# Interpretable Parallel Recurrent Neural Networks with Convolutional Attentions for Multi-Modality Activity Modeling

arXiv: https://arxiv.org/abs/1805.07233 · HTML: https://mlsanity.com/paper/1805.07233/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 May 2018.

## Built on by (most cited first, 3 of 3)

- [An Overview of Human Activity Recognition Using Wearable Sensors: Healthcare and Artificial Intelligence](https://mlsanity.com/paper/2103.15990/) (2021)
- [ASM2TV: An Adaptive Semi-Supervised Multi-Task Multi-View Learning Framework for Human Activity Recognition](https://mlsanity.com/paper/2105.08643/) (2021)
- [Game Theory Solutions in Sensor-Based Human Activity Recognition: A Review](https://mlsanity.com/paper/2311.06311/) (2023)

## Abstract (arXiv)

Multimodal features play a key role in wearable sensor-based human activity recognition (HAR). Selecting the most salient features adaptively is a promising way to maximize the effectiveness of multimodal sensor data. In this regard, we propose a "collect fully and select wisely" principle as well as an interpretable parallel recurrent model with convolutional attentions to improve the recognition performance. We first collect modality features and the relations between each pair of features to generate activity frames, and then introduce an attention mechanism to select the most prominent regions from activity frames precisely. The selected frames not only maximize the utilization of valid features but also reduce the number of features to be computed effectively. We further analyze the accuracy and interpretability of the proposed model based on extensive experiments. The results show that our model achieves competitive performance on two benchmarked datasets and works well in real life scenarios.