refactor(topics): 멀티 에이전트용 지식 재편 — _Common(공통 기본기) + Domain_* 구조
에이전트 8종(대화형/프로그래머 C·S/디자이너/설계자/기획자/QA/PD/PM)에게 [공통 기본 능력 + 롤별 Specialty] 2층으로 지식을 주입하기 위한 재분류. 문서 내용·포맷은 무수정, 폴더 이동만 (6,372개 문서 수 보존 확인). - Topic_Programming → Domain_Programming (내부 구조 보존) - Topic_Graphic → Domain_Design - Topic_Business → Domain_Product - Topic_General → Domain_General - _Common 신설: Math(구 Topic_Math_Specialty), Reasoning(구 General/From_Thinking & Reasoning), Reasoning_Creativity(구 General/From_창의성), Communication(Poetic_Blog_Writing + From_writing) - 타 도메인의 From_* 폴더는 유지 (출처 표기일 뿐, 이미 도메인에 맞게 분류된 문서) - 빈 폴더 정리 (memory/procedures) - 에이전트→폴더 매핑은 workspace의 .astra/agent-knowledge-map.json (9개 에이전트) Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
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Antigravity Agent
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---
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id: wiki-2026-0508-exergaming
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title: Exergaming
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category: 10_Wiki/Topics
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status: verified
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canonical_id: self
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aliases: [Exergaming, Exergame, Fitness Gaming, Active Gaming]
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duplicate_of: none
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source_trust_level: A
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confidence_score: 0.9
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verification_status: applied
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tags: [game-design, fitness, vr, ar, health]
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raw_sources: []
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last_reinforced: 2026-05-10
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github_commit: pending
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tech_stack:
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language: design
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framework: vr-fitness
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---
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# Exergaming
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## 매 한 줄
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> **"매 game mechanic 을 physical exercise 와 결합한다"**. 매 Exergaming은 DDR(1998) 의 dance arcade에서 시작 → Wii Fit(2007) mass-market 진입 → Pokemon Go(2016) AR walking → 매 2026 Quest 3/Vision Pro VR fitness 의 mainstream 시대. 매 sedentary lifestyle 대응 + intrinsic motivation 결합.
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## 매 핵심
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### 매 Why it works
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- **Intrinsic motivation**: 매 fun → exercise (vs treadmill 의 외재 동기)
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- **Flow**: 매 difficulty/skill match — 매 exercise 인지 인식 안 됨
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- **Social**: 매 multiplayer 가 적정한 peer pressure
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- **Telemetry feedback**: 매 calorie, heart rate 즉시 가시화
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### 매 Technology stack
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- **Camera-based** (Kinect, smartphone pose): 매 markerless tracking
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- **Motion controller** (Wii, Quest, PSVR2): 매 6DOF tracking
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- **Wearable** (HRM, Apple Watch): 매 heart rate / step
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- **GPS-based** (Pokemon Go, Zwift): 매 outdoor / indoor cycling
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### 매 응용
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1. VR fitness (Beat Saber, Supernatural, Les Mills Bodycombat).
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2. Outdoor AR (Pokemon Go, Zombies Run!).
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3. Console fitness (Ring Fit Adventure, Just Dance).
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4. Indoor cycling (Zwift, Peloton + game).
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5. Rehab exergames (stroke recovery, balance training).
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## 💻 패턴
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### Pose tracking (MediaPipe + Python)
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```python
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import cv2
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import mediapipe as mp
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mp_pose = mp.solutions.pose
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pose = mp_pose.Pose(min_detection_confidence=0.7)
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cap = cv2.VideoCapture(0)
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squat_count = 0
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in_squat = False
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while cap.isOpened():
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ret, frame = cap.read()
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rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
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results = pose.process(rgb)
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if results.pose_landmarks:
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hip_y = results.pose_landmarks.landmark[24].y
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knee_y = results.pose_landmarks.landmark[26].y
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# 매 hip drops below knee = squat
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if hip_y > knee_y - 0.05 and not in_squat:
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in_squat = True
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elif hip_y < knee_y - 0.15 and in_squat:
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squat_count += 1
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in_squat = False
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print(f"매 squat #{squat_count}")
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```
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### Calorie estimation (METS + HR)
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```python
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def estimate_calories(weight_kg: float, duration_min: float,
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avg_hr: float, age: int, is_male: bool) -> float:
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# 매 Keytel formula
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if is_male:
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cal_per_min = (-55.0969 + 0.6309*avg_hr + 0.1988*weight_kg + 0.2017*age) / 4.184
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else:
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cal_per_min = (-20.4022 + 0.4472*avg_hr - 0.1263*weight_kg + 0.074*age) / 4.184
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return max(0, cal_per_min * duration_min)
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```
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### Beat Saber-style hit detection (Unity)
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```csharp
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public class Saber : MonoBehaviour
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{
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public Vector3 prevPos;
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public float minSpeedToCut = 2f;
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void OnTriggerEnter(Collider block)
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{
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var velocity = (transform.position - prevPos) / Time.deltaTime;
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if (velocity.magnitude < minSpeedToCut) return;
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var blockNormal = block.transform.up;
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var dot = Vector3.Dot(velocity.normalized, blockNormal);
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if (dot < -0.7f) { // 매 cutting in correct direction
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block.GetComponent<Block>().Cut();
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ScoreSystem.Add(100);
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}
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}
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void LateUpdate() { prevPos = transform.position; }
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}
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```
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### Heart rate zone (Apple Watch / WatchOS)
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```swift
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import HealthKit
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let store = HKHealthStore()
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let hrType = HKQuantityType.quantityType(forIdentifier: .heartRate)!
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let query = HKAnchoredObjectQuery(type: hrType, predicate: nil,
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anchor: nil, limit: HKObjectQueryNoLimit) {
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_, samples, _, _, _ in
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for sample in samples as? [HKQuantitySample] ?? [] {
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let bpm = sample.quantity.doubleValue(for: HKUnit(from: "count/min"))
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let zone = hrZone(bpm: bpm, age: 30)
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// 매 game difficulty 조정 — zone 4 너무 길면 reduce
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GameDifficulty.adjust(zone: zone)
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}
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}
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store.execute(query)
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func hrZone(bpm: Double, age: Int) -> Int {
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let max_hr = 220.0 - Double(age)
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let pct = bpm / max_hr
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switch pct {
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case ..<0.6: return 1
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case ..<0.7: return 2
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case ..<0.8: return 3
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case ..<0.9: return 4
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default: return 5
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}
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}
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```
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### GPS-based step (React Native)
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```typescript
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import Geolocation from '@react-native-community/geolocation';
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let totalDistance = 0;
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let prev: GeolocationPosition | null = null;
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Geolocation.watchPosition((position) => {
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if (prev) {
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const dist = haversine(
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prev.coords.latitude, prev.coords.longitude,
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position.coords.latitude, position.coords.longitude
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);
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if (dist < 50 && position.coords.speed < 5) { // 매 walking
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totalDistance += dist;
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spawnPokemonIfDistanceThreshold(totalDistance);
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}
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}
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prev = position;
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}, null, { enableHighAccuracy: true, distanceFilter: 5 });
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```
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### Adaptive difficulty by HR
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```python
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def adjust_intensity(current_hr: float, target_zone: tuple,
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game_difficulty: float) -> float:
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low, high = target_zone # 매 e.g. (140, 160) for zone 3
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if current_hr < low:
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return min(1.0, game_difficulty + 0.1) # 매 ramp up
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elif current_hr > high:
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return max(0.1, game_difficulty - 0.15) # 매 cool down
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return game_difficulty
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```
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## 매 결정 기준
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| 상황 | Platform |
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|---|---|
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| High-intensity cardio | VR (Beat Saber, Supernatural) |
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| Outdoor walking | AR mobile (Pokemon Go) |
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| Family casual | Console (Just Dance, Ring Fit) |
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| Strength training | Wearable + companion app |
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| Rehab / elderly | Camera-based (low barrier) |
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| Cycling | Smart trainer + Zwift |
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**기본값**: 매 target heart rate zone 3-4, session 20-45min, gamified streak + progression.
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## 🔗 Graph
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- 부모: [[Game Design]]
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- 변형: [[Active Gaming]]
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- 응용: [[Beat Saber]]
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- Adjacent: [[Gamification]] · [[Pose Estimation]]
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## 🤖 LLM 활용
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**언제**: 매 fitness app design, exercise game mechanic 설계, HR-based difficulty algorithm.
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**언제 X**: 매 medical-grade rehab — 매 clinical validation 필요, LLM scope 외.
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## ❌ 안티패턴
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- **No safety check**: 매 elderly user 에게 high-intensity 강제 → 매 injury risk.
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- **Overgamification**: 매 streak 압박이 매 overtraining 유발.
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- **No rest day**: 매 daily quest 만 있고 recovery 무시.
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- **Inaccurate calorie**: 매 inflated number → user trust 상실.
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## 🧪 검증 / 중복
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- Verified (Peng et al., "Is playing exergames really exercising?", 2013, systematic review).
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- Verified (Quest 3 fitness API, Meta Health, 2024).
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- 신뢰도 A.
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## 🕓 Changelog
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| 날짜 | 변경 |
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|---|---|
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| 2026-05-08 | Phase 1 |
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| 2026-05-10 | Manual cleanup — VR fitness + Pokemon Go + pose tracking |
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