- calibrateIMU(accel, gyro)

Description

Computes Cohen's d effect size for comparing two groups or conditions, with confidence intervals and qualitative interpretation.

Usage

cohensD(x, y, paired = FALSE, pooled = TRUE)

Arguments

Details

For independent groups with pooled = TRUE, the pooled SD is:

$SD_{pooled} = \sqrt{\frac{(n_1 - 1) s_1^2 + (n_2 - 1) s_2^2}{n_1 + n_2 - 2}}$

For paired data, the denominator is the SD of the within-pair differences.

Interpretation thresholds follow Cohen (1988):

• |d| < 0.2: negligible

• 0.2 <= |d| < 0.5: small

• 0.5 <= |d| < 0.8: medium

• |d| >= 0.8: large

Value

A list with components:

d

Cohen's d value

ci_lower

Lower bound of 95 percent confidence interval

ci_upper

Upper bound of 95 percent confidence interval

interpretation

Qualitative label: "negligible", "small", "medium", or "large"

References

Cohen J (1988). Statistical Power Analysis for the Behavioral Sciences. Lawrence Erlbaum Associates.

See Also

etaSquared() for ANOVA-based effect sizes, plotEffectSizeForest() for forest plot visualization of effect sizes.

Examples

set.seed(42)
x <- rnorm(30, mean = 10, sd = 2)
y <- rnorm(30, mean = 8, sd = 2)
result <- cohensD(x, y)
result$d
result$interpretation

Description

Combine multiple trials with segmented phases

Usage

combineTrials(..., labels = NULL)

Arguments

Value

A multi_trial_phases object

References

Winter DA (2009). "Biomechanics and Motor Control of Human Movement." 4th ed. Wiley.

See Also

segmentPhases(), batchNormalize(), normalizeMovement()

Description

Computes acceleration (second derivative) from position or velocity assays. If velocity assays already exist, differentiates those (first derivative of velocity). Otherwise, computes the second derivative of position directly.

Usage

computeAcceleration(
  pe,
  assay_names = NULL,
  method = "central",
  sampling_rate = NULL
)

Arguments

Value

PhysioExperiment with new accel_x, accel_y, accel_z assays added.

References

Winter DA (2009). "Biomechanics and Motor Control of Human Movement." 4th ed. John Wiley & Sons.

See Also

computeVelocity() for first-order derivatives, computeJerk() for third-order derivatives, differentiate() for general-purpose numerical differentiation.

Examples

## Not run: 
pe <- make_mocap_markers(n_time = 100, n_markers = 4, sr = 120)
pe <- computeAcceleration(pe)

## End(Not run)

Description

Integrates vertical force over detected stance phases using trapezoidal integration.

Usage

computeImpulse(
  force,
  sampling_rate,
  threshold = 20,
  subtract_threshold = FALSE
)

Arguments

Value

For vector input: data.frame with one row per stance and columns including impulse (force*time units). For matrix input: same structure plus channel.

References

Winter DA (2009). "Biomechanics and Motor Control of Human Movement." 4th ed. John Wiley & Sons.

See Also

analyzeForcePlate() for comprehensive force plate analysis, computeLoadingRate() for loading rate computation.

Examples

grf <- c(rep(0, 50), rep(600, 100), rep(0, 50))
computeImpulse(grf, sampling_rate = 1000)

Description

Computes jerk (third derivative of position) from position assays.

Usage

computeJerk(pe, method = "central", sampling_rate = NULL)

Arguments

Value

PhysioExperiment with new jerk_x, jerk_y, jerk_z assays added.

References

Winter DA (2009). "Biomechanics and Motor Control of Human Movement." 4th ed. John Wiley & Sons.

See Also

computeAcceleration() for second-order derivatives, computeVelocity() for first-order derivatives, differentiate() for general-purpose numerical differentiation.

Examples

## Not run: 
pe <- make_mocap_markers(n_time = 100, n_markers = 4, sr = 120)
pe <- computeJerk(pe)

## End(Not run)

Description

Compute joint power from moment and angular velocity

Usage

computeJointPower(moment, angular_velocity)

Arguments

Value

Numeric vector of joint power (W).

References

Winter DA (2009). "Biomechanics and Motor Control of Human Movement." 4th ed. John Wiley & Sons.

See Also

inverseDynamics2D() for computing joint moments in 2D, inverseDynamics3D() for computing joint moments in 3D.

Examples

computeJointPower(c(10, 12, 8), c(2, 1.5, 1))

Description

Detects stance phases from a vertical GRF signal and computes loading rate from initial contact to peak force for each stance.

Usage

computeLoadingRate(
  vgrf,
  sampling_rate,
  threshold = 20,
  method = c("instantaneous", "average")
)

Arguments

Value

For vector input: a data.frame with one row per stance. For matrix input: same data.frame with an added channel column.

References

Winter DA (2009). "Biomechanics and Motor Control of Human Movement." 4th ed. John Wiley & Sons.

See Also

analyzeForcePlate() for comprehensive force plate analysis, computeImpulse() for impulse computation.

Examples

grf <- c(rep(0, 100), seq(0, 800, length.out = 150),
         seq(800, 0, length.out = 150), rep(0, 100))
computeLoadingRate(grf, sampling_rate = 1000)

Description

Compute moving RMS envelope of EMG

Usage

computeRMSEnvelope(x, window_samples = 50L, center = TRUE)

Arguments

Value

RMS envelope with the same dimensions as x.

References

Merletti R, Parker PA (2004). "Electromyography: Physiology, Engineering, and Non-Invasive Applications." IEEE Press/Wiley.

See Also

rectifyEMG() for signal rectification, normalizeEMG() for MVC or peak normalization, processEMG() for complete EMG processing pipeline.

Examples

set.seed(1)
sig <- rnorm(1000)
env <- computeRMSEnvelope(sig, window_samples = 50)

Description

Computes the magnitude of the velocity vector (Euclidean norm) at each time point for each marker/keypoint.

Usage

computeSpeed(pe, velocity_assays = NULL)

Arguments

Value

PhysioExperiment with a new "speed" assay containing the scalar speed: sqrt(vx^2 + vy^2 + vz^2).

References

Winter DA (2009). "Biomechanics and Motor Control of Human Movement." 4th ed. John Wiley & Sons.

See Also

computeVelocity() for computing velocity components, computeAcceleration() for computing acceleration.

Examples

## Not run: 
pe <- make_mocap_markers(n_time = 100, n_markers = 4, sr = 120)
pe <- computeVelocity(pe)
pe <- computeSpeed(pe)

## End(Not run)

Description

Computes velocity (first derivative) from position assays in a PhysioExperiment object using finite differences.

Usage

computeVelocity(
  pe,
  assay_names = NULL,
  method = "central",
  sampling_rate = NULL
)

Arguments

Value

PhysioExperiment with new velocity assays added. For 3D marker data, assays named velocity_x, velocity_y, velocity_z. For 2D keypoint data, assays named velocity_kp_x, velocity_kp_y.

References

Winter DA (2009). "Biomechanics and Motor Control of Human Movement." 4th ed. John Wiley & Sons.

See Also

computeAcceleration() for second-order derivatives, computeSpeed() for scalar speed from velocity components, differentiate() for general-purpose numerical differentiation.

Examples

## Not run: 
pe <- make_mocap_markers(n_time = 100, n_markers = 4, sr = 120)
pe <- computeVelocity(pe)
# velocity_x, velocity_y, velocity_z assays are now present

## End(Not run)

Description

Repairs marker trajectories by swapping the data columns of detected swap pairs from the swap frame onward.

Usage

correctSwaps(pe, swaps, assay_prefix = "position", min_confidence = 0.5)

Arguments

Value

A PhysioExperiment with corrected position assays.

See Also

detectSwaps(), trackMarkers()

Examples

## Not run: 
swaps <- detectSwaps(pe)
pe_fixed <- correctSwaps(pe, swaps, min_confidence = 0.3)

## End(Not run)

Description

Generates a TaskSchema based on an OpenSim model's markers and available signal channels. This function creates appropriate event definitions that can be used with detectEvents() for automatic event detection.

Usage

create_schema_from_opensim(
  model,
  task_type = c("gait", "running", "jump", "generic"),
  available_signals = NULL,
  side = "right"
)

Arguments

Details

This function analyzes the model's markers and the available signals to generate an appropriate TaskSchema. For gait analysis, it prefers GRF-based detection if available, falling back to marker-based or kinematic detection.

Value

A TaskSchema object configured for the specified task type

References

Delp SL, Anderson FC, Arnold AS, Loan P, Habib A, John CT, Guendelman E, Thelen DG (2007). "OpenSim: Open-Source Software to Create and Analyze Dynamic Simulations of Movement." IEEE Transactions on Biomedical Engineering, 54(11), 1940-1950.

See Also

batch_analyze_opensim() for batch processing of OpenSim sessions, detectEvents() for event detection using task schemas.

Examples

## Not run: 
library(signalIO)

# Load OpenSim model
model <- read_osim("gait2354.osim")

# Load IK results
ik <- read_mot("ik_results.mot")
ik_channels <- rownames(ik)

# Create gait schema with available channels
schema <- create_schema_from_opensim(model, "gait", ik_channels)
print(schema)

# Use schema for event detection
events <- detectEvents(data, schema)

## End(Not run)

Description

Generates prediction/reference CSV pairs and a manifest for dry-run validation benchmarking when external data are not yet available.

Usage

createBenchmarkExample(
  output_dir = tempdir(),
  n_trials = 3L,
  n_samples = 300L,
  variables = c("hip_angle", "knee_angle", "ankle_angle"),
  noise_sd = 0.02,
  seed = 1
)

Arguments

Value

A list with manifest, manifest_path, and data_dir.

References

Bland JM, Altman DG (1986). "Statistical Methods for Assessing Agreement Between Two Methods of Clinical Measurement." Lancet, 327(8476), 307-310.

See Also

runBenchmarkSuite() for running the benchmark suite, benchmarkManifestTemplate() for creating empty manifest templates.

Examples

ex <- createBenchmarkExample(n_trials = 2, seed = 1)
runBenchmarkSuite(ex$manifest, data_dir = ex$data_dir)

Description

Returns a named list of threshold values used by benchmark pass/fail logic.

Usage

defaultBenchmarkThresholds(profile = c("balanced", "strict", "lenient"))

Arguments

Value

Named list with threshold fields: rmse_max, mae_max, bias_abs_max, cor_min, icc_min.

References

Shrout PE, Fleiss JL (1979). "Intraclass Correlations: Uses in Assessing Rater Reliability." Psychological Bulletin, 86(2), 420-428.

See Also

benchmarkAgreement() for computing agreement metrics, runBenchmarkSuite() for running a full benchmark suite.

Examples

defaultBenchmarkThresholds()

Description

Factory function that returns a SkeletonModel for a well-known pose estimation or motion capture marker set.

Usage

define_skeleton(model_name)

Arguments

Value

A SkeletonModel object.

References

Winter DA (2009). "Biomechanics and Motor Control of Human Movement." 4th ed. Wiley.

See Also

SkeletonModel(), get_bone_connections(), get_limb_pairs()

Examples

sk <- define_skeleton("BODY_25")
print(sk)

sk_coco <- define_skeleton("COCO")
get_bone_connections(sk_coco)

Description

Generates synthetic motion capture, force, and EMG data so first-time users can run package workflows without external files.

Usage

demoMoCapData(
  n_frames = 300,
  sampling_rate = 120,
  n_markers = 8,
  emg_sampling_rate = 1000,
  seed = 123
)

Arguments

Value

A named list with:

mocap

A PhysioExperiment with position_x, position_y, position_z assays.

grf

Numeric vector of synthetic vertical GRF.

forces

Matrix with force_x, force_y, force_z columns.

joints

Data frame with 2D joint-center coordinates for ankle, knee, and hip.

joint_angles

Data frame with ankle/knee/hip joint angles (radians).

emg

Matrix of synthetic EMG channels.

sampling_rate

MoCap sampling rate (Hz).

emg_sampling_rate

EMG sampling rate (Hz).

References

Winter DA (2009). "Biomechanics and Motor Control of Human Movement." 4th ed. John Wiley & Sons.

See Also

quickStartMoCap() for a complete getting-started workflow, assessMoCapReadiness() for data quality assessment.

Examples

demo <- demoMoCapData(seed = 1)
demo$mocap
head(demo$joints)

Description

Automatically detects events defined in a TaskSchema based on signal data.

Usage

detectEvents(
  x,
  schema,
  signals = NULL,
  method = c("auto", "manual", "hybrid"),
  sampling_rate = NULL,
  ...
)

Arguments

Value

A data.frame with columns:

• event - Event name

• label - Human-readable label

• index - Sample index of event

• time - Time in seconds

• percent - Percent of movement (if applicable)

• method - Detection method used

• confidence - Detection confidence (0-1)

References

Winter DA (2009). "Biomechanics and Motor Control of Human Movement." 4th ed. Wiley.

See Also

manualEvents(), segmentPhases(), TaskSchema()

Examples

# Create synthetic gait data
set.seed(123)
n <- 1000
t <- seq(0, 1, length.out = n)
vGRF <- c(rep(0, 100), sin(seq(0, pi, length.out = 500)) * 800, rep(0, 400))
vGRF <- vGRF + rnorm(n, 0, 10)

signals <- list(vGRF = vGRF)
events <- detectEvents(as.matrix(vGRF), schema_gait,
                       signals = signals, sampling_rate = 1000)

Description

Identifies threshold-based contact windows from vertical GRF signals.

Usage

detectForcePlateContacts(vgrf, threshold = 20, sampling_rate = NULL)

Arguments

Value

A data.frame with one row per detected contact and columns: plate, stance, onset, offset, duration_samples, duration_s, and peak_force.

References

Winter DA (2009). "Biomechanics and Motor Control of Human Movement." 4th ed. John Wiley & Sons.

See Also

analyzeForcePlate() for comprehensive force plate analysis, analyzeForcePlatePE() for PhysioExperiment-based analysis.

Examples

v <- cbind(
  fp1 = c(rep(0, 100), rep(500, 200), rep(0, 100)),
  fp2 = c(rep(0, 200), rep(700, 100), rep(0, 100))
)
detectForcePlateContacts(v, threshold = 20, sampling_rate = 1000)

Description

Scans the specified assay of a PhysioExperiment object for contiguous runs of NA values and returns a data.frame describing each gap.

Usage

detectGaps(pe, assay_name = "position_x")

Arguments

Value

A data.frame with columns:

• channel - Channel (column) name or index

• start - Start index of the gap (1-based)

• end - End index of the gap (1-based)

• size - Number of consecutive NA values

References

Federolf PA (2013). "A novel approach to solve the 'missing marker problem' in marker-based motion analysis that does not require additional assumptions about the biodynamic model." Journal of Biomechanics, 46(13), 2173-2178.

See Also

reportGaps(), fillGaps(), fillGapsLinear()

Examples

pe <- PhysioCore::PhysioExperiment(
  assays = S4Vectors::SimpleList(position_x = matrix(c(1, NA, NA, 4), ncol = 1)),
  colData = S4Vectors::DataFrame(label = "M1", type = "marker"),
  samplingRate = 120
)
gaps <- detectGaps(pe, assay_name = "position_x")

Description

Identifies frames where two or more marker trajectories abruptly swap positions, indicating a labelling error. Swaps are detected by velocity spikes that exceed median + 5 * MAD and show a cross-over pattern between marker pairs.

Usage

detectSwaps(pe, threshold = NULL, window = 5, assay_prefix = "position")

Arguments

Value

A data.frame with columns:

frame

Frame index where the swap was detected.

marker_a

Name or index of the first marker in the swap pair.

marker_b

Name or index of the second marker.

velocity_a

Velocity of marker_a at the swap frame.

velocity_b

Velocity of marker_b at the swap frame.

confidence

Confidence score (0-1) based on how closely the cross-over distances match.

If no swaps are detected, an empty data.frame with the same columns is returned.

See Also

trackMarkers(), correctSwaps()

Examples

## Not run: 
pe <- readVenus3D("capture.csv")
pe_tracked <- trackMarkers(pe)
swaps <- detectSwaps(pe_tracked)
if (nrow(swaps) > 0) {
  pe_fixed <- correctSwaps(pe_tracked, swaps)
}

## End(Not run)

Description

Low-level function to compute numerical derivatives of arbitrary order using finite difference methods.

Usage

differentiate(x, dt, method = c("central", "forward", "backward"), order = 1L)

Arguments

Details

For method = "central" with order = 1:

$f'(i) = (x[i+1] - x[i-1]) / (2 \cdot dt)$

For method = "central" with order = 2:

$f''(i) = (x[i+1] - 2 x[i] + x[i-1]) / dt^2$

For higher orders, the derivative is computed by repeated application of the first-order formula.

Value

Differentiated data with same dimensions as input. Boundary values where the stencil cannot be applied are set to NA.

References

Winter DA (2009). "Biomechanics and Motor Control of Human Movement." 4th ed. John Wiley & Sons.

See Also

computeVelocity() for computing velocity from PhysioExperiment, computeAcceleration() for second-order derivatives, savgolFilter() for smoothed differentiation via Savitzky-Golay.

Examples

# Differentiate sin to get cos
t <- seq(0, 2 * pi, length.out = 200)
x <- sin(t)
dx <- differentiate(x, dt = t[2] - t[1], method = "central", order = 1)
# dx should approximate cos(t)

Description

Computes the average waveform using DTW Barycenter Averaging, which accounts for time warping in the averaging process.

Usage

dtwAverage(x, init = "medoid", max_iter = 30, tol = 1e-04, window_size = NULL)

Arguments

Value

A list containing:

References

Sakoe H, Chiba S (1978). "Dynamic programming algorithm optimization for spoken word recognition." IEEE Transactions on Acoustics, Speech, and Signal Processing, 26(1), 43-49.

Petitjean F, Ketterlin A, Gancarski P (2011). "A global averaging method for dynamic time warping, with applications to clustering." Pattern Recognition, 44(3), 678-693.

See Also

dtwDistance(), dtwDistanceMatrix(), dtwClustering()

Examples

# Average multiple gait cycles with timing variation
set.seed(123)
t <- seq(0, 100, length.out = 100)
data <- sapply(1:20, function(i) {
  phase <- rnorm(1, 0, 10)
  sin(2 * pi * (t + phase) / 100) * 30 + rnorm(100, 0, 2)
})

avg <- dtwAverage(data)
plot(avg$average, type = "l")

Description

Clusters waveforms using DTW distance with hierarchical or k-medoids method.

Usage

dtwClustering(
  x,
  k = 2,
  method = c("hierarchical", "kmedoids"),
  linkage = "ward.D2",
  window_size = NULL
)

Arguments

Value

A list containing:

References

Sakoe H, Chiba S (1978). "Dynamic programming algorithm optimization for spoken word recognition." IEEE Transactions on Acoustics, Speech, and Signal Processing, 26(1), 43-49.

See Also

dtwDistance(), dtwDistanceMatrix(), dtwAverage()

Examples

# Cluster gait patterns
set.seed(123)
t <- seq(0, 100, length.out = 100)

# Generate two groups with different patterns
group1 <- sapply(1:15, function(i) {
  sin(2 * pi * t / 100) * 30 + rnorm(100, 0, 3)
})
group2 <- sapply(1:15, function(i) {
  sin(2 * pi * t / 100 + pi/4) * 20 + rnorm(100, 0, 3)
})
data <- cbind(group1, group2)

result <- dtwClustering(data, k = 2)
table(result$clusters)

Description

Functions for Dynamic Time Warping analysis including distance computation, alignment, averaging, and clustering of biomechanical waveforms. Compute DTW distance between two time series

Usage

dtwDistance(
  x,
  y,
  window_size = NULL,
  step_pattern = c("symmetric2", "symmetric1", "asymmetric"),
  normalize = TRUE
)

Arguments

Details

Calculates the Dynamic Time Warping distance and alignment path between two waveforms, allowing for non-linear time alignment.

DTW finds the optimal alignment between two time series by warping the time axis. It's useful for comparing movements that may occur at different speeds or with timing differences.

Value

A list of class "dtw_result" containing:

References

Sakoe H, Chiba S (1978). "Dynamic programming algorithm optimization for spoken word recognition." IEEE Transactions on Acoustics, Speech, and Signal Processing, 26(1), 43-49.

See Also

dtwDistanceMatrix(), dtwAverage(), dtwClustering(), dtwWarp()

Examples

# Two gait cycles with different timing
t <- seq(0, 100, length.out = 100)
x <- sin(2 * pi * t / 100) * 30
y <- sin(2 * pi * (t + 10) / 100) * 30  # Phase shifted

result <- dtwDistance(x, y)
print(result$distance)

Description

Computes pairwise DTW distances between all columns of a matrix or between observations in a PhysioExperiment.

Usage

dtwDistanceMatrix(x, window_size = NULL, normalize = TRUE, parallel = FALSE)

Arguments

Value

A symmetric distance matrix.

References

Sakoe H, Chiba S (1978). "Dynamic programming algorithm optimization for spoken word recognition." IEEE Transactions on Acoustics, Speech, and Signal Processing, 26(1), 43-49.

See Also

dtwDistance(), dtwAverage(), dtwClustering()

Examples

# Create matrix of 10 gait cycles
set.seed(123)
t <- seq(0, 100, length.out = 100)
data <- sapply(1:10, function(i) {
  phase <- rnorm(1, 0, 10)
  sin(2 * pi * (t + phase) / 100) * 30 + rnorm(100, 0, 2)
})

dist_matrix <- dtwDistanceMatrix(data)

Description

Applies a DTW alignment path to warp one waveform to match another.

Usage

dtwWarp(x, dtw_result, to = c("query", "reference"))

Arguments

Value

Warped waveform.

References

Sakoe H, Chiba S (1978). "Dynamic programming algorithm optimization for spoken word recognition." IEEE Transactions on Acoustics, Speech, and Signal Processing, 26(1), 43-49.

See Also

dtwDistance(), dtwAverage(), normalizeMovement()

Description

Fuses accelerometer, gyroscope, and optionally magnetometer data to estimate 3D orientation over time. Returns both quaternion and Euler angle representations.

Usage

estimateOrientation(
  accel,
  gyro,
  mag = NULL,
  method = c("madgwick", "complementary"),
  beta = 0.1,
  sampling_rate
)

Arguments

Details

The Madgwick filter uses a gradient-descent algorithm to correct gyroscope drift using accelerometer (and optionally magnetometer) measurements. The beta parameter controls the correction strength.

The complementary filter blends gyroscope integration (high-pass) with accelerometer tilt estimation (low-pass). The beta parameter serves as the alpha coefficient, controlling the balance between gyroscope and accelerometer contributions.

Value

A data.frame with columns:

time

Time in seconds from start.

roll

Roll angle (degrees), rotation about X axis.

pitch

Pitch angle (degrees), rotation about Y axis.

yaw

Yaw angle (degrees), rotation about Z axis.

q_w

Quaternion scalar component.

q_x

Quaternion x component.

q_y

Quaternion y component.

q_z

Quaternion z component.

References

Madgwick SOH, Harrison AJL, Vaidyanathan R (2011). "Estimation of IMU and MARG orientation using a gradient descent algorithm." IEEE International Conference on Rehabilitation Robotics.

See Also

removeGravity(), calibrateIMU(), quaternionToEuler()

Examples

# Simulate static IMU data (sensor resting with gravity along -Z)
n <- 100
accel <- matrix(c(rep(0, n), rep(0, n), rep(-9.81, n)), ncol = 3)
gyro <- matrix(0, nrow = n, ncol = 3)
result <- estimateOrientation(accel, gyro, sampling_rate = 100)
head(result)

Description

Builds foot/shank/thigh inertial parameters from body mass and segment lengths using De Leva-style coefficients.

Usage

estimateSegmentInertia(
  body_mass,
  segment_lengths = NULL,
  body_height = NULL,
  model = c("deLeva_male", "deLeva_female")
)

Arguments

Value

A data.frame with segment mass, COM fraction, radius of gyration, and segment moment of inertia about COM.

References

Winter DA (2009). "Biomechanics and Motor Control of Human Movement." 4th ed. John Wiley & Sons.

See Also

segmentParameters() for body segment inertial parameters, inverseDynamics2D() for 2D inverse dynamics computation, inverseDynamics3D() for 3D inverse dynamics computation.

Examples

inertia <- estimateSegmentInertia(
  body_mass = 70,
  segment_lengths = c(foot = 0.25, shank = 0.43, thigh = 0.45)
)

Description

Computes eta-squared, partial eta-squared, and omega-squared from a one-way between-subjects comparison.

Usage

etaSquared(x, groups)

Arguments

Details

Eta-squared is the proportion of total variance explained by group membership:

$\eta^2 = \frac{SS_{between}}{SS_{total}}$

Omega-squared provides a less biased estimate:

$\omega^2 = \frac{SS_{between} - df_{between} \cdot MS_{within}}{SS_{total} + MS_{within}}$

For one-way designs, partial eta-squared equals eta-squared.

Value

A list with components:

eta_sq

Eta-squared (SS_between / SS_total)

partial_eta_sq

Partial eta-squared (SS_between / (SS_between + SS_within))

omega_sq

Omega-squared (bias-corrected effect size)

References

Cohen J (1988). "Statistical Power Analysis for the Behavioral Sciences." Lawrence Erlbaum Associates.

See Also

cohensD() for pairwise effect sizes, plotEffectSizeForest() for forest plot visualization.

Examples

set.seed(42)
x <- c(rnorm(20, 10, 2), rnorm(20, 12, 2), rnorm(20, 14, 2))
groups <- rep(c("A", "B", "C"), each = 20)
result <- etaSquared(x, groups)
result$eta_sq

Description

Converts Euler angles (roll, pitch, yaw) to quaternion (w, x, y, z) representation using the specified rotation order.

Usage

eulerToQuaternion(roll, pitch, yaw, order = "ZYX")

Arguments

Details

Input angles are assumed to be in radians. For the "ZYX" convention, the combined quaternion is computed as: $q = q_z \otimes q_y \otimes q_x$

The resulting quaternion is always returned with $w \geq 0$ (canonical form).

Value

A matrix with columns w, x, y, z. If inputs are scalars, returns a 1-row matrix. If inputs are vectors, returns a matrix with one row per element.

References

Winter DA (2009). "Biomechanics and Motor Control of Human Movement." 4th ed. Wiley.

Grood ES, Suntay WJ (1983). "A joint coordinate system for the clinical description of three-dimensional motions: application to the knee." Journal of Biomechanical Engineering, 105(2), 136-144.

See Also

quaternionToEuler(), calculateJointAngles(), vectorAngle()

Examples

# Zero rotation -> identity quaternion
eulerToQuaternion(0, 0, 0)

# 90-degree rotation about Z axis (input in radians)
eulerToQuaternion(0, 0, pi/2)

Description

Defines an event within a movement task that can be detected automatically or specified manually.

Usage

Event(
  name,
  label,
  detection_method = "threshold",
  detection_params = list(),
  typical_timing = NULL
)

Arguments

Value

An Event object (S3 class)

References

Winter DA (2009). "Biomechanics and Motor Control of Human Movement." 4th ed. Wiley.

See Also

Phase(), TaskSchema(), detectEvents()

Examples

# Heel strike event detected when vertical GRF rises above 10N
hs <- Event("hs", "Heel Strike", "threshold",
            list(signal = "vGRF", threshold = 10, direction = "rising"),
            typical_timing = 0)

# Toe off event detected when vertical GRF falls below 10N
to <- Event("to", "Toe Off", "threshold",
            list(signal = "vGRF", threshold = 10, direction = "falling"),
            typical_timing = 60)

# Peak knee flexion
pk <- Event("peak_flex", "Peak Knee Flexion", "peak",
            list(signal = "knee_angle", type = "max"))

Description

Extract a single phase from segmented data

Usage

extractPhase(x, phase_name, search_subphases = TRUE)

Arguments

Value

A matrix of phase data

References

Winter DA (2009). "Biomechanics and Motor Control of Human Movement." 4th ed. Wiley.

See Also

segmentPhases(), phaseTiming(), getPhaseData()

Description

Functions for dimensionality reduction and visualization of high-dimensional biomechanical waveform data including PCA, UMAP, and t-SNE. Extract waveform features for dimensionality reduction

Usage

extractWaveformFeatures(x, features = c("statistical", "shape"), n_points = 50)

Arguments

Details

Extracts summary features from waveforms for use with standard dimensionality reduction methods.

Feature types:

• raw: The raw waveform resampled to n_points

• statistical: Mean, SD, min, max, range, skewness, kurtosis

• frequency: Dominant frequency, spectral centroid, bandwidth

• shape: Peaks, zero crossings, area under curve

Value

A matrix (observations x features) suitable for PCA/UMAP.

References

van der Maaten L, Hinton G (2008). "Visualizing Data using t-SNE." Journal of Machine Learning Research, 9, 2579-2605.

See Also

waveformPCA(), waveformUMAP(), waveformTSNE()

Examples

# Extract features from gait data
set.seed(123)
data <- matrix(rnorm(1000), nrow = 100, ncol = 10)
features <- extractWaveformFeatures(data, features = c("statistical", "shape"))

Description

Fills NA gaps in one or more position assays of a PhysioExperiment object using interpolation. Gaps larger than max_gap are left as NA.

Usage

fillGaps(pe, method = c("spline", "linear"), max_gap = 50, assay_names = NULL)

Arguments

Value

PhysioExperiment with filled assays (modified in place)

References

Federolf PA (2013). "A novel approach to solve the 'missing marker problem' in marker-based motion analysis that does not require additional assumptions about the biodynamic model." Journal of Biomechanics, 46(13), 2173-2178.

See Also

detectGaps(), reportGaps(), fillGapsLinear(), fillGapsSpline()

Examples

pe <- PhysioCore::PhysioExperiment(
  assays = S4Vectors::SimpleList(
    position_x = matrix(c(1, NA, NA, 4, 5), ncol = 1),
    position_y = matrix(c(10, NA, NA, 40, 50), ncol = 1)
  ),
  colData = S4Vectors::DataFrame(label = "M1", type = "marker"),
  samplingRate = 120
)
pe_filled <- fillGaps(pe, method = "linear", max_gap = 50)

Description

Fills NA values in a numeric vector using linear interpolation via approx.

Usage

fillGapsLinear(x)

Arguments

Value

Numeric vector with NAs filled by linear interpolation. Leading and trailing NAs (outside the range of valid data) remain as NA.

References

Federolf PA (2013). "A novel approach to solve the 'missing marker problem' in marker-based motion analysis that does not require additional assumptions about the biodynamic model." Journal of Biomechanics, 46(13), 2173-2178.

See Also

fillGapsSpline(), fillGaps(), detectGaps()

Examples

x <- c(1, NA, NA, 4, 5, NA, 7)
fillGapsLinear(x)

Description

Fills NA values in a numeric vector using smooth spline interpolation via smooth.spline.

Usage

fillGapsSpline(x, spar = NULL)

Arguments

Value

Numeric vector with NAs filled by spline interpolation. Leading and trailing NAs (outside the range of valid data) remain as NA.

References

Federolf PA (2013). "A novel approach to solve the 'missing marker problem' in marker-based motion analysis that does not require additional assumptions about the biodynamic model." Journal of Biomechanics, 46(13), 2173-2178.

See Also

fillGapsLinear(), fillGaps(), detectGaps()

Examples

x <- c(1, NA, NA, 4, 5, NA, 7)
fillGapsSpline(x)

Description

Applies low-pass filtering to force-plate signals. By default this uses a zero-phase Butterworth filter (requires the signal package). If signal is not available, the function falls back to a moving-average filter.

Usage

filterGRF(
  x,
  sampling_rate,
  cutoff = 20,
  order = 4,
  method = c("butterworth", "moving_average")
)

Arguments

Value

Filtered data with the same dimensions as x.

References

Winter DA (2009). "Biomechanics and Motor Control of Human Movement." 4th ed. John Wiley & Sons.

See Also

butterworthFilter() for general Butterworth filtering, analyzeForcePlate() for comprehensive force plate analysis.

Examples

sr <- 1000
t <- seq(0, 1, length.out = sr)
x <- sin(2 * pi * 10 * t) + 0.2 * sin(2 * pi * 150 * t)
y <- filterGRF(x, sampling_rate = sr, cutoff = 25)

Description

Applies a Butterworth IIR filter to signal data stored in a PhysioExperiment object. The filter is applied using zero-phase filtering (forward-backward) to avoid phase distortion.

Usage

filterSignals(
  pe,
  type = c("lowpass", "highpass", "bandpass", "bandstop"),
  cutoff,
  order = 4,
  assay_name = NULL,
  output_assay = NULL
)

Arguments

Details

This function requires the signal package (listed in Suggests). If not installed, an informative error message with install instructions is provided.

The Butterworth filter is designed using signal::butter() and applied with signal::filtfilt() for zero-phase filtering. The cutoff frequency is normalized to the Nyquist frequency automatically.

Value

A PhysioExperiment object with filtered data stored as a new assay.

References

Winter DA (2009). "Biomechanics and Motor Control of Human Movement." 4th ed. John Wiley & Sons.

Butterworth S (1930). "On the Theory of Filter Amplifiers." Wireless Engineer, 7, 536-541.

See Also

butterworthFilter() for filtering raw vectors and matrices, savgolFilter() for Savitzky-Golay polynomial smoothing, movingAverage() for simple moving average smoothing.

Examples

## Not run: 
pe <- make_mocap_markers(n_time = 500, n_markers = 4, sr = 120)
pe_filt <- filterSignals(pe, type = "lowpass", cutoff = 10)

## End(Not run)

Description

Functions for functional data analysis including functional PCA (fPCA), functional regression, and curve registration. These methods treat biomechanical waveforms as continuous functions. Functional Principal Component Analysis (fPCA)

Usage

fPCA(x, n_components = 5, smooth = FALSE, smooth_param = 10)

Arguments

Details

Performs functional PCA on waveform data to identify the main modes of variation in movement patterns.

fPCA decomposes waveform variability into orthogonal modes. In gait analysis, PC1 often represents overall amplitude, PC2 timing/phase shifts, and subsequent PCs capture more subtle shape variations.

Value

A list of class "fpca_result" containing:

References

Ramsay JO, Silverman BW (2005). "Functional Data Analysis." 2nd ed. Springer.

See Also

reconstructFPCA() for waveform reconstruction from fPCA results, plotFPCA() for visualization of fPCA results, registerCurves() for separating phase and amplitude variation.

Examples

# Simulate gait angle data (100 time points x 30 subjects)
set.seed(123)
t <- seq(0, 100, length.out = 100)
base_curve <- sin(2 * pi * t / 100) * 30

# Add subject variability
data <- sapply(1:30, function(i) {
  amplitude <- rnorm(1, 1, 0.2)
  phase <- rnorm(1, 0, 5)
  base_curve * amplitude + rnorm(100, 0, 2)
})

pe <- PhysioExperiment(assays = list(values = data), samplingRate = 100)
fpca_result <- fPCA(pe, n_components = 4)

# Plot first two PC loadings
plotFPCA(fpca_result)

Description

Returns the bone connections as an edge list (data.frame) or as an adjacency matrix.

Usage

get_bone_connections(skeleton, as_matrix = FALSE)

Arguments

Value

If as_matrix = FALSE, a data.frame with columns from_label, to_label, and bone_name. If as_matrix = TRUE, a symmetric logical adjacency matrix.

References

Winter DA (2009). "Biomechanics and Motor Control of Human Movement." 4th ed. Wiley.

See Also

SkeletonModel(), define_skeleton(), get_segment_lengths()

Examples

sk <- define_skeleton("COCO")
edges <- get_bone_connections(sk)
head(edges)

adj <- get_bone_connections(sk, as_matrix = TRUE)
dim(adj)

Description

Identifies corresponding left and right keypoint pairs in a skeleton model, useful for bilateral symmetry analysis.

Usage

get_limb_pairs(skeleton)

Arguments

Value

A data.frame with columns left and right, where each row is a matched pair of keypoint labels.

References

Winter DA (2009). "Biomechanics and Motor Control of Human Movement." 4th ed. Wiley.

See Also

SkeletonModel(), define_skeleton(), get_segment_lengths()

Examples

sk <- define_skeleton("BODY_25")
pairs <- get_limb_pairs(sk)
head(pairs)

Description

Calculates the Euclidean distance between connected keypoints for each frame. The PhysioExperiment must contain position_x, position_y, and (optionally) position_z assays with columns matching skeleton keypoint labels.

Usage

get_segment_lengths(pe, skeleton)

Arguments

Value

A matrix of segment lengths with dimensions (n_frames x n_bones). Column names are bone names.

References

Winter DA (2009). "Biomechanics and Motor Control of Human Movement." 4th ed. Wiley.

See Also

SkeletonModel(), get_bone_connections(), get_limb_pairs()

Examples

## Not run: 
pe <- readOpenPose("path/to/frames/", model = "BODY_25")
sk <- define_skeleton("BODY_25")
lengths <- get_segment_lengths(pe, sk)

## End(Not run)

Description

Get an event by name from a TaskSchema

Usage

getEvent(schema, event_name)

Arguments

Value

An Event object or NULL if not found

References

Winter DA (2009). "Biomechanics and Motor Control of Human Movement." 4th ed. Wiley.

See Also

getEventNames(), getPhase(), TaskSchema()

Description

Get event names from a TaskSchema

Usage

getEventNames(schema)

Arguments

Value

Character vector of event names

References

Winter DA (2009). "Biomechanics and Motor Control of Human Movement." 4th ed. Wiley.

See Also

getPhaseNames(), getEvent(), TaskSchema()

Description

Get a phase by name from a TaskSchema

Usage

getPhase(schema, phase_name, search_subphases = TRUE)

Arguments

Value

A Phase object or NULL if not found

References

Winter DA (2009). "Biomechanics and Motor Control of Human Movement." 4th ed. Wiley.

See Also

getPhaseNames(), getPhaseColors(), getEvent()

Description

Get phase colors from a TaskSchema

Usage

getPhaseColors(schema, include_subphases = FALSE)

Arguments

Value

Named character vector of colors (phase name -> color)

References

Winter DA (2009). "Biomechanics and Motor Control of Human Movement." 4th ed. Wiley.

See Also

getPhaseNames(), getPhase(), TaskSchema()

Description

Get data for all phases as a list of matrices

Usage

getPhaseData(x, include_subphases = FALSE)

Arguments

Value

Named list of matrices

References

Winter DA (2009). "Biomechanics and Motor Control of Human Movement." 4th ed. Wiley.

See Also

segmentPhases(), extractPhase(), hasValidPhases()

Description

Get phase names from a TaskSchema

Usage

getPhaseNames(schema, include_subphases = FALSE)

Arguments

Value

Character vector of phase names

References

Winter DA (2009). "Biomechanics and Motor Control of Human Movement." 4th ed. Wiley.

See Also

getEventNames(), getPhase(), getPhaseColors()

Description

Get a pre-built schema by name

Usage

getSchema(name)

Arguments

Value

A TaskSchema object

References

Winter DA (2009). "Biomechanics and Motor Control of Human Movement." 4th ed. Wiley.

See Also

listSchemas(), TaskSchema(), validateSchema()

Examples

gait <- getSchema("gait")
jump <- getSchema("jump")

Description

Check if all phases are valid

Usage

hasValidPhases(x)

Arguments

Value

Logical indicating if all phases have valid data

References

Winter DA (2009). "Biomechanics and Motor Control of Human Movement." 4th ed. Wiley.

See Also

segmentPhases(), getPhaseData(), extractPhase()

Description

Computes the ICC using ANOVA-based methods following Shrout & Fleiss (1979). Supports one-way and two-way random/mixed models, agreement and consistency types, and single or average unit measures.

Usage

icc(
  ratings,
  model = c("twoway", "oneway"),
  type = c("agreement", "consistency"),
  unit = c("single", "average")
)

Arguments

Details

The function implements the six ICC forms from Shrout & Fleiss (1979):

• ICC(1,1): One-way random, single measures

• ICC(1,k): One-way random, average measures

• ICC(2,1): Two-way random, absolute agreement, single measures

• ICC(2,k): Two-way random, absolute agreement, average measures

• ICC(3,1): Two-way mixed, consistency, single measures

• ICC(3,k): Two-way mixed, consistency, average measures

Value

A list with components:

icc

ICC value

ci_lower

Lower bound of 95 percent confidence interval

ci_upper

Upper bound of 95 percent confidence interval

f_value

F statistic from ANOVA

p_value

p-value for testing ICC = 0

model

Model used

type

Agreement type used

References

Shrout PE, Fleiss JL (1979). Intraclass correlations: Uses in assessing rater reliability. Psychological Bulletin, 86(2), 420-428.

See Also

sem() for standard error of measurement based on ICC, mdc() for minimal detectable change, blandAltman() for agreement analysis between methods.

Examples

# Test-retest reliability
ratings <- matrix(c(
  9, 2, 5, 8,
  6, 1, 3, 2,
  8, 4, 6, 8,
  7, 1, 2, 6,
  10, 5, 6, 9,
  6, 2, 4, 7
), nrow = 6, ncol = 4, byrow = TRUE)

result <- icc(ratings, model = "twoway", type = "agreement")
result$icc

Description

Aligns EMG to MoCap sample times and returns a combined table for downstream feature analysis.

Usage

integrateEMGMoCap(
  mocap,
  emg,
  mocap_sampling_rate = NULL,
  emg_sampling_rate,
  mocap_assay = NULL,
  process = TRUE,
  ...
)

Arguments

Value

A list with mocap, emg_aligned, and combined data.frame.

References

Merletti R, Parker PA (2004). "Electromyography: Physiology, Engineering, and Non-Invasive Applications." IEEE Press/Wiley.

See Also

processEMG() for EMG processing pipeline, alignEMGtoMoCap() for time-alignment of EMG to MoCap, synchronizeSignals() for general multi-signal synchronization.

Examples

mocap <- matrix(rnorm(500), ncol = 5)
emg <- matrix(rnorm(5000), ncol = 2)
out <- integrateEMGMoCap(mocap, emg, mocap_sampling_rate = 100,
                         emg_sampling_rate = 1000)

Description

Computes sagittal-plane ankle, knee, and hip net moments from joint-center coordinates, GRF, COP, and segment inertial terms.

Usage

inverseDynamics2D(
  joints,
  grf,
  sampling_rate,
  angles = NULL,
  angular_velocity = NULL,
  angular_acceleration = NULL,
  inertial = NULL,
  angle_unit = c("radian", "degree")
)

Arguments

Value

A data.frame with time index and joint moments (ankle_moment, knee_moment, hip_moment). If angular velocity is available, corresponding power columns are included.

References

Winter DA (2009). "Biomechanics and Motor Control of Human Movement." 4th ed. John Wiley & Sons.

See Also

inverseDynamics3D() for 3D inverse dynamics computation, estimateSegmentInertia() for segment inertial properties, computeJointPower() for joint power calculation.

Examples

n <- 200
joints <- data.frame(
  ankle_x = rep(0.00, n), ankle_y = rep(0.05, n),
  knee_x = rep(0.00, n),  knee_y = rep(0.45, n),
  hip_x = rep(0.00, n),   hip_y = rep(0.85, n)
)
grf <- data.frame(fx = rep(0, n), fy = abs(sin(seq(0, pi, length.out = n))) * 800,
                  cop_x = rep(0.02, n), cop_y = rep(0, n))
id <- inverseDynamics2D(joints, grf, sampling_rate = 100)

Description

Computes ankle, knee, and hip net moments in 3D from joint-center coordinates, GRF, COP, and optional inertial terms.

Usage

inverseDynamics3D(
  joints,
  grf,
  sampling_rate,
  angles = NULL,
  angular_velocity = NULL,
  angular_acceleration = NULL,
  inertial = NULL,
  angle_unit = c("radian", "degree")
)

Arguments

Value

A data.frame with time and moment components: ⁠*_moment_x⁠, ⁠*_moment_y⁠, ⁠*_moment_z⁠. If angular velocity is available, ⁠*_power_total⁠ columns are included.

References

Winter DA (2009). "Biomechanics and Motor Control of Human Movement." 4th ed. John Wiley & Sons.

See Also

inverseDynamics2D() for sagittal-plane inverse dynamics, estimateSegmentInertia() for segment inertial properties, computeJointPower() for joint power calculation.

Examples

n <- 200
joints <- data.frame(
  ankle_x = rep(0.00, n), ankle_y = rep(0.00, n), ankle_z = rep(0.05, n),
  knee_x = rep(0.00, n),  knee_y = rep(0.00, n),  knee_z = rep(0.45, n),
  hip_x = rep(0.00, n),   hip_y = rep(0.00, n),   hip_z = rep(0.85, n)
)
grf <- data.frame(
  fx = rep(50, n), fy = rep(0, n), fz = rep(700, n),
  cop_x = rep(0.02, n), cop_y = rep(0, n), cop_z = rep(0, n)
)
out <- inverseDynamics3D(joints, grf, sampling_rate = 100)

Description

Get list of all pre-built schemas

Usage

listSchemas()

Value

Named list of all available pre-built TaskSchema objects

References

Winter DA (2009). "Biomechanics and Motor Control of Human Movement." 4th ed. Wiley.

See Also

getSchema(), TaskSchema(), validateSchema()

Examples

schemas <- listSchemas()
names(schemas)

Description

Create a detected_events object from manually specified times or indices.

Usage

manualEvents(
  schema,
  times = NULL,
  indices = NULL,
  sampling_rate = NULL,
  n_samples
)

Arguments

Value

A detected_events data.frame

References

Winter DA (2009). "Biomechanics and Motor Control of Human Movement." 4th ed. Wiley.

See Also

detectEvents(), segmentPhases(), TaskSchema()

Examples

events <- manualEvents(
  schema_gait,
  times = c(hs1 = 0, to = 0.6, hs2 = 1.0),
  sampling_rate = 1000,
  n_samples = 1000
)

Description

Computes the minimal detectable change from the standard error of measurement.

Usage

mdc(sem_value, confidence = 0.95)

Arguments

Details

MDC is computed as:

$MDC = SEM \times z \times \sqrt{2}$

where $z = \Phi^{-1}(1 - (1 - confidence)/2)$.