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PIM (Proportional Integration Mode) Algorithm

Overview

The PIM (Proportional Integration Mode) Algorithm processes 3-axis IMU (Inertial Measurement Unit) data to extract activity metrics. It computes the absolute values of acceleration on each axis and aggregates them over time windows.

Algorithm Name: PIMAlgorithm
Version: v0.1.0

Algorithm Description

PIM is a signal processing technique that:

  1. Computes absolute acceleration values for X, Y, and Z axes
  2. Aggregates data over time windows using the specified method
  3. Provides multi-axis activity representation

This approach preserves directional information while quantifying movement intensity across all three spatial dimensions.

Parameters

  • Aggregation Window: Default 5 seconds
  • Aggregation Method: Sum (default) or other statistical measures (mean, max, etc.)

Usage Example

from physiodsp.activity.pim import PIMAlgorithm
from physiodsp.sensors.imu.base import IMUData
import numpy as np

# Create sample IMU data
timestamps = np.arange(0, 100, 0.01)  # 100 seconds at 100 Hz
x = np.random.normal(0, 0.2, len(timestamps))
y = np.random.normal(0, 0.2, len(timestamps))
z = np.ones(len(timestamps)) + np.random.normal(0, 0.2, len(timestamps))

imu_data = IMUData(
    timestamps=timestamps,
    x=x,
    y=y,
    z=z,
    fs=100  # 100 Hz sampling frequency
)

# Initialize PIM algorithm
pim = PIMAlgorithm()

# Estimate activity
result = pim.estimate(imu_data)

# Aggregate results
result.aggregate(method='sum')

# Get aggregated results
print(result.biomarker_agg)

Output

The algorithm returns a Pandas DataFrame with:

  • timestamps_unix: Aggregated time windows (unix timestamps)
  • x: Aggregated absolute acceleration on X-axis
  • y: Aggregated absolute acceleration on Y-axis
  • z: Aggregated absolute acceleration on Z-axis

Applications

  • Multi-axis activity monitoring
  • Movement magnitude assessment
  • Directional activity analysis
  • Device orientation-independent activity tracking

Advantages

  • Preserves directional information
  • Resistant to device orientation changes
  • Suitable for complex movement patterns
  • Useful for rehabilitation and sports analytics

References

  • Proportional Integration algorithms in signal processing
  • Multi-axis accelerometer data analysis