# A Novel Deep Hybrid Framework with Ensemble-Based Feature Optimization for Robust Real-Time Human Activity Recognition

arXiv: https://arxiv.org/abs/2508.18695 · HTML: https://mlsanity.com/paper/2508.18695/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 38 references that are in the map.
- First posted on arXiv 26 Aug 2025.

## Builds on (most cited first, 10 of 10)

- [Two-Stream Convolutional Networks for Action Recognition in Videos](https://mlsanity.com/paper/1406.2199/) (2014)
- [Learning Compact Recurrent Neural Networks with Block-Term Tensor Decomposition](https://mlsanity.com/paper/1712.05134/) (2017)
- [SETransformer: A Hybrid Attention-Based Architecture for Robust Human Activity Recognition](https://mlsanity.com/paper/2505.19369/) (2025)
- [COVID-19 Detection in Chest X-Ray Images using a New Channel Boosted CNN](https://mlsanity.com/paper/2012.05073/) (2020)
- [Two-Stream RNN/CNN for Action Recognition in 3D Videos](https://mlsanity.com/paper/1703.09783/) (2017)
- [ViT-ReT: Vision and Recurrent Transformer Neural Networks for Human Activity Recognition in Videos](https://mlsanity.com/paper/2208.07929/) (2022)
- [MoPFormer: Motion-Primitive Transformer for Wearable-Sensor Activity Recognition](https://mlsanity.com/paper/2505.20744/) (2025)
- [Sequential Deep Trajectory Descriptor for Action Recognition with Three-stream CNN](https://mlsanity.com/paper/1609.03056/) (2016)
- [Transformer-Based Approaches for Sensor-Based Human Activity Recognition: Opportunities and Challenges](https://mlsanity.com/paper/2410.13605/) (2024)
- [A Novel Channel Boosted Residual CNN-Transformer with Regional-Boundary Learning for Breast Cancer Detection](https://mlsanity.com/paper/2503.15008/) (2025)

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

- [RSwinV2-MD: An Enhanced Residual SwinV2 Transformer for Monkeypox Detection from Skin Images](https://mlsanity.com/paper/2601.01835/) (2026)

## Abstract (arXiv)

Real-time Human Activity Recognition (HAR) has wide-ranging applications in areas such as context-aware environments, public safety, assistive technologies, and autonomous monitoring and surveillance systems. However, existing real-time HAR systems face significant challenges, including limited scalability and high computational costs arising from redundant features. To address these issues, the Inception-V3 model was customized with region-based and boundary-aware operations, using average pooling and max pooling, respectively, to enhance region homogeneity, suppress noise, and capture discriminative local features, while improving robustness through down-sampling. Furthermore, to effectively encode motion dynamics, an Attention-Augmented Long Short-Term Memory (AA-LSTM) network was employed to learn temporal dependencies across video frames. Features are extracted from video dataset and are then optimized through a novel proposed dynamic composite feature selection method called Adaptive Dynamic Fitness Sharing and Attention (ADFSA). This ADFSA mechanism is embedded within a genetic algorithm to select a compact, optimized subset of features by dynamically balancing multiple objectives, accuracy, redundancy reduction, feature uniqueness, and complexity minimization. As a result, the selected subset of diverse and discriminative features enables lightweight machine learning classifiers to achieve accurate and robust HAR in heterogeneous environments. Experimental results demonstrate up to 99.65% accuracy using as few as seven selected features, with improved inference time on the challenging UCF-YouTube dataset, which includes factors such as occlusion, cluttered backgrounds, complex motion dynamics, and poor illumination conditions.