Note

Click here to download the full example code

Recruitment Thresholds

The first step in using MyoGen is to generate the recruitment thresholds of the motor units (MUs).

Note

A recruitment threshold is the minimum force required to activate a MU.

In MyoGen, the threshold is defined between 0 and 1, where 0 is the minimum force required to activate a MU and 1 is the maximum force required to activate a MU.

MyoGen offers 4 different models to generate the recruitment thresholds:

• Fuglevand model: Classic exponential distribution (Fuglevand et al., 1993)
• De Luca model: Slope-corrected exponential distribution (De Luca & Contessa, 2012)
• Konstantin model: Exponential with explicit maximum threshold control (Akhmadeev et al., 2020; the mode is named after the first author's given name, Konstantin)
• Combined model: Hybrid approach combining De Luca shape with Konstantin scaling (Ours)

Import Libraries

from pathlib import Path

import joblib
from matplotlib import pyplot as plt
from myogen import load_profile, simulator

plt.style.use("fivethirtyeight")

Define Common Parameters

Each recruitment threshold simulation is defined by the following parameters:

• N: Number of motor units in the pool
• recruitment_range: Recruitment range (max_threshold / min_threshold)

Note

The recruitment_range is defined as the ratio between the maximum and minimum recruitment thresholds. For example, if the recruitment_range is 50, the biggest MU will have a recruitment threshold 50 times bigger than the smallest MU.

n_motor_units = 100  # Number of motor units in the pool
recruitment_range = 100  # Recruitment range (max_threshold / min_threshold)

# Create results directory
save_path = Path("./results")
save_path.mkdir(exist_ok=True)

Fuglevand Model

The Fuglevand model uses a simple exponential distribution for recruitment thresholds. This is the classic approach from Fuglevand et al. (1993).

No additional parameters needed - only requires the common parameters.

Important

MyoGen is intended to be used with the following API:

from myogen import simulator

rt_fuglevand, _ = simulator.RecruitmentThresholds(
    N=n_motor_units, recruitment_range__ratio=recruitment_range, mode="fuglevand"
)

plt.plot(rt_fuglevand * 100, "-o", label="Fuglevand Model")
plt.title("Fuglevand Recruitment Thresholds")
plt.xlabel("Motor Unit (#)")
plt.ylabel("Recruitment Threshold (%)")
plt.tight_layout()
plt.show()

De Luca Model

The De Luca model includes a slope correction parameter that allows control over the shape of the recruitment threshold distribution.

Additional parameter:

• deluca__slope: Controls the shape of the distribution

deluca_results = {
    slope: simulator.RecruitmentThresholds(
        N=n_motor_units,
        recruitment_range__ratio=recruitment_range,
        deluca__slope=slope,
        mode="deluca",
    )[0]
    for slope in [0.001, 5, 25, 50]
}

for s, rt in deluca_results.items():
    plt.plot(rt * 100, "-o", label=f"Slope={s}")
plt.title("De Luca Recruitment Thresholds")
plt.xlabel("Motor Unit (#)")
plt.ylabel("Recruitment Threshold (%)")
plt.legend(framealpha=1.0, edgecolor="none")
plt.tight_layout()
plt.show()

Konstantin Model

The Konstantin model provides explicit control over the maximum recruitment threshold while maintaining physiological recruitment patterns.

Additional parameter:

• konstantin__max_threshold: Maximum recruitment threshold

rt_konstantin, _ = simulator.RecruitmentThresholds(
    N=n_motor_units,
    recruitment_range__ratio=recruitment_range,
    konstantin__max_threshold__ratio=1.0,
    mode="konstantin",
)

plt.plot(rt_konstantin * 100, "-o", label="Konstantin Model")
plt.title("Konstantin Recruitment Thresholds")
plt.xlabel("Motor Unit (#)")
plt.ylabel("Recruitment Threshold (%)")
plt.tight_layout()
plt.show()

Combined Model

The Combined model merges De Luca's shape control with Konstantin's scaling, offering the most flexibility for custom recruitment patterns.

Additional parameters:

• deluca__slope: Controls the shape of the distribution
• konstantin__max_threshold: Maximum recruitment threshold # Slopes for combined model

combined_results = {
    slope: simulator.RecruitmentThresholds(
        N=n_motor_units,
        recruitment_range__ratio=recruitment_range,
        deluca__slope=slope,
        konstantin__max_threshold__ratio=1.0,
        mode="combined",
    )[0]
    for slope in [0.001, 5, 25, 50]
}

for s, rt in combined_results.items():
    plt.plot(rt * 100, "-o", label=f"Slope={s}")
plt.title("Combined Recruitment Thresholds")
plt.xlabel("Motor Unit (#)")
plt.ylabel("Recruitment Threshold (%)")
plt.legend(framealpha=1.0, edgecolor="none")
plt.tight_layout()
plt.show()

The FDI Profile Card ^^^^^^^^^^^^^^^^^^^^

The following examples simulate the first dorsal interosseous (FDI) with the fdi_semg profile card, which holds every validated setting with its source (myogen/profiles/fdi_semg.yaml). Its thresholds are the ones the FDI drive was calibrated with: 120 units, Fuglevand model, recruitment range 50.

Note

load_profile("fdi_semg.published") gives each component as its source paper describes it.

card = load_profile()  # fdi_semg
recruitment_thresholds = card.thresholds()

plt.plot(recruitment_thresholds * 100, "-o")
plt.title("FDI Card Recruitment Thresholds")
plt.xlabel("Motor Unit (#)")
plt.ylabel("Recruitment Threshold (%)")
plt.tight_layout()
plt.show()

Save Recruitment Thresholds ^^^^^^^^^^^^^^^^^^^^^^^^^^^^

Note

All MyoGen objects can be saved to a file using joblib. This is useful to avoid re-running expensive simulations if you need to use the same parameters.

joblib.dump(recruitment_thresholds, save_path / "thresholds.pkl")

Out:

['results/thresholds.pkl']

Total running time of the script: ( 0 minutes 0.371 seconds)

Download Python source code: 01_simulate_recruitment_thresholds.py

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