Applicability of memorization indicators for early spotting of overfitting while recalibrating sEMG-decoders on low sample sizes
arXiv:2606.27855v1 Announce Type: new Abstract: Deep learning models for surface electromyography (sEMG) can benefit substantially from subject-specific (re-)calibration, since no sufficiently large and diverse datasets are available to train fully generic decoders. However, for user acceptance, the number of repetitions that can realistically be collected during calibration is severely limited, which increases the risk of overfitting and, in extreme cases, can even degrade performance compared to the uncalibrated model. Classical overfitting indicators such as validation performance and regul
The proliferation of deep learning models in sensitive applications like sEMG, coupled with limitations in data collection, makes identifying and mitigating overfitting an increasingly relevant challenge.
Improving the robustness and reliability of personalized AI systems, especially in medical and human-computer interaction contexts, is crucial for broader adoption and beneficial real-world applications.
The development of reliable early indicators for overfitting in low-sample, personalized AI calibration scenarios means more efficient and trustworthy model deployment without extensive user data.
- · AI model developers
- · Medical device manufacturers
- · Rehabilitation clinics
- · Users of sEMG-controlled prosthetics
- · Companies relying on large, generic datasets
- · Inefficient AI calibration methodologies
More efficient and reliable personalized AI model training, especially in data-scarce domains, will become possible.
This could accelerate the development and adoption of advanced prosthetics and human-machine interfaces, leading to better functional outcomes for users.
The principles might extend to other domains requiring low-data personalization, potentially democratizing access to complex AI applications by reducing data burdens.
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Read at arXiv cs.LG