# Detecting Domain Shifts in Myoelectric Activations: Challenges and Opportunities in Stream Learning

arXiv: https://arxiv.org/abs/2508.21278 · HTML: https://mlsanity.com/paper/2508.21278/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 43 references that are in the map.
- First posted on arXiv 29 Aug 2025.

## Builds on (most cited first, 7 of 7)

- [Learning under Concept Drift: A Review](https://mlsanity.com/paper/2004.05785/) (2020)
- [Challenges in Benchmarking Stream Learning Algorithms with Real-world Data](https://mlsanity.com/paper/2005.00113/) (2020)
- [Kernel PCA for Out-of-Distribution Detection](https://mlsanity.com/paper/2402.02949/) (2024)
- [Fair Streaming Principal Component Analysis: Statistical and Algorithmic Viewpoint](https://mlsanity.com/paper/2310.18593/) (2023)
- [A Framework and Call to Action for the Future Development of EMG-Based Input in HCI](https://mlsanity.com/paper/2304.00582/) (2023)
- [Adaptive Bernstein Change Detector for High-Dimensional Data Streams](https://mlsanity.com/paper/2306.12974/) (2023)
- [Bayesian Approach for Adaptive EMG Pattern Classification Via Semi-Supervised Sequential Learning](https://mlsanity.com/paper/2310.00252/) (2023)

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

- [LiteEMG-FM: An Efficient and Deployable Foundation Model for Robust EMG Sensing](https://mlsanity.com/paper/2610.02497/) (2026)

## Abstract (arXiv)

Detecting domain shifts in myoelectric activations poses a significant challenge due to the inherent non-stationarity of electromyography (EMG) signals. This paper explores the detection of domain shifts using data stream (DS) learning techniques, focusing on the DB6 dataset from the Ninapro database. We define domains as distinct time-series segments based on different subjects and recording sessions, applying Kernel Principal Component Analysis (KPCA) with a cosine kernel to pre-process and highlight these shifts. By evaluating multiple drift detection methods such as CUSUM, Page-Hinckley, and ADWIN, we reveal the limitations of current techniques in achieving high performance for real-time domain shift detection in EMG signals. Our results underscore the potential of streaming-based approaches for maintaining stable EMG decoding models, while highlighting areas for further research to enhance robustness and accuracy in real-world scenarios.