# An Empirical Evaluation of Neural and Neuro-symbolic Approaches to Real-time Multimodal Complex Event Detection

arXiv: https://arxiv.org/abs/2402.11403 · HTML: https://mlsanity.com/paper/2402.11403/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 24 references that are in the map.
- First posted on arXiv 17 Feb 2024.

## Builds on (most cited first, 9 of 9)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling](https://mlsanity.com/paper/1803.01271/) (2018)
- [LongBench: A Bilingual, Multitask Benchmark for Long Context Understanding](https://mlsanity.com/paper/2308.14508/) (2023)
- [Multimodal Machine Learning: A Survey and Taxonomy](https://mlsanity.com/paper/1705.09406/) (2017)
- [Long Range Arena: A Benchmark for Efficient Transformers](https://mlsanity.com/paper/2011.04006/) (2020)
- [Neural Probabilistic Logic Programming in DeepProbLog](https://mlsanity.com/paper/1907.08194/) (2019)
- [The Future is not One-dimensional: Complex Event Schema Induction by Graph Modeling for Event Prediction](https://mlsanity.com/paper/2104.06344/) (2021)
- [Temporal and Object Quantification Networks](https://mlsanity.com/paper/2106.05891/) (2021)
- [Human Activity Recognition using Deep Learning Models on Smartphones and Smartwatches Sensor Data](https://mlsanity.com/paper/2103.03836/) (2021)

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

- [Neuro-Symbolic Fusion of Wi-Fi Sensing Data for Passive Radar with Inter-Modal Knowledge Transfer](https://mlsanity.com/paper/2407.04734/) (2024)

## Abstract (arXiv)

Robots and autonomous systems require an understanding of complex events (CEs) from sensor data to interact with their environments and humans effectively. Traditional end-to-end neural architectures, despite processing sensor data efficiently, struggle with long-duration events due to limited context sizes and reasoning capabilities. Recent advances in neuro-symbolic methods, which integrate neural and symbolic models leveraging human knowledge, promise improved performance with less data. This study addresses the gap in understanding these approaches' effectiveness in complex event detection (CED), especially in temporal reasoning. We investigate neural and neuro-symbolic architectures' performance in a multimodal CED task, analyzing IMU and acoustic data streams to recognize CE patterns. Our methodology includes (i) end-to-end neural architectures for direct CE detection from sensor embeddings, (ii) two-stage concept-based neural models mapping sensor embeddings to atomic events (AEs) before CE detection, and (iii) a neuro-symbolic approach using a symbolic finite-state machine for CE detection from AEs. Empirically, the neuro-symbolic architecture significantly surpasses purely neural models, demonstrating superior performance in CE recognition, even with extensive training data and ample temporal context for neural approaches.