# Explainable Deep Learning Framework for Human Activity Recognition

arXiv: https://arxiv.org/abs/2408.11552 · HTML: https://mlsanity.com/paper/2408.11552/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 22 references that are in the map.
- First posted on arXiv 21 Aug 2024.

## Builds on (most cited first, 8 of 8)

- [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)
- [TS-MULE: Local Interpretable Model-Agnostic Explanations for Time Series Forecast Models](https://mlsanity.com/paper/2109.08438/) (2021)
- [Reliable Local Explanations for Machine Listening](https://mlsanity.com/paper/2005.07788/) (2020)
- [Human Activity Recognition Based on Wearable Sensor Data: A Standardization of the State-of-the-Art](https://mlsanity.com/paper/1806.05226/) (2018)
- [TSInsight: A local-global attribution framework for interpretability in time-series data](https://mlsanity.com/paper/2004.02958/) (2020)
- [McXai: Local model-agnostic explanation as two games](https://mlsanity.com/paper/2201.01044/) (2022)

## Built on by (most cited first, 1 of 1)

- [Explainable Human Activity Recognition: A Unified Review of Concepts and Mechanisms](https://mlsanity.com/paper/2604.09799/) (2026)

## Abstract (arXiv)

In the realm of human activity recognition (HAR), the integration of explainable Artificial Intelligence (XAI) emerges as a critical necessity to elucidate the decision-making processes of complex models, fostering transparency and trust. Traditional explanatory methods like Class Activation Mapping (CAM) and attention mechanisms, although effective in highlighting regions vital for decisions in various contexts, prove inadequate for HAR. This inadequacy stems from the inherently abstract nature of HAR data, rendering these explanations obscure. In contrast, state-of-th-art post-hoc interpretation techniques for time series can explain the model from other perspectives. However, this requires extra effort. It usually takes 10 to 20 seconds to generate an explanation. To overcome these challenges, we proposes a novel, model-agnostic framework that enhances both the interpretability and efficacy of HAR models through the strategic use of competitive data augmentation. This innovative approach does not rely on any particular model architecture, thereby broadening its applicability across various HAR models. By implementing competitive data augmentation, our framework provides intuitive and accessible explanations of model decisions, thereby significantly advancing the interpretability of HAR systems without compromising on performance.