📦 Hardware Requirements (Sensor Configuration)

• Accelerometer: The core sensor that measures changes in the speed and direction of punches. A minimum of 3-axis (X, Y, Z) measurement is required, with a high sampling rate (≥ 100Hz).
• Gyroscope: Detects rotational movement to determine angular velocity and punch trajectory.
• Magnetometer: Corrects punch direction based on absolute orientation.
• Pressure Sensor: Measures the impact force directly at the moment of contact.

🛡️ Physical Durability

• High Impact Resistance: Capable of withstanding accelerations of at least 20G.
• Water and Dust Resistance: Minimum IPX7 rating.
• Lightweight Design: Less than 20g to minimize movement interference.
• Flexible Materials: Does not restrict wrist or finger movement.

🧠 Measurement Principles
Punch Detection Algorithm

Punches follow a characteristic acceleration pattern:

• Preparation phase: Low acceleration.
• Acceleration phase: Rapid increase in acceleration.
• Impact moment: Peak acceleration.
• Deceleration phase: Sharp decline.

The system analyzes these patterns in real time to detect punches.

Speed Calculation

• Velocity is computed by integrating acceleration over time:
    v(t) = ∫a(t)dt
• Kalman filters or low-pass filters are applied to reduce noise.

Power Estimation

• Punch power is calculated using:
    P = F × v (force × velocity)
• It is estimated based on peak acceleration and velocity at impact, incorporating personal parameters such as body weight and arm length.

🧩 Core Algorithms
Punch Type Classification
Machine learning-based classification algorithms are used.
Each punch type (jab, cross, hook, uppercut) has distinct acceleration and angular velocity patterns.
Common algorithms include:

• SVM (Support Vector Machine)
• Random Forest

Noise Filtering

• Butterworth Filter: Removes high-frequency noise.\
• High-Pass Filter: Removes gravity components.
• Adaptive Filter: Compensates for sensor drift.

Real-Time Processing

• To minimize latency, a sliding window approach is used.
• Typically, punches are detected and analyzed within a 100–200ms window.

🔄 Data Processing Pipeline

1. Preprocessing

• Noise removal from raw sensor data.
• Sensor fusion (e.g., accelerometer + gyroscope).
• Coordinate system correction.

2. Feature Extraction

• Maximum acceleration.
• Punch duration.
• Frequency spectrum.
• Motion trajectory, etc.

3. Pattern Recognition

• Pre-trained models classify punch type, strength, and accuracy.

4. Postprocessing

• Analyze relations between successive punches.
• Outlier removal and statistical correction.

📈 Accuracy Enhancement Techniques

Personal Calibration
Customized models are built based on the user’s punch style and body type.
Baseline values are set through initial training sessions.

Multi-Sensor Fusion
Combining data from multiple sensors reduces measurement errors.
Bayesian inference or extended Kalman filters are typically used.

Context Awareness
Different training environments (e.g., heavy bag, mitts, sparring) are recognized,
with algorithms optimized accordingly.

⚠️ Technical Challenges & Solutions
Position Drift
To counteract cumulative error from acceleration integration,
Zero Velocity Updates (ZUPT) or Inertial Navigation Corrections are applied.

Impact Detection Difficulty
To distinguish between real impacts, glancing blows, or air punches,
acoustic sensors or vibration pattern analysis can be integrated.

Battery Optimization
To reduce power consumption from high-performance sensors:

• Implement adaptive sampling.
• Enable low-power operation modes.

These technical components work together to deliver a precise and reliable boxing tracking wearable experience.