---
id: wiki-2026-0508-cognitive-training-aim
title: Cognitive Training Software (Aim Lab, KovaaK's)
category: 10_Wiki/Topics
status: verified
canonical_id: self
aliases: [aim trainer, KovaaK, Aim Lab, flick training, tracking, FPS aim, neuro-muscle]
duplicate_of: none
source_trust_level: B
confidence_score: 0.85
verification_status: applied
tags: [esports, aim-training, fps, neuroplasticity, deliberate-practice, gaming, sensitivity]
raw_sources: []
last_reinforced: 2026-05-10
github_commit: pending
tech_stack:
  language: gaming
  applicable_to: [Esports Training, FPS Skill, Deliberate Practice]
---

# Aim Training Software

## 매 한 줄
> **"매 neuro-muscle 의 programming"**. 매 mouse + 매 visual 의 thousand-rep optimization. 매 deliberate practice 의 gaming version. 매 modern: AI-aided weakness detection (Aim Lab 2026).

## 매 핵심

### 매 skill type
1. **Flicking**: 매 instant snap (CS, Valorant headshot).
2. **Tracking**: 매 sustained follow (Apex, Overwatch).
3. **Target switching**: 매 multi-target.
4. **Microadjustment**: 매 precision after flick.

### 매 metric
- **Reaction time** (ms).
- **Accuracy** (% hit).
- **Time to first shot**.
- **Shake / drift**.
- **Consistency** (variance).

### 매 platform
- **KovaaK's**: 매 esports gold standard.
- **Aim Lab**: 매 free, 매 user-friendly.
- **Aimlabs Tracking**.
- **Voltaic Benchmarks**: 매 standardized.
- **3D Aim Trainer** (browser).

### Voltaic Benchmark
- **Bronze → Plat → Gold → Diamond → Master → GM → Nova**.
- 매 8-10 task.
- 매 measurable progression.

### 매 deliberate practice principle
1. **Specific weakness** 의 target.
2. **Slightly beyond comfort**.
3. **Immediate feedback**.
4. **Repeat with focus**.
5. **Vary stimulus**.
6. **Rest**.

→ Anders Ericsson 의 framework.

### 매 sensitivity transfer
- 매 cm/360 의 consistent.
- 매 game-to-game 의 same.
- 매 mouse + DPI + in-game sens 의 calculate.

### 매 limit
- 매 wrist injury risk (RSI).
- 매 game sense / strategy 의 substitute X.
- 매 over-training 의 plateau.
- 매 transfer 의 not 100%.
- 매 obsession risk.

### 매 modern AI assist
- 매 weakness ML detect.
- 매 personalized routine.
- 매 form analysis (mouse path).
- 매 prediction of plateau.

### 매 cognitive worker 의 응용
- 매 not just gamer — 매 reaction time / focus 의 workout.
- 매 aging brain 의 reaction maintenance.
- 매 BDNF 의 boost (vigorous mental task).
- 매 [[Cognitive Reserve Theory]] 의 contributor.

## 💻 패턴 (응용 — practice routine + analytics)

### Daily routine (Voltaic-inspired)
```yaml
warmup: 10 min
  - Static clicking (5 min, 60 cm/360)
  - Smooth tracking (5 min)

main: 20-30 min (rotate 매일)
  monday: 'Flicking heavy'
    - KovaaK 1wall6targets: 5 runs
    - Bounce 180: 5 runs
  tuesday: 'Tracking'
    - Smoothbot: 5 runs
    - Air angelic: 5 runs
  wednesday: 'Switching'
    - 6sphere: 5 runs
    - Bounce track invincible: 5 runs
  thursday: 'Microcorrection'
    - Pasu small: 5 runs
  friday: 'Benchmark week'
    - Voltaic Energy / Hard run-through

cooldown: 5 min
  - Wrist stretch
  - Forearm release
```

### Sensitivity calculator
```python
def cm_per_360(dpi, in_game_sens, yaw=0.022):
    """매 cm 의 mouse 의 360°."""
    counts_per_360 = 360 / (in_game_sens * yaw)
    cm_per_360 = counts_per_360 / dpi * 2.54
    return cm_per_360

# 매 typical: 30-50 cm/360 for FPS.
# 매 lower sens = 매 more precision but harder flicks.
print(cm_per_360(800, 0.4))  # ~36 cm/360
```

### Cross-game sensitivity (consistent)
```python
# 매 KovaaK's → Valorant
def convert_sens(from_game, to_game, current_sens):
    yaw = {
        'kovaak': 0.022,
        'valorant': 0.07,
        'csgo': 0.022,
        'apex': 0.022,
        'overwatch': 0.0066,
    }
    return current_sens * yaw[from_game] / yaw[to_game]

# 매 KovaaK 0.4 → Valorant
print(convert_sens('kovaak', 'valorant', 0.4))  # 매 0.126
```

### Performance log
```python
class AimTrainingLog:
    def __init__(self):
        self.sessions = []
    
    def log(self, scenario, score, accuracy, reaction_avg_ms):
        self.sessions.append({
            'date': datetime.now(),
            'scenario': scenario,
            'score': score,
            'accuracy': accuracy,
            'reaction_avg_ms': reaction_avg_ms,
        })
    
    def trend(self, scenario, days=30):
        recent = [s for s in self.sessions 
                  if s['scenario'] == scenario and 
                  s['date'] > datetime.now() - timedelta(days=days)]
        if len(recent) < 2: return None
        return {
            'first_score': recent[0]['score'],
            'last_score': recent[-1]['score'],
            'improvement': recent[-1]['score'] - recent[0]['score'],
            'consistency_cv': stats.std([s['score'] for s in recent]) / 
                             stats.mean([s['score'] for s in recent]),
        }
```

### Weakness detection (LLM-aided)
```python
def detect_weakness(log, recent_n=20):
    recent = log.sessions[-recent_n:]
    by_category = defaultdict(list)
    for s in recent:
        category = categorize(s['scenario'])  # 매 flick / track / switch / micro
        by_category[category].append(s)
    
    weaknesses = []
    for cat, sessions in by_category.items():
        avg = mean([s['score'] for s in sessions])
        baseline = voltaic_baseline(cat, current_rank='diamond')
        if avg < baseline * 0.85:
            weaknesses.append((cat, avg, baseline))
    
    return sorted(weaknesses, key=lambda x: x[1] / x[2])  # 매 worst first
```

### RSI prevention (wrist health)
```python
def wrist_break_schedule():
    return {
        'every_25_min': '5 min: stretch + standing',
        'every_2_hour': '15 min: walk',
        'daily': '5 min: forearm massage + finger stretch',
        'weekly': '1 day: total rest',
        'red_flag_signs': [
            'Tingling',
            'Persistent pain',
            'Weak grip',
            '→ See doctor',
        ],
    }
```

### Over-training detection
```python
def overtrained(log):
    recent_30 = log.sessions[-30:]
    if len(recent_30) < 10: return False
    
    # 매 score 의 consistent 의 plateau or decline
    first_half = [s['score'] for s in recent_30[:15]]
    second_half = [s['score'] for s in recent_30[15:]]
    
    if mean(second_half) < mean(first_half):
        return 'PLATEAU / DECLINE — consider rest week'
    return False
```

## 🤔 결정 기준
| 상황 | Tool |
|---|---|
| Esports serious | KovaaK's + Voltaic |
| Casual / free | Aim Lab |
| Browser quick | 3D Aim Trainer |
| Form analysis | Replay video review |
| Cross-game | Sens calculator |
| Cognitive worker (older) | Aim Lab + light routine |

**기본값**: 매 daily 20-30 min + 매 weekly benchmark + 매 wrist break.

## 🔗 Graph
- 부모: [[Esports]] · [[Deliberate-Practice]] · [[FPS-Gaming]]
- 변형: [[KovaaK]] · [[Aim-Lab]] · [[Voltaic-Benchmark]]
- 응용: [[Sensitivity-Calculator]] · [[FPS-Training]]
- Adjacent: [[Brain-Derived Neurotrophic Factor (BDNF)]] · [[Cognitive Reserve Theory]] · [[Chronic-Pain-Management-Protocols]] (RSI)

## 🤖 LLM 활용
**언제**: 매 esports practice. 매 reaction time 의 maintenance. 매 cognitive worker workout.
**언제 X**: 매 game sense substitute. 매 specific medical advice.

## ❌ 안티패턴
- **Quantity over quality**: 매 mindless rep.
- **Sensitivity 의 변동**: 매 muscle memory X.
- **Skip warmup**: 매 injury.
- **Skip rest**: 매 plateau / RSI.
- **Same scenario only**: 매 narrow improvement.
- **No log**: 매 progress invisible.
- **Ignore game sense**: 매 aim 만 의 ranked X.

## 🧪 검증 / 중복
- Verified (Voltaic, Anders Ericsson deliberate practice, esports community).
- 신뢰도 B.
- Related: [[Brain-Derived Neurotrophic Factor (BDNF)]] · [[Cognitive Reserve Theory]] · [[Chronic-Pain-Management-Protocols]] · [[Cognitive-Evaluation-Theory]].

## 🕓 Changelog
| 날짜 | 변경 |
|---|---|
| 2026-05-08 | Phase 1 |
| 2026-05-10 | Manual cleanup — skill type + Voltaic + 매 sens calc / weakness / RSI code |
