# DanHAR: Dual Attention Network For Multimodal Human Activity Recognition Using Wearable Sensors

arXiv: https://arxiv.org/abs/2006.14435 · HTML: https://mlsanity.com/paper/2006.14435/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Jun 2020.

## Built on by (most cited first, 14 of 14)

- [Deep Learning in Human Activity Recognition with Wearable Sensors: A Review on Advances](https://mlsanity.com/paper/2111.00418/) (2021)
- [Past, Present, and Future of Sensor-Based Human Activity Recognition Using Wearables: A Surveying Tutorial on a Still Challenging Task](https://mlsanity.com/paper/2411.14452/) (2024)
- [Human Activity Recognition Using Tools of Convolutional Neural Networks: A State of the Art Review, Data Sets, Challenges and Future Prospects](https://mlsanity.com/paper/2202.03274/) (2022)
- [A Survey on Multimodal Wearable Sensor-based Human Action Recognition](https://mlsanity.com/paper/2404.15349/) (2024)
- [Virtual Fusion with Contrastive Learning for Single Sensor-based Activity Recognition](https://mlsanity.com/paper/2312.02185/) (2023)
- [Disentangling Imperfect: A Wavelet-Infused Multilevel Heterogeneous Network for Human Activity Recognition in Flawed Wearable Sensor Data](https://mlsanity.com/paper/2402.09434/) (2024)
- [Multi-channel Time Series Decomposition Network For Generalizable Sensor-Based Activity Recognition](https://mlsanity.com/paper/2406.16872/) (2024)
- [WSense: A Robust Feature Learning Module for Lightweight Human Activity Recognition](https://mlsanity.com/paper/2303.17845/) (2023)
- [Unified Contrastive Fusion Transformer for Multimodal Human Action Recognition](https://mlsanity.com/paper/2309.05032/) (2023)
- [CMD-HAR: Cross-Modal Disentanglement for Wearable Human Activity Recognition](https://mlsanity.com/paper/2503.21843/) (2025)
- [Redundant feature screening method for human activity recognition based on attention purification mechanism](https://mlsanity.com/paper/2503.23537/) (2025)
- [Process Optimization and Deployment for Sensor-Based Human Activity Recognition Based on Deep Learning](https://mlsanity.com/paper/2504.03687/) (2025)
- [Confidence-driven Gradient Modulation for Multimodal Human Activity Recognition: A Dynamic Contrastive Dual-Path Learning Approach](https://mlsanity.com/paper/2507.02826/) (2025)
- [Structure over Depth: A Single-Block Spatio-Temporal Transformer for Multi-Entity Reasoning](https://mlsanity.com/paper/2607.23077/) (2026)

## Abstract (arXiv)

Human activity recognition (HAR) in ubiquitous computing has been beginning to incorporate attention into the context of deep neural networks (DNNs), in which the rich sensing data from multimodal sensors such as accelerometer and gyroscope is used to infer human activities. Recently, two attention methods are proposed via combining with Gated Recurrent Units (GRU) and Long Short-Term Memory (LSTM) network, which can capture the dependencies of sensing signals in both spatial and temporal domains simultaneously. However, recurrent networks often have a weak feature representing power compared with convolutional neural networks (CNNs). On the other hand, two attention, i.e., hard attention and soft attention, are applied in temporal domains via combining with CNN, which pay more attention to the target activity from a long sequence. However, they can only tell where to focus and miss channel information, which plays an important role in deciding what to focus. As a result, they fail to address the spatial-temporal dependencies of multimodal sensing signals, compared with attention-based GRU or LSTM. In the paper, we propose a novel dual attention method called DanHAR, which introduces the framework of blending channel attention and temporal attention on a CNN, demonstrating superiority in improving the comprehensibility for multimodal HAR. Extensive experiments on four public HAR datasets and weakly labeled dataset show that DanHAR achieves state-of-the-art performance with negligible overhead of parameters. Furthermore, visualizing analysis is provided to show that our attention can amplifies more important sensor modalities and timesteps during classification, which agrees well with human common intuition.