# USAD: End-to-End Human Activity Recognition via Diffusion Model with Spatiotemporal Attention

arXiv: https://arxiv.org/abs/2507.02827 · HTML: https://mlsanity.com/paper/2507.02827/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Video generation, one of 14 directions in Diffusion models.
- Builds on 7 of its 30 references that are in the map.
- First posted on arXiv 3 Jul 2025.

## Placed in

- Diffusion models / [Video generation](https://mlsanity.com/diffusion/video-generation/)

## Builds on (most cited first, 7 of 7)

- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [Res2Net: A New Multi-scale Backbone Architecture](https://mlsanity.com/paper/1904.01169/) (2019)
- [Diverse Intra- and Inter-Domain Activity Style Fusion for Cross-Person Generalization in Activity Recognition](https://mlsanity.com/paper/2406.04609/) (2024)
- [Unsupervised Statistical Feature-Guided Diffusion Model for Sensor-based Human Activity Recognition](https://mlsanity.com/paper/2306.05285/) (2023)
- [Gated Res2Net for Multivariate Time Series Analysis](https://mlsanity.com/paper/2009.11705/) (2020)
- [Deep Generative Domain Adaptation with Temporal Attention for Cross-User Activity Recognition](https://mlsanity.com/paper/2403.17958/) (2024)
- [In-sensor 24 classes HAR under 850 Bytes](https://mlsanity.com/paper/2502.17472/) (2025)

## Abstract (arXiv)

The primary objective of human activity recognition (HAR) is to infer ongoing human actions from sensor data, a task that finds broad applications in health monitoring, safety protection, and sports analysis. Despite proliferating research, HAR still faces key challenges, including the scarcity of labeled samples for rare activities, insufficient extraction of high-level features, and suboptimal model performance on lightweight devices. To address these issues, this paper proposes a comprehensive optimization approach centered on multi-attention interaction mechanisms. First, an unsupervised, statistics-guided diffusion model is employed to perform data augmentation, thereby alleviating the problems of labeled data scarcity and severe class imbalance. Second, a multi-branch spatio-temporal interaction network is designed, which captures multi-scale features of sequential data through parallel residual branches with 3*3, 5*5, and 7*7 convolutional kernels. Simultaneously, temporal attention mechanisms are incorporated to identify critical time points, while spatial attention enhances inter-sensor interactions. A cross-branch feature fusion unit is further introduced to improve the overall feature representation capability. Finally, an adaptive multi-loss function fusion strategy is integrated, allowing for dynamic adjustment of loss weights and overall model optimization. Experimental results on three public datasets, WISDM, PAMAP2, and OPPORTUNITY, demonstrate that the proposed unsupervised data augmentation spatio-temporal attention diffusion network (USAD) achieves accuracies of 98.84%, 93.81%, and 80.92% respectively, significantly outperforming existing approaches. Furthermore, practical deployment on embedded devices verifies the efficiency and feasibility of the proposed method.