2nd/10_Wiki/Topic_General/From_Other/Neuromuscular-Control.md

id, title, category, status, canonical_id, aliases, duplicate_of, source_trust_level, confidence_score, verification_status, tags, raw_sources, last_reinforced, github_commit, tech_stack

id	title	category	status	canonical_id	aliases	duplicate_of	source_trust_level	confidence_score	verification_status	tags	raw_sources	last_reinforced	github_commit	tech_stack
wiki-2026-0508-neuromuscular-control	Neuromuscular Control	10_Wiki/Topics	verified	self	신경근 조절 Motor Control NMS Control	none	A	0.9	applied	neuroscience biomechanics motor-control robotics biomedical		2026-05-10	pending	language framework python opensim

Neuromuscular Control

매 한 줄

"매 brain plans, spine reflexes, muscle executes". Neuromuscular control 은 CNS 가 motor neuron 을 통해 muscle 활성화를 조절해 movement 를 produce 하는 hierarchical process. 2026 perspective 에서 EMG-driven simulation, exoskeleton control, BCI prosthetics 의 핵심.

매 핵심

매 hierarchy

• Cortex (M1, PMC, SMA): motor planning.
• Cerebellum: timing, coordination, error correction.
• Basal ganglia: action selection, gain modulation.
• Spinal cord: reflex circuits, central pattern generators (CPG).
• Motor unit: α-MN + muscle fibers (final common pathway).

매 control principles

• Size principle (Henneman): small MN 먼저, large 나중.
• Co-contraction: agonist + antagonist 동시 활성화 → stiffness 조절.
• Stretch reflex: muscle spindle Ia → α-MN monosynaptic.
• Equilibrium point hypothesis: descending command = desired length.

매 응용

1. Prosthetic / exoskeleton control.
2. Rehabilitation robotics.
3. Surgical motor mapping.
4. Sport biomechanics.

💻 패턴

Hill-type muscle model

import numpy as np

def hill_muscle(activation, length, velocity,
                F_max=1000, l_opt=0.1, v_max=10):
    f_l = np.exp(-((length - l_opt) / (0.5 * l_opt))**2)
    if velocity <= 0:
        f_v = (v_max + velocity) / (v_max - 4 * velocity)
    else:
        f_v = (1.8 - 0.8 * (v_max + velocity) / (v_max - 7.56 * velocity))
    return F_max * activation * f_l * f_v

Motor unit recruitment (size principle)

def recruit(excitation, n_units=100, threshold_max=1.0):
    thresholds = np.linspace(0.05, threshold_max, n_units)
    return np.where(excitation > thresholds,
                    (excitation - thresholds) / (1 - thresholds), 0)

EMG → activation mapping

from scipy.signal import butter, filtfilt

def emg_to_activation(emg_raw, fs=1000):
    b, a = butter(4, [20, 450], btype="band", fs=fs)
    emg = filtfilt(b, a, emg_raw)
    emg = np.abs(emg)
    b, a = butter(4, 6, btype="low", fs=fs)
    env = filtfilt(b, a, emg)
    return env / env.max()

Inverse dynamics (joint torque)

def inverse_dynamics(theta, theta_dot, theta_ddot, m=5, l=0.4, g=9.81):
    I = m * l**2 / 3
    return I * theta_ddot + 0.5 * m * g * l * np.sin(theta)

CPG (Matsuoka oscillator)

def matsuoka_step(x1, x2, v1, v2, u=1.0, beta=2.5, tau=0.1, dt=0.001):
    y1, y2 = max(0, x1), max(0, x2)
    dx1 = (-x1 - beta*v1 - 2.0*y2 + u) / tau
    dx2 = (-x2 - beta*v2 - 2.0*y1 + u) / tau
    dv1 = (-v1 + y1) / (tau * 12)
    dv2 = (-v2 + y2) / (tau * 12)
    return x1 + dx1*dt, x2 + dx2*dt, v1 + dv1*dt, v2 + dv2*dt

EMG-driven prosthetic control

class MyoelectricController:
    def __init__(self, n_channels=8):
        self.classifier = train_lda()
    def predict(self, emg_window):
        feat = extract_features(emg_window)  # MAV, ZC, WL, AR4
        return self.classifier.predict(feat)

매 결정 기준

상황	Approach
Whole-body simulation	OpenSim / MyoSuite
Single joint, real-time	Hill-type + LDA EMG
Locomotion robot	CPG + reflexes
Pathology study	Inverse dynamics + EMG

기본값: Hill-type muscle + size-principle recruitment + EMG envelope.

🔗 Graph

• 변형: Perceptual-Motor-Skills
• Adjacent: Reinforcement Learning · BCI

🤖 LLM 활용

언제: simulation parameter tuning, EMG feature engineering, paper synthesis. 언제 X: clinical motor diagnosis — neurologist 필수.

❌ 안티패턴

• Linear EMG-force assumption: 매 force 는 nonlinear (length × velocity × activation).
• Ignoring co-contraction: stiffness control 무시 → unstable model.
• Pure feedback control: feed-forward (internal model) 누락 → laggy.

🧪 검증 / 중복

• Verified (Zajac 1989, Delp OpenSim 2018, Henneman 1965).
• 신뢰도 A.

🕓 Changelog

날짜	변경
2026-05-08	Phase 1
2026-05-10	Manual cleanup — Hill model + EMG + CPG 패턴