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Efficient human activity recognition with spatio-temporal spiking neural networks.

Yuhang Li1, Ruokai Yin1, Youngeun Kim1

  • 1Department of Electrical Engineering, Yale University, New Haven, CT, United States.

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|October 2, 2023
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Summary

Spiking Neural Networks (SNNs) offer a low-power solution for Human Activity Recognition (HAR) using wearable sensors. This approach achieves competitive performance while significantly reducing energy consumption compared to traditional Artificial Neural Networks (ANNs).

Keywords:
brain-inspired computinghardware efficiencyhuman activity recognitionneuromorphic computingspiking neural networks

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Biomedical Engineering

Background:

  • Human Activity Recognition (HAR) is crucial for health applications, typically using wearable sensor data.
  • Current Artificial Neural Networks (ANNs) for HAR face challenges with high computational load and limited temporal feature extraction.
  • Existing ANNs' activation functions hinder efficient processing on resource-constrained wearable devices.

Purpose of the Study:

  • To investigate the efficacy of Spiking Neural Networks (SNNs) for Human Activity Recognition (HAR).
  • To address the computational and temporal feature extraction limitations of ANNs in wearable HAR systems.
  • To evaluate SNNs' potential for low-power, high-performance activity recognition.

Main Methods:

  • Proposed the application of Spiking Neural Networks (SNNs), inspired by biological neurons, for HAR tasks.
  • Utilized SNNs' ability to accumulate input activation and generate binary spikes for spatio-temporal feature extraction.
  • Conducted experiments on three distinct HAR datasets to compare SNN performance against ANNs.

Main Results:

  • SNNs demonstrated competitive or superior performance compared to traditional ANNs in HAR tasks.
  • Achieved significant energy consumption reduction, up to 94%, by leveraging binary spikes for computation.
  • SNNs effectively addressed limitations in temporal feature extraction present in conventional ANN approaches.

Conclusions:

  • Spiking Neural Networks (SNNs) present a viable and efficient alternative for Human Activity Recognition (HAR) on wearable devices.
  • The proposed SNN approach offers substantial energy savings without compromising recognition accuracy.
  • SNNs provide a promising direction for developing next-generation, low-power wearable health monitoring systems.