2nd/10_Wiki/Topics/AI_and_ML/Cognitive Training Software (eg Aim Lab_KovaaKs).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-cognitive-training-aim	Cognitive Training Software (Aim Lab, KovaaK's)	10_Wiki/Topics	verified	self	aim trainer KovaaK Aim Lab flick training tracking FPS aim neuro-muscle	none	B	0.85	applied	esports aim-training fps neuroplasticity deliberate-practice gaming sensitivity		2026-05-10	pending	language applicable_to gaming 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)

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

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)

# 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

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)

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)

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

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