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<p>The proliferation of smart devices with inertial measurement units has driven human activity recognition (HAR) research for several years. However, existing datasets like the Smartphone and Smartwatch Activity and Biometrics Dataset (from the Wireless Sensor Data Mining Lab, i.e., the WISDM Lab) may suffer from non-uniform sampling, missing data, and sensor misalignment. To overcome the limitations mentioned above, we present the Smart Inertial Device Data from Human Activities (SIDDHA) dataset, which is a meticulous reconstruction of the previously described dataset. Our rigorous reconstruction employs a two-phase characterization followed by spline interpolation methods for resampling and filtering, yielding a uniformly sampled and realigned data. An additional key innovation of this process is to include spike-encoded inertial data, generated using eleven distinct encoding techniques. We specifically tailor this process for spiking neural networks and neuromorphic computing. Technical validation confirms SIDDHA’s enhanced quality. Experimental results demonstrate an improved HAR and better accuracy on SIDDHA’s raw data with recurrent architectures such as the Legendre memory unit, and the long short-term memory.</p>

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The Smart Inertial Device Data from Human Activities dataset

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年4月9日

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  • Riccardo Pignari,
  • Bendetto Leto,
  • Stefano Quer,
  • Enrico Macii,
  • Gianvito Urgese,
  • Vittorio Fra

摘要

The proliferation of smart devices with inertial measurement units has driven human activity recognition (HAR) research for several years. However, existing datasets like the Smartphone and Smartwatch Activity and Biometrics Dataset (from the Wireless Sensor Data Mining Lab, i.e., the WISDM Lab) may suffer from non-uniform sampling, missing data, and sensor misalignment. To overcome the limitations mentioned above, we present the Smart Inertial Device Data from Human Activities (SIDDHA) dataset, which is a meticulous reconstruction of the previously described dataset. Our rigorous reconstruction employs a two-phase characterization followed by spline interpolation methods for resampling and filtering, yielding a uniformly sampled and realigned data. An additional key innovation of this process is to include spike-encoded inertial data, generated using eleven distinct encoding techniques. We specifically tailor this process for spiking neural networks and neuromorphic computing. Technical validation confirms SIDDHA’s enhanced quality. Experimental results demonstrate an improved HAR and better accuracy on SIDDHA’s raw data with recurrent architectures such as the Legendre memory unit, and the long short-term memory.