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

arXiv: https://arxiv.org/abs/2212.02233 · HTML: https://mlsanity.com/paper/2212.02233/

## Key facts

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

## Builds on (most cited first, 15 of 15)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [XNOR-Net: ImageNet Classification Using Binary Convolutional Neural Networks](https://mlsanity.com/paper/1603.05279/) (2016)
- [Deep Learning in Spiking Neural Networks](https://mlsanity.com/paper/1804.08150/) (2018)
- [Temporal Efficient Training of Spiking Neural Network via Gradient Re-weighting](https://mlsanity.com/paper/2202.11946/) (2022)
- [Optimal Conversion of Conventional Artificial Neural Networks to Spiking Neural Networks](https://mlsanity.com/paper/2103.00476/) (2021)
- [A Free Lunch From ANN: Towards Efficient, Accurate Spiking Neural Networks Calibration](https://mlsanity.com/paper/2106.06984/) (2021)
- [Revisiting Batch Normalization for Training Low-latency Deep Spiking Neural Networks from Scratch](https://mlsanity.com/paper/2010.01729/) (2020)
- [UniMiB SHAR: a new dataset for human activity recognition using acceleration data from smartphones](https://mlsanity.com/paper/1611.07688/) (2016)
- [Neural Architecture Search for Spiking Neural Networks](https://mlsanity.com/paper/2201.10355/) (2022)
- [Exploring Lottery Ticket Hypothesis in Spiking Neural Networks](https://mlsanity.com/paper/2207.01382/) (2022)
- [SATA: Sparsity-Aware Training Accelerator for Spiking Neural Networks](https://mlsanity.com/paper/2204.05422/) (2022)
- [PrivateSNN: Privacy-Preserving Spiking Neural Networks](https://mlsanity.com/paper/2104.03414/) (2021)
- [Converting Artificial Neural Networks to Spiking Neural Networks via Parameter Calibration](https://mlsanity.com/paper/2205.10121/) (2022)

## Built on by (most cited first, 6 of 6)

- [Spiking PointNet: Spiking Neural Networks for Point Clouds](https://mlsanity.com/paper/2310.06232/) (2023)
- [Direct Learning-Based Deep Spiking Neural Networks: A Review](https://mlsanity.com/paper/2305.19725/) (2023)
- [Evaluating Spiking Neural Network On Neuromorphic Platform For Human Activity Recognition](https://mlsanity.com/paper/2308.00787/) (2023)
- [Integrating LLM, EEG, and Eye-Tracking Biomarker Analysis for Word-Level Neural State Classification in Semantic Inference Reading Comprehension](https://mlsanity.com/paper/2309.15714/) (2023)
- [Behavioral Sequence Modeling with Ensemble Learning](https://mlsanity.com/paper/2411.02174/) (2024)
- [ASecond-Order SpikingSSM for Wearables](https://mlsanity.com/paper/2510.14386/) (2025)

## 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