校園體育

In campus sports, FitMao heart rate systems and body composition analyzers are driving a shift from experience-based teaching to data-driven, scientific instruction. They improve safety and efficiency while supporting comprehensive student fitness management.

I. FitMao Heart Rate Training System: Real-time Optimization and Safety

1. Precise intensity control

Dynamic load monitoring: Real-time heart rate feedback helps teachers segment intensity zones (warm-up, fat-burning, aerobic/anaerobic) to match student fitness. For middle/long-distance training, keeping HR at 75%–90% HRmax targets aerobic or mixed aerobic capacity.
Avoid overtraining: Persistent high HR raises risk (e.g., SCD). Set alert thresholds (e.g., > 180 bpm) to prompt intervention.

2. Safety protection

Health screening: Resting HR and recovery tests identify potential cardiovascular issues. Slow recovery (> 3 minutes) suggests abnormalities and need for medical evaluation.
Emergency warnings: Supports monitoring for up to 60 students simultaneously; teachers view HR in real time and receive automatic alerts for abrupt spikes/drops.

3. Evaluation and personalization

Quantify outcomes: Long-term HR tracking (e.g., lower HR at same workload) evaluates cardiovascular improvement and informs curriculum design.
Tiered instruction: Group students by HR recovery or VO₂max estimate. Weaker students focus on low-intensity aerobic; stronger ones do high-intensity intervals.

Bluetooth armband, swimming armband, and 4G watch versions support near-range, pool, and long-range transmission to fit classrooms, tracks, gyms, pools, and outdoors.

Bluetooth armband

Swimming armband

4G HR watch

II. FitMao Body Composition Analyzer: Foundation for Assessment and Growth Monitoring

1. Comprehensive physique assessment

Composition analysis: BIA measures body fat %, muscle mass, and water ratio, identifying "hidden obesity" or malnutrition, overcoming BMI limitations.

2. Scientific nutrition and training guidance

Fat loss vs. muscle gain: Differentiate muscular overweight from adipose overweight; design low-fat, high-protein diets for fat loss and resistance programs for muscle gain.
Growth monitoring: Track trends in muscle and fat; combine with bone age to assess development and avoid early specialization imbalances.

3. Talent selection and long-term tracking

Talent selection: Use muscle distribution and BMR to identify endurance vs. power potential.
Health records: Store annual composition data to build a student fitness database for policy-making (e.g., lunch standards).

III. Multi-device Data Collaboration

Cloud platform: Integrate HR and composition data to analyze "HR–body fat change" and generate personalized prescriptions.
Case: After finding low muscle mass, adjust HR zone to muscular endurance (65%–75% HRmax) and increase strength sessions.
AI models: Predict injury risk or performance potential from historical data.

Together, FitMao HR systems and analyzers form a "dual-drive" for campus sports: dynamic control for safe, efficient training and static assessment for long-term development. This reduces risk, improves instruction, and builds health management awareness.

Real-world scenarios

Showcase

Campus Sports