docs(10_Wiki): 위키 전체 재구성 — Topic_* 폴더를 4개 카테고리로 통합 + 대규모 중복 제거

Topic_Agent/Topic_Blog/Topics/Topics_Biz/Topics_Meeting/Topics_Rag의 마크다운 지식 문서를 Topic_General/Topic_Programming/Topic_Graphic/Topic_Business 4개 카테고리로 재분류. - 중복 제거: frontmatter의 status:duplicate/merged + duplicate_of/redirect_to 필드로 자기 자신을 중복으로 선언한 리다이렉트 stub 1032개 제거, 완전 동일 내용 파일 472개 제거, 동일 파일명·다른 내용 충돌 시 더 큰(완전한) 버전만 유지(162개 제거) — 총 1639개 중복 제거. - 분류: 폴더 단위로 명확한 항목(AI_and_ML/Coding/Architecture 등 → Programming, Comfyui/Visual_Effects → Graphic, Topics_Biz/Topics_Meeting/사업 등 → Business, Poetic_Blog_Writing/창의성/Game_Design 등 → General)은 폴더 우선순위로, 나머지 혼재 폴더(Topic_Agent/Topic_Blog/Topics 루트/Thinking & Reasoning/Other/UI_UX_Assets)는 title/tags 키워드 스코어링으로 파일 단위 분류(불명확한 경우 General로 폴백). 원본 폴더명은 "From_*" 서브폴더로 보존해 추적 가능성 유지. - 최종 배치: Programming 2784 / General 1608 / Graphic 285 / Business 249 = 4926개 문서. - 에이전트 운영 상태(.astra/.agent/.obsidian/sessions/memory/_company/docs/lessons/_shared/src)는 지식 콘텐츠가 아니므로 재분류 대상에서 제외하고 원위치 유지. - Topics/Topic_email(상위 보호 폴더 Topic_email과 파일명 100% 중복) 삭제 — 보호 폴더 자체는 미변경. - 완전히 비게 된 Topic_Agent/Topic_Blog/Topics_Biz/Topics_Rag 폴더 제거.
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+---
+id: wiki-2026-0508-neuromuscular-control
+title: Neuromuscular Control
+category: 10_Wiki/Topics
+status: verified
+canonical_id: self
+aliases: [신경근 조절, Motor Control, NMS Control]
+duplicate_of: none
+source_trust_level: A
+confidence_score: 0.9
+verification_status: applied
+tags: [neuroscience, biomechanics, motor-control, robotics, biomedical]
+raw_sources: []
+last_reinforced: 2026-05-10
+github_commit: pending
+tech_stack:
+  language: python
+  framework: 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
+```python
+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)
+```python
+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
+```python
+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)
+```python
+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)
+```python
+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
+```python
+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 |