mlsanityData to 7 Oct 2026

Wearable-based Human Activity Recognition with Spatio-Temporal Spiking Neural Networks

arXiv 2212.02233 ↗First posted 14 Nov 2022cs.NE cs.LG eess.SPcited by 6

Not on the map. Why?

Abstract (arXiv)

We study the Human Activity Recognition (HAR) task, which predicts user daily activity based on time series data from wearable sensors. Recently, researchers use end-to-end Artificial Neural Networks (ANNs) to extract the features and perform classification in HAR. However, ANNs pose a huge computation burden on wearable devices and lack temporal feature extraction. In this work, we leverage Spiking Neural Networks (SNNs)--an architecture inspired by biological neurons--to HAR tasks. SNNs allow spatio-temporal extraction of features and enjoy low-power computation with binary spikes. We conduct extensive experiments on three HAR datasets with SNNs, demonstrating that SNNs are on par with ANNs in terms of accuracy while reducing up to 94% energy consumption. The code is publicly available in https://github.com/Intelligent-Computing-Lab-Yale/SNN_HAR

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 15 of its 34 references that are in the map. First posted on arXiv 14 Nov 2022.

Builds on

15 of 34 references.

References · 15

  1. 2017Attention Is All You Needcited by 59,932
  2. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  3. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,160
  4. 2016XNOR-Net: ImageNet Classification Using Binary Convolutional Neural Networkscited by 829
  5. 2018Deep Learning in Spiking Neural Networkscited by 258
  6. 2022Temporal Efficient Training of Spiking Neural Network via Gradient Re-weightingcited by 192
  7. 2021Optimal Conversion of Conventional Artificial Neural Networks to Spiking Neural Networkscited by 134
  8. 2021A Free Lunch From ANN: Towards Efficient, Accurate Spiking Neural Networks Calibrationcited by 111
  9. 2020Revisiting Batch Normalization for Training Low-latency Deep Spiking Neural Networks from Scratchcited by 76
  10. 2016UniMiB SHAR: a new dataset for human activity recognition using acceleration data from smartphonescited by 54
  11. 2022Neural Architecture Search for Spiking Neural Networkscited by 52
  12. 2022Exploring Lottery Ticket Hypothesis in Spiking Neural Networkscited by 25
3 more
  1. 2022SATA: Sparsity-Aware Training Accelerator for Spiking Neural Networkscited by 18
  2. 2021PrivateSNN: Privacy-Preserving Spiking Neural Networkscited by 15
  3. 2022Converting Artificial Neural Networks to Spiking Neural Networks via Parameter Calibrationcited by 6

Built on by

Cited by year

Citing papers per year: 2023: 4; 2024: 1; 2025: 1.

6 papers.

  1. 2023Spiking PointNet: Spiking Neural Networks for Point Cloudscited by 34
  2. 2023Direct Learning-Based Deep Spiking Neural Networks: A Reviewcited by 31
  3. 2023Evaluating Spiking Neural Network On Neuromorphic Platform For Human Activity Recognitioncited by 2
  4. 2023Integrating LLM, EEG, and Eye-Tracking Biomarker Analysis for Word-Level Neural State Classification in Semantic Inference Reading Comprehensioncited by 2
  5. 2024Behavioral Sequence Modeling with Ensemble Learningcited by 0
  6. 2025ASecond-Order SpikingSSM for Wearablescited by 0