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Validation of Inertial Sensor-Based Step Detection Algorithms for Edge Device Deployment
Maksymilian Kisiel1, Arslan Amjad1, Agnieszka Szczęsna1
1Department of Computer Graphics, Vision and Digital Systems, Faculty of Automatic Control, Electronics and Computer Science, Silesian University of Technology, 44-100 Gliwice, Poland.
Sensors (Basel, Switzerland)
|February 13, 2026
Summary
This study compared five step detection algorithms using inertial measurement units (IMUs). Peak Detection performed best overall, while Spectral Analysis excelled on stairs, offering insights for edge computing and activity monitoring.
Area of Science:
- Wearable Technology
- Biomedical Engineering
- Signal Processing
Background:
- Step detection using inertial measurement units (IMUs) is crucial for human activity recognition, indoor navigation, and health monitoring.
- Edge device implementation requires efficient and accurate step detection algorithms.
Purpose of the Study:
- To validate and compare five distinct step detection algorithms for edge device suitability.
- To assess algorithm performance across various activities and sensor placements.
Main Methods:
- Developed a Raspberry Pi Pico 2W system with two IMU sensors.
- Implemented and tested Peak Detection, Zero-Crossing, Spectral Analysis, Adaptive Threshold, and SHOE algorithms.
- Validated across seven scenarios (e.g., walking, jogging, stairs) and four sensor locations (thigh, ankle, wrist, upper arm).
Main Results:
- Peak Detection achieved the highest overall F1-score (0.82).
- Spectral Analysis showed superior performance in stair climbing scenarios (F1 = 0.86-0.92).
- Upper arm sensor placement unexpectedly yielded the highest accuracy (F1 = 0.84).
Conclusions:
- Peak Detection is recommended for general step detection on edge devices.
- Algorithm choice should consider specific activities, especially stair climbing.
- Further research is needed to address algorithm limitations in non-continuous gait scenarios.
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