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This work presents a TinyML-based approach for classifying daily motion patterns and detecting pre-fall activity in older adults using inertial sensor data. A multilayer perceptron neural network was trained on six input signals (3-axis accelerometer and gyroscope) from the SisFall dataset. The model was optimized for edge deployment using quantization and pruning, achieving a global accuracy of 85.5% across 15 activity classes. The final model was converted to C for integration into microcontrollers. Results show high performance with minimal latency, enabling real-time fall prevention strategies in embedded health monitoring systems.

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TinyML Approach for Pre-fall Motion Pattern Detection in Older Adults

  • 会议论文

  • 发表日期：

    2026年

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  • Jefferson Sarmiento-Rojas,
  • Angela Maria Torres-Lara,
  • Pedro Antonio-Aya Parra,
  • Jonnier Sebastián Jaramillo-Isaza,
  • Oscar Julian Perdomo

摘要

This work presents a TinyML-based approach for classifying daily motion patterns and detecting pre-fall activity in older adults using inertial sensor data. A multilayer perceptron neural network was trained on six input signals (3-axis accelerometer and gyroscope) from the SisFall dataset. The model was optimized for edge deployment using quantization and pruning, achieving a global accuracy of 85.5% across 15 activity classes. The final model was converted to C for integration into microcontrollers. Results show high performance with minimal latency, enabling real-time fall prevention strategies in embedded health monitoring systems.