# Sequential Weakly Labeled Multi-Activity Localization and Recognition on Wearable Sensors using Recurrent Attention Networks

arXiv: https://arxiv.org/abs/2004.05768 · HTML: https://mlsanity.com/paper/2004.05768/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Apr 2020.

## Built on by (most cited first, 4 of 4)

- [A Matter of Annotation: An Empirical Study on In Situ and Self-Recall Activity Annotations from Wearable Sensors](https://mlsanity.com/paper/2305.08752/) (2023)
- [Accoustate: Auto-annotation of IMU-generated Activity Signatures under Smart Infrastructure](https://mlsanity.com/paper/2112.06651/) (2021)
- [CNN Autoencoders for Hierarchical Feature Extraction and Fusion in Multi-sensor Human Activity Recognition](https://mlsanity.com/paper/2502.04489/) (2025)
- [VALERIAN: Invariant Feature Learning for IMU Sensor-based Human Activity Recognition in the Wild](https://mlsanity.com/paper/2303.06048/) (2023)

## Abstract (arXiv)

With the popularity and development of the wearable devices such as smartphones, human activity recognition (HAR) based on sensors has become as a key research area in human computer interaction and ubiquitous computing. The emergence of deep learning leads to a recent shift in the research of HAR, which requires massive strictly labeled data. In comparison with video data, activity data recorded from accelerometer or gyroscope is often more difficult to interpret and segment. Recently, several attention mechanisms are proposed to handle the weakly labeled human activity data, which do not require accurate data annotation. However, these attention-based models can only handle the weakly labeled dataset whose sample includes one target activity, as a result it limits efficiency and practicality. In the paper, we propose a recurrent attention networks (RAN) to handle sequential weakly labeled multi-activity recognition and location tasks. The model can repeatedly perform steps of attention on multiple activities of one sample and each step is corresponding to the current focused activity. The effectiveness of the RAN model is validated on a collected sequential weakly labeled multi-activity dataset and the other two public datasets. The experiment results show that our RAN model can simultaneously infer multi-activity types from the coarse-grained sequential weak labels and determine specific locations of every target activity with only knowledge of which types of activities contained in the long sequence. It will greatly reduce the burden of manual labeling. The code of our work is available at https://github.com/KennCoder7/RAN.