---
id: wiki-2026-0508-exergaming
title: Exergaming
category: 10_Wiki/Topics
status: verified
canonical_id: self
aliases: [Exergaming, Exergame, Fitness Gaming, Active Gaming]
duplicate_of: none
source_trust_level: A
confidence_score: 0.9
verification_status: applied
tags: [game-design, fitness, vr, ar, health]
raw_sources: []
last_reinforced: 2026-05-10
github_commit: pending
tech_stack:
  language: design
  framework: vr-fitness
---

# Exergaming

## 매 한 줄
> **"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.

## 매 핵심

### 매 Why it works
- **Intrinsic motivation**: 매 fun → exercise (vs treadmill 의 외재 동기)
- **Flow**: 매 difficulty/skill match — 매 exercise 인지 인식 안 됨
- **Social**: 매 multiplayer 가 적정한 peer pressure
- **Telemetry feedback**: 매 calorie, heart rate 즉시 가시화

### 매 Technology stack
- **Camera-based** (Kinect, smartphone pose): 매 markerless tracking
- **Motion controller** (Wii, Quest, PSVR2): 매 6DOF tracking
- **Wearable** (HRM, Apple Watch): 매 heart rate / step
- **GPS-based** (Pokemon Go, Zwift): 매 outdoor / indoor cycling

### 매 응용
1. VR fitness (Beat Saber, Supernatural, Les Mills Bodycombat).
2. Outdoor AR (Pokemon Go, Zombies Run!).
3. Console fitness (Ring Fit Adventure, Just Dance).
4. Indoor cycling (Zwift, Peloton + game).
5. Rehab exergames (stroke recovery, balance training).

## 💻 패턴

### Pose tracking (MediaPipe + Python)
```python
import cv2
import mediapipe as mp

mp_pose = mp.solutions.pose
pose = mp_pose.Pose(min_detection_confidence=0.7)

cap = cv2.VideoCapture(0)
squat_count = 0
in_squat = False

while cap.isOpened():
    ret, frame = cap.read()
    rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
    results = pose.process(rgb)
    
    if results.pose_landmarks:
        hip_y = results.pose_landmarks.landmark[24].y
        knee_y = results.pose_landmarks.landmark[26].y
        # 매 hip drops below knee = squat
        if hip_y > knee_y - 0.05 and not in_squat:
            in_squat = True
        elif hip_y < knee_y - 0.15 and in_squat:
            squat_count += 1
            in_squat = False
            print(f"매 squat #{squat_count}")
```

### Calorie estimation (METS + HR)
```python
def estimate_calories(weight_kg: float, duration_min: float, 
                      avg_hr: float, age: int, is_male: bool) -> float:
    # 매 Keytel formula
    if is_male:
        cal_per_min = (-55.0969 + 0.6309*avg_hr + 0.1988*weight_kg + 0.2017*age) / 4.184
    else:
        cal_per_min = (-20.4022 + 0.4472*avg_hr - 0.1263*weight_kg + 0.074*age) / 4.184
    return max(0, cal_per_min * duration_min)
```

### Beat Saber-style hit detection (Unity)
```csharp
public class Saber : MonoBehaviour
{
    public Vector3 prevPos;
    public float minSpeedToCut = 2f;
    
    void OnTriggerEnter(Collider block)
    {
        var velocity = (transform.position - prevPos) / Time.deltaTime;
        if (velocity.magnitude < minSpeedToCut) return;
        
        var blockNormal = block.transform.up;
        var dot = Vector3.Dot(velocity.normalized, blockNormal);
        if (dot < -0.7f) {  // 매 cutting in correct direction
            block.GetComponent<Block>().Cut();
            ScoreSystem.Add(100);
        }
    }
    
    void LateUpdate() { prevPos = transform.position; }
}
```

### Heart rate zone (Apple Watch / WatchOS)
```swift
import HealthKit

let store = HKHealthStore()
let hrType = HKQuantityType.quantityType(forIdentifier: .heartRate)!

let query = HKAnchoredObjectQuery(type: hrType, predicate: nil, 
                                   anchor: nil, limit: HKObjectQueryNoLimit) { 
    _, samples, _, _, _ in
    for sample in samples as? [HKQuantitySample] ?? [] {
        let bpm = sample.quantity.doubleValue(for: HKUnit(from: "count/min"))
        let zone = hrZone(bpm: bpm, age: 30)
        // 매 game difficulty 조정 — zone 4 너무 길면 reduce
        GameDifficulty.adjust(zone: zone)
    }
}
store.execute(query)

func hrZone(bpm: Double, age: Int) -> Int {
    let max_hr = 220.0 - Double(age)
    let pct = bpm / max_hr
    switch pct {
    case ..<0.6: return 1
    case ..<0.7: return 2
    case ..<0.8: return 3
    case ..<0.9: return 4
    default: return 5
    }
}
```

### GPS-based step (React Native)
```typescript
import Geolocation from '@react-native-community/geolocation';

let totalDistance = 0;
let prev: GeolocationPosition | null = null;

Geolocation.watchPosition((position) => {
    if (prev) {
        const dist = haversine(
            prev.coords.latitude, prev.coords.longitude,
            position.coords.latitude, position.coords.longitude
        );
        if (dist < 50 && position.coords.speed < 5) {  // 매 walking
            totalDistance += dist;
            spawnPokemonIfDistanceThreshold(totalDistance);
        }
    }
    prev = position;
}, null, { enableHighAccuracy: true, distanceFilter: 5 });
```

### Adaptive difficulty by HR
```python
def adjust_intensity(current_hr: float, target_zone: tuple, 
                     game_difficulty: float) -> float:
    low, high = target_zone  # 매 e.g. (140, 160) for zone 3
    if current_hr < low:
        return min(1.0, game_difficulty + 0.1)  # 매 ramp up
    elif current_hr > high:
        return max(0.1, game_difficulty - 0.15)  # 매 cool down
    return game_difficulty
```

## 매 결정 기준
| 상황 | Platform |
|---|---|
| High-intensity cardio | VR (Beat Saber, Supernatural) |
| Outdoor walking | AR mobile (Pokemon Go) |
| Family casual | Console (Just Dance, Ring Fit) |
| Strength training | Wearable + companion app |
| Rehab / elderly | Camera-based (low barrier) |
| Cycling | Smart trainer + Zwift |

**기본값**: 매 target heart rate zone 3-4, session 20-45min, gamified streak + progression.

## 🔗 Graph
- 부모: [[Game Design]]
- 변형: [[Active Gaming]]
- 응용: [[Beat Saber]]
- Adjacent: [[Gamification]] · [[Pose Estimation]]

## 🤖 LLM 활용
**언제**: 매 fitness app design, exercise game mechanic 설계, HR-based difficulty algorithm.
**언제 X**: 매 medical-grade rehab — 매 clinical validation 필요, LLM scope 외.

## ❌ 안티패턴
- **No safety check**: 매 elderly user 에게 high-intensity 강제 → 매 injury risk.
- **Overgamification**: 매 streak 압박이 매 overtraining 유발.
- **No rest day**: 매 daily quest 만 있고 recovery 무시.
- **Inaccurate calorie**: 매 inflated number → user trust 상실.

## 🧪 검증 / 중복
- Verified (Peng et al., "Is playing exergames really exercising?", 2013, systematic review).
- Verified (Quest 3 fitness API, Meta Health, 2024).
- 신뢰도 A.

## 🕓 Changelog
| 날짜 | 변경 |
|---|---|
| 2026-05-08 | Phase 1 |
| 2026-05-10 | Manual cleanup — VR fitness + Pokemon Go + pose tracking |
