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fitness-analyzer

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运动分析器技能

Fitness Analyzer Skill

分析运动数据，识别运动模式，评估健身进展，并提供个性化训练建议。
Analyze sports data, identify exercise patterns, evaluate fitness progress, and provide personalized training recommendations.

功能

Features

1. 运动趋势分析

1. Exercise Trend Analysis

分析运动量、频率、强度的变化趋势，识别改善或需要调整的方面。
分析维度：
  • 运动量趋势（时长、距离、卡路里）
  • 运动频率趋势（每周运动天数）
  • 强度分布变化（低/中/高强度占比）
  • 运动类型偏好变化
输出：
  • 趋势方向（改善/稳定/下降）
  • 变化幅度和百分比
  • 趋势显著性
  • 改进建议
Analyze the changing trends of exercise volume, frequency, and intensity, and identify areas for improvement or adjustment.
Analysis Dimensions:
  • Exercise volume trends (duration, distance, calories)
  • Exercise frequency trends (number of exercise days per week)
  • Intensity distribution changes (proportion of low/medium/high intensity)
  • Changes in exercise type preferences
Output:
  • Trend direction (improvement/stable/decline)
  • Magnitude and percentage of change
  • Trend significance
  • Improvement suggestions

2. 运动进步追踪

2. Fitness Progress Tracking

追踪特定运动类型的进步情况，量化健身效果。
支持的进步追踪：
  • 跑步进步：配速提升、距离增加、心率改善
  • 力量训练进步：重量增加、容量提升、RPE变化
  • 耐力进步：运动时长增加、距离延长
  • 柔韧性进步：关节活动度改善
输出：
  • 开始值 vs 当前值
  • 改善百分比
  • 进步可视化
  • 达成的里程碑
Track progress in specific exercise types and quantify fitness effects.
Supported Progress Tracking:
  • Running Progress: Pace improvement, distance increase, heart rate improvement
  • Strength Training Progress: Weight increase, volume improvement, RPE changes
  • Endurance Progress: Exercise duration increase, distance extension
  • Flexibility Progress: Joint mobility improvement
Output:
  • Starting value vs. current value
  • Improvement percentage
  • Progress visualization
  • Achieved milestones

3. 运动习惯分析

3. Exercise Habit Analysis

识别用户的运动习惯和模式。
分析内容：
  • 常用运动时间（早晨/下午/晚上）
  • 运动频率模式（每周几天）
  • 运动类型偏好
  • 休息日分布
  • 运动一致性评分
输出：
  • 习惯总结
  • 一致性评分（0-100）
  • 优化建议
  • 习惯养成建议
Identify users' exercise habits and patterns.
Analysis Content:
  • Common exercise times (morning/afternoon/evening)
  • Exercise frequency patterns (number of days per week)
  • Exercise type preferences
  • Rest day distribution
  • Exercise consistency score
Output:
  • Habit summary
  • Consistency score (0-100)
  • Optimization suggestions
  • Habit formation advice

4. 相关性分析

4. Correlation Analysis

分析运动与其他健康指标的相关性。
支持的相关性分析：
  • 运动 ↔ 体重：运动消耗与体重变化的关系
  • 运动 ↔ 血压：运动对血压的长期影响
  • 运动 ↔ 血糖：运动对血糖控制的效果
  • 运动 ↔ 情绪/睡眠：运动对情绪和睡眠的影响
输出：
  • 相关系数（-1到1）
  • 相关性强度（弱/中/强）
  • 统计显著性
  • 因果关系推断
  • 实践建议
Analyze the correlation between exercise and other health indicators.
Supported Correlation Analysis:
  • Exercise ↔ Weight: Relationship between exercise consumption and weight changes
  • Exercise ↔ Blood Pressure: Long-term impact of exercise on blood pressure
  • Exercise ↔ Blood Glucose: Effect of exercise on blood glucose control
  • Exercise ↔ Mood/Sleep: Impact of exercise on mood and sleep
Output:
  • Correlation coefficient (-1 to 1)
  • Correlation strength (weak/medium/strong)
  • Statistical significance
  • Causal inference
  • Practical suggestions

5. 个性化建议生成

5. Personalized Recommendation Generation

基于用户数据生成个性化运动建议。
建议类型：
  • 运动频率建议：是否需要增加/减少运动频率
  • 运动强度建议：强度调整建议
  • 运动类型建议：推荐尝试的运动类型
  • 运动时间建议：最佳运动时间
  • 恢复建议：休息和恢复建议
建议依据：
  • WHO/ACSM/AHA运动指南
  • 用户运动历史数据
  • 用户健康状况
  • 用户健身目标
Generate personalized exercise recommendations based on user data.
Recommendation Types:
  • Exercise Frequency Suggestions: Whether to increase/decrease exercise frequency
  • Exercise Intensity Suggestions: Intensity adjustment recommendations
  • Exercise Type Suggestions: Recommended exercise types to try
  • Exercise Time Suggestions: Optimal exercise time
  • Recovery Suggestions: Rest and recovery recommendations
Recommendation Basis:
  • WHO/ACSM/AHA exercise guidelines
  • User's exercise history data
  • User's health status
  • User's fitness goals

输出格式

Output Formats

趋势分析报告

Trend Analysis Report

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运动趋势分析报告

Exercise Trend Analysis Report

分析周期

Analysis Period

2025-03-20 至 2025-06-20（3个月）
2025-03-20 to 2025-06-20 (3 months)

运动量趋势

Exercise Volume Trends

运动时长

Exercise Duration

  • 趋势：⬆️ 上升
  • 开始：平均120分钟/周
  • 当前：平均180分钟/周
  • 变化：+50%（+60分钟/周）
  • 解读：运动量显著增加，表现优秀
  • Trend: ⬆️ Increasing
  • Starting: Average 120 minutes/week
  • Current: Average 180 minutes/week
  • Change: +50% (+60 minutes/week)
  • Interpretation: Exercise volume has increased significantly, excellent performance

卡路里消耗

Calorie Burn

  • 趋势：⬆️ 上升
  • 开始：平均960卡/周
  • 当前：平均1440卡/周
  • 变化：+50%
  • 解读：运动消耗增加，有助于体重管理
  • Trend: ⬆️ Increasing
  • Starting: Average 960 calories/week
  • Current: Average 1440 calories/week
  • Change: +50%
  • Interpretation: Exercise consumption has increased, which is beneficial for weight management

运动距离

Exercise Distance

  • 趋势：⬆️ 上升
  • 开始：平均10公里/周
  • 当前：平均20公里/周
  • 变化：+100%
  • 解读：耐力显著提升
  • Trend: ⬆️ Increasing
  • Starting: Average 10 km/week
  • Current: Average 20 km/week
  • Change: +100%
  • Interpretation: Endurance has improved significantly

运动频率

Exercise Frequency

  • 当前频率：4天/周
  • 目标频率：4-5天/周
  • 状态：✅ 达标
  • 建议：保持当前频率
  • Current frequency: 4 days/week
  • Target frequency: 4-5 days/week
  • Status: ✅ Meeting standard
  • Suggestion: Maintain current frequency

强度分布

Intensity Distribution

强度占比变化
低强度25%+5%
中等强度55%-10%
高强度20%+5%
分析：强度分布合理，中等强度占主导，符合有氧运动建议。
IntensityProportionChange
Low intensity25%+5%
Medium intensity55%-10%
High intensity20%+5%
Analysis: The intensity distribution is reasonable, with medium intensity as the main focus, which is in line with aerobic exercise recommendations.

运动类型分布

Exercise Type Distribution

运动类型占比
跑步50%
瑜伽25%
力量训练25%
建议：可以适当增加力量训练比例至30-40%。
Exercise TypeProportion
Running50%
Yoga25%
Strength Training25%
Suggestion: Appropriately increase the proportion of strength training to 30-40%.

洞察与建议

Insights and Suggestions

优势

Strengths

  1. ✅ 运动量稳定增长，(+50%)
  2. ✅ 运动频率稳定，每周4天
  3. ✅ 休息日充足，恢复良好
  1. ✅ Exercise volume has grown steadily (+50%)
  2. ✅ Exercise frequency is stable, 4 days per week
  3. ✅ Sufficient rest days, good recovery

改进建议

Improvement Suggestions

  1. 📈 每周增加2次力量训练
  2. 📈 尝试不同运动类型避免单调
  3. 📈 适当增加高强度间歇训练(HIIT)
  1. 📈 Add 2 strength training sessions per week
  2. 📈 Try different exercise types to avoid monotony
  3. 📈 Appropriately increase high-intensity interval training (HIIT)

警示

Warnings

  1. ⚠️ 注意运动强度不宜过高，控制在中等强度为主
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  1. ⚠️ Avoid excessive exercise intensity, focus on medium intensity as the mainstay
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相关性分析报告

Correlation Analysis Report

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运动与血压相关性分析

Correlation Analysis between Exercise and Blood Pressure

数据来源

Data Sources

  • 运动数据：fitness-logs (2025-03-20 至 2025-06-20)
  • 血压数据：hypertension-tracker (同期)
  • Exercise data: fitness-logs (2025-03-20 to 2025-06-20)
  • Blood pressure data: hypertension-tracker (same period)

分析结果

Analysis Results

相关系数

Correlation Coefficient

  • 变量：每周运动时长 ↔ 收缩压
  • 相关系数：r = -0.68
  • 相关性强度：强负相关
  • 统计显著性：p < 0.01 高度显著
  • Variables: Weekly exercise duration ↔ Systolic blood pressure
  • Correlation coefficient: r = -0.68
  • Correlation strength: Strong negative correlation
  • Statistical significance: p < 0.01 Highly significant

解读

Interpretation

运动时长与收缩压呈强负相关，意味着：
  • 运动越多，血压越低
  • 每增加30分钟运动，收缩压平均下降3-5 mmHg
There is a strong negative correlation between exercise duration and systolic blood pressure, which means:
  • The more exercise, the lower the blood pressure
  • For every additional 30 minutes of exercise, systolic blood pressure decreases by an average of 3-5 mmHg

实践建议

Practical Suggestions

  1. ✅ 继续保持规律运动，每周5-7天
  2. ✅ 每次运动30-60分钟，中等强度
  3. ✅ 优先选择有氧运动（快走、慢跑、骑行）
  4. ⚠️ 避免憋气动作和突然爆发性运动
  1. ✅ Continue regular exercise, 5-7 days per week
  2. ✅ Exercise for 30-60 minutes each time, medium intensity
  3. ✅ Prioritize aerobic exercise (brisk walking, jogging, cycling)
  4. ⚠️ Avoid breath-holding movements and sudden explosive exercises

医学参考

Medical Reference

  • AHA声明：规律有氧运动可降低收缩压5-7 mmHg
  • 您的运动效果：降低约10 mmHg，效果显著！
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  • AHA statement: Regular aerobic exercise can reduce systolic blood pressure by 5-7 mmHg
  • Your exercise effect: Reduced by approximately 10 mmHg, significant effect!
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进步追踪报告

Progress Tracking Report

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跑步进步追踪

Running Progress Tracking

分析周期

Analysis Period

2025-01-01 至 2025-06-20（6个月）
2025-01-01 to 2025-06-20 (6 months)

配速进步

Pace Progress

指标开始当前改善
平均配速7:30 min/km6:00 min/km+20% ⬆️
最快配速7:00 min/km5:30 min/km+22% ⬆️
5公里用时37:3030:00+20% ⬆️
趋势：配速持续稳定提升，进步显著！
IndicatorStartingCurrentImprovement
Average pace7:30 min/km6:00 min/km+20% ⬆️
Fastest pace7:00 min/km5:30 min/km+22% ⬆️
5km time37:3030:00+20% ⬆️
Trend: Pace has continued to improve steadily, significant progress!

距离进步

Distance Progress

指标开始当前改善
最长单次距离3 km12 km+300% ⬆️
月度总距离40 km86 km+115% ⬆️
平均距离5 km6 km+20% ⬆️
趋势：耐力大幅提升，可以完成更长距离。
IndicatorStartingCurrentImprovement
Longest single distance3 km12 km+300% ⬆️
Monthly total distance40 km86 km+115% ⬆️
Average distance5 km6 km+20% ⬆️
Trend: Endurance has improved significantly, able to complete longer distances.

心率改善

Heart Rate Improvement

指标开始当前改善
静息心率78 bpm72 bpm-6 bpm ⬇️
相同配速心率155 bpm145 bpm-10 bpm ⬇️
分析：心肺功能显著改善，相同配速下心率降低。
IndicatorStartingCurrentImprovement
Resting heart rate78 bpm72 bpm-6 bpm ⬇️
Heart rate at same pace155 bpm145 bpm-10 bpm ⬇️
Analysis: Cardiopulmonary function has improved significantly, with lower heart rate at the same pace.

里程碑

Milestones

  • ✅ 2025-03-15：首次完成5公里跑
  • ✅ 2025-05-20：首次完成10公里跑
  • ✅ 2025-06-10：配速突破6:00 min/km
  • ✅ 2025-03-15: Completed 5km run for the first time
  • ✅ 2025-05-20: Completed 10km run for the first time
  • ✅ 2025-06-10: Pace broke through 6:00 min/km

下一步目标

Next Goals

  • 🎯 完成半程马拉松（21公里）
  • 🎯 配速提升至5:30 min/km
  • 🎯 尝试间歇训练提升速度
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  • 🎯 Complete a half marathon (21 km)
  • 🎯 Improve pace to 5:30 min/km
  • 🎯 Try interval training to improve speed
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数据源

Data Sources

主要数据源

Main Data Sources

  1. 运动日志
    • 路径：
      data/fitness-logs/YYYY-MM/YYYY-MM-DD.json
    • 内容：运动记录（类型、时长、强度、心率、距离等）
    • 频率：每次运动后更新
  2. 用户档案
    • 路径：
      data/fitness-tracker.json
    • 内容：用户档案、健身目标、统计数据
    • 更新：定期更新
  3. 健康数据关联
    • data/hypertension-tracker.json
      （血压数据）
    • data/diabetes-tracker.json
      （血糖数据）
    • data/profile.json
      （体重、BMI等）
  1. Fitness Logs
    • Path: 
      data/fitness-logs/YYYY-MM/YYYY-MM-DD.json
    • Content: Exercise records (type, duration, intensity, heart rate, distance, etc.)
    • Frequency: Updated after each exercise
  2. User Profile
    • Path: 
      data/fitness-tracker.json
    • Content: User profile, fitness goals, statistical data
    • Update: Regularly updated
  3. Health Data Correlation
    • data/hypertension-tracker.json
      (blood pressure data)
    • data/diabetes-tracker.json
      (blood glucose data)
    • data/profile.json
      (weight, BMI, etc.)

数据质量检查

Data Quality Check

  • 数据完整性：检查必要字段是否存在
  • 数据合理性：检查数值是否在合理范围内
  • 时间一致性：检查时间戳是否合理
  • 重复数据：检测并处理重复记录
  • Data completeness: Check if necessary fields exist
  • Data rationality: Check if values are within reasonable ranges
  • Time consistency: Check if timestamps are reasonable
  • Duplicate data: Detect and handle duplicate records

算法说明

Algorithm Description

1. 线性回归趋势分析

1. Linear Regression Trend Analysis

使用线性回归分析运动数据的时间趋势。
公式： y = a + bx
其中：
  • y：运动指标（时长、卡路里、距离等）
  • x：时间
  • a：截距
  • b：斜率（趋势方向和速度）
解释：
  • b > 0：上升趋势
  • b < 0：下降趋势
  • b ≈ 0：稳定
Use linear regression to analyze the time trend of exercise data.
Formula: y = a + bx
Where:
  • y: Exercise indicator (duration, calories, distance, etc.)
  • x: Time
  • a: Intercept
  • b: Slope (trend direction and speed)
Interpretation:
  • b > 0: Increasing trend
  • b < 0: Decreasing trend
  • b ≈ 0: Stable

2. Pearson相关系数

2. Pearson Correlation Coefficient

用于分析两个变量之间的线性相关性。
公式： r = Σ[(xi - x̄)(yi - ȳ)] / √[Σ(xi - x̄)² × Σ(yi - ȳ)²]
范围：-1 ≤ r ≤ 1
解释：
  • r = 1：完全正相关
  • r = -1：完全负相关
  • r = 0：无线性相关
强度判断：
  • |r| < 0.3：弱相关
  • 0.3 ≤ |r| < 0.7：中等相关
  • |r| ≥ 0.7：强相关
Used to analyze the linear correlation between two variables.
Formula: r = Σ[(xi - x̄)(yi - ȳ)] / √[Σ(xi - x̄)² × Σ(yi - ȳ)²]
Range: -1 ≤ r ≤ 1
Interpretation:
  • r = 1: Perfect positive correlation
  • r = -1: Perfect negative correlation
  • r = 0: No linear correlation
Intensity Judgment:
  • |r| < 0.3: Weak correlation
  • 0.3 ≤ |r| < 0.7: Medium correlation
  • |r| ≥ 0.7: Strong correlation

3. 配速计算

3. Pace Calculation

配速 = 运动时长 / 距离
单位：min/km 或 min/mile
示例：
  • 30分钟跑5公里
  • 配速 = 30 / 5 = 6 min/km
Pace = Exercise duration / Distance
Unit: min/km or min/mile
Example:
  • Run 5 km in 30 minutes
  • Pace = 30 / 5 = 6 min/km

4. MET能量代谢计算

4. MET Metabolic Equivalent Calculation

卡路里消耗 = MET × 体重(kg) × 时间(小时)
常见运动的MET值：
  • 走路（3-5 km/h）：3.5-5 MET
  • 慢跑（8 km/h）：8 MET
  • 快跑（10 km/h）：10 MET
  • 游泳：6-10 MET
  • 骑行（休闲）：4 MET
  • 力量训练：5 MET
  • 瑜伽：3 MET
Calorie Burn = MET × Weight(kg) × Time(hours)
MET Values for Common Exercises:
  • Walking (3-5 km/h): 3.5-5 MET
  • Jogging (8 km/h): 8 MET
  • Running (10 km/h): 10 MET
  • Swimming: 6-10 MET
  • Cycling (leisure): 4 MET
  • Strength Training: 5 MET
  • Yoga: 3 MET

医学安全边界

Medical Safety Boundaries

⚠️ 重要声明 本分析仅供健康参考，不构成医疗建议。
⚠️ Important Notice This analysis is for health reference only and does not constitute medical advice.

分析能力范围

Scope of Analysis Capabilities

✅ 能做到：
  • 运动数据统计和分析
  • 趋势识别和可视化
  • 相关性计算和解释
  • 一般性运动建议
❌ 不做到：
  • 疾病诊断
  • 运动风险评估
  • 具体运动处方设计
  • 运动损伤诊断和治疗
✅ Can Do:
  • Statistics and analysis of exercise data
  • Trend identification and visualization
  • Correlation calculation and interpretation
  • General exercise recommendations
❌ Cannot Do:
  • Disease diagnosis
  • Exercise risk assessment
  • Design of specific exercise prescriptions
  • Diagnosis and treatment of exercise injuries

危险信号检测

Warning Signal Detection

在分析过程中检测以下危险信号：
  1. 心率异常
    • 运动心率 > 95%最大心率
    • 静息心率 > 100 bpm
  2. 血压异常
    • 收缩压 ≥ 180 mmHg
    • 舒张压 ≥ 110 mmHg
  3. 过度训练迹象
    • 连续7天高强度运动
    • 运动感受持续下降（RPE > 17）
  4. 体重快速下降
    • 每周减重 > 1kg（可能不健康）
Detect the following warning signals during analysis:
  1. Abnormal Heart Rate
    • Exercise heart rate > 95% of maximum heart rate
    • Resting heart rate > 100 bpm
  2. Abnormal Blood Pressure
    • Systolic blood pressure ≥ 180 mmHg
    • Diastolic blood pressure ≥ 110 mmHg
  3. Signs of Overtraining
    • 7 consecutive days of high-intensity exercise
    • Continuous decline in exercise feeling (RPE > 17)
  4. Rapid Weight Loss
    • Weight loss > 1kg per week (may be unhealthy)

建议分级

Recommendation Levels

Level 1: 一般性建议
  • 基于WHO/ACSM指南
  • 适用于一般人群
Level 2: 参考性建议
  • 基于用户数据
  • 需结合个人情况
Level 3: 医疗建议
  • 涉及疾病管理
  • 需医生确认
Level 1: General Recommendations
  • Based on WHO/ACSM guidelines
  • Suitable for general population
Level 2: Reference Recommendations
  • Based on user data
  • Need to be combined with personal situation
Level 3: Medical Recommendations
  • Involves disease management
  • Need doctor's confirmation

使用示例

Usage Examples

示例1：生成运动趋势报告

Example 1: Generate Exercise Trend Report

bash
/fitness trend 3months
输出：
  • 3个月运动趋势分析
  • 运动量、频率、强度变化
  • 洞察和建议
bash
/fitness trend 3months
Output:
  • 3-month exercise trend analysis
  • Changes in exercise volume, frequency, and intensity
  • Insights and suggestions

示例2：追踪跑步进步

Example 2: Track Running Progress

bash
/fitness analysis progress running
输出：
  • 配速进步
  • 距离进步
  • 心率改善
  • 里程碑达成
bash
/fitness analysis progress running
Output:
  • Pace improvement
  • Distance improvement
  • Heart rate improvement
  • Milestones achieved

示例3：分析运动与血压相关性

Example 3: Analyze Correlation between Exercise and Blood Pressure

bash
/fitness analysis correlation blood_pressure
输出：
  • 相关系数
  • 相关性强度
  • 显著性检验
  • 实践建议
技能版本: v1.0 最后更新: 2026-01-02 维护者: WellAlly Tech
bash
/fitness analysis correlation blood_pressure
Output:
  • Correlation coefficient
  • Correlation strength
  • Significance test
  • Practical suggestions
Skill Version: v1.0 Last Updated: 2026-01-02 Maintainer: WellAlly Tech