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The Smart Inertial Device Data from Human Activities dataset.

Riccardo Pignari1, Bendetto Leto1, Stefano Quer1

  • 1Politecnico di Torino, Turin, 10129, Italy.

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|April 9, 2026
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Summary
This summary is machine-generated.

A new dataset, Smart Inertial Device Data from Human Activities (SIDDHA), enhances human activity recognition (HAR) by providing uniformly sampled and realigned data. This dataset improves HAR accuracy with recurrent neural networks.

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Area of Science:

  • Human Activity Recognition (HAR)
  • Wearable Sensor Data Analysis
  • Neuromorphic Computing

Background:

  • Existing human activity recognition (HAR) datasets, such as the WISDM Lab dataset, suffer from issues like non-uniform sampling, missing data, and sensor misalignment.
  • These limitations hinder the performance and reliability of HAR systems, particularly those utilizing data from smart devices with inertial measurement units.

Purpose of the Study:

  • To introduce the Smart Inertial Device Data from Human Activities (SIDDHA) dataset, a meticulously reconstructed and enhanced version of existing HAR datasets.
  • To address data quality issues and incorporate novel spike-encoded inertial data tailored for spiking neural networks and neuromorphic computing applications.

Main Methods:

  • Reconstruction of an existing HAR dataset using a two-phase characterization process.
  • Application of spline interpolation for resampling and filtering to achieve uniform sampling and sensor realignment.
  • Generation of spike-encoded inertial data using eleven distinct encoding techniques for neuromorphic applications.

Main Results:

  • Technical validation confirmed the enhanced data quality of the SIDDHA dataset.
  • Experimental results demonstrated improved HAR performance using SIDDHA's raw data with recurrent neural network architectures like Legendre memory units and Long Short-Term Memory (LSTM).
  • The inclusion of spike-encoded data shows potential for advanced applications in spiking neural networks and neuromorphic computing.

Conclusions:

  • The SIDDHA dataset offers a high-quality, uniformly sampled, and realigned resource for advancing human activity recognition research.
  • SIDDHA's unique spike-encoded data facilitates novel research at the intersection of HAR, spiking neural networks, and neuromorphic computing.
  • The dataset enables improved accuracy in HAR tasks, particularly when analyzed with advanced recurrent architectures.