Skip to content

MATLAB API Reference

Overview

The MATLAB API provides native MATLAB functions for analyzing standardized biomechanical datasets.

Coming Soon

The MATLAB API documentation is currently under development. Check back soon for complete function references.

Core Functions

Data Loading

load_locomotion_data

Load a standardized biomechanical dataset.

data = load_locomotion_data(filepath)
data = load_locomotion_data(filepath, 'columns', {'col1', 'col2'})

Parameters: - filepath (string) - Path to parquet file - columns (cell array, optional) - Specific columns to load

Returns: - data (table) - MATLAB table containing the dataset

Data Exploration

get_subjects

Get unique subject identifiers from dataset.

subjects = get_subjects(data)

get_tasks

Get unique task names from dataset.

tasks = get_tasks(data)

Data Filtering

filter_task

Filter dataset for a specific task.

filtered_data = filter_task(data, 'level_walking')

filter_subject

Filter dataset for a specific subject.

filtered_data = filter_subject(data, 'SUB01')

Analysis Functions

get_cycles

Extract individual gait cycles.

[cycles_3d, features] = get_cycles(data, subject, task)

Parameters: - data (table) - Dataset - subject (string) - Subject ID - task (string) - Task name

Returns: - cycles_3d (array) - 3D array of cycles (cycles × phase × variables) - features (table) - Cycle-level features

get_mean_patterns

Compute mean patterns across cycles.

mean_patterns = get_mean_patterns(data, subject, task)

calculate_rom

Calculate range of motion for all variables.

rom = calculate_rom(data, subject, task)

Visualization

plot_phase_patterns

Create phase-averaged plots with standard deviation bands.

plot_phase_patterns(data, subject, task, variables)
plot_phase_patterns(data, subject, task, variables, 'ShowSD', true)

Parameters: - data (table) - Dataset - subject (string) - Subject ID - task (string) - Task name - variables (cell array) - Variable names to plot - ShowSD (logical, optional) - Show standard deviation bands

Usage Examples

Basic Workflow

% Load data
data = load_locomotion_data('converted_datasets/umich_2021_phase.parquet');

% Filter for specific task
level_walking = filter_task(data, 'level_walking');

% Get mean patterns
mean_patterns = get_mean_patterns(level_walking, 'SUB01', 'level_walking');

% Visualize
plot_phase_patterns(level_walking, 'SUB01', 'level_walking', ...
                   {'knee_flexion_angle_ipsi_rad', 'hip_flexion_angle_ipsi_rad'});

Memory-Efficient Loading

% Load only specific columns
columns = {'subject', 'task', 'cycle_id', 'phase_percent', ...
           'knee_flexion_angle_ipsi_rad', 'hip_flexion_angle_ipsi_rad'};
data = load_locomotion_data('dataset.parquet', 'columns', columns);

Data Structures

Dataset Table

The main data structure is a MATLAB table with columns: - subject - Subject identifier - task - Task name - cycle_id - Gait cycle number - phase_percent - Gait cycle phase (0-100) - Biomechanical variables (angles in radians, moments in Nm, forces in N)

Variable Naming Convention

  • Format: joint_motion_side_unit
  • Example: knee_flexion_angle_ipsi_rad
  • Joint: knee
  • Motion: flexion
  • Side: ipsi (ipsilateral) or contra (contralateral)
  • Unit: rad (radians)

See Also