Assessing Observable Resolution in Open-Access sEMG Datasets for High-Resolution Hand-Gesture Research: A Quantization-Aware Methodological Study

Open-access surface electromyography (sEMG) datasets are widely used for gesture-recognition research; however, limited documentation of acquisition and post-processing may prevent verification of whether the numerical structure imposed during acquisition is preserved in the distributed data. This study evaluates one selected signal vector from each of eleven public forearm sEMG datasets using a quantization-aware framework, complemented by an assessment across participants and channels to examine within-dataset consistency. The framework distinguishes declared resolution (DR) from observable effective resolution (OER), a signal-dependent descriptor of the numerical granularity observable in the distributed data rather than a measure of hardware ENOB. The analysis uses the minimum nonzero first-order difference (Δmin), zero-difference proportion (P0), relative signal cardinality, and a quantization-grid alignment metric (Egrid) to characterize numerical discreteness and grid preservation. Recordings analyzed from five datasets exhibited ordered difference levels consistent with a preserved observable quantization grid, whereas those from six showed disrupted structures. The complementary assessment revealed both consistent numerical patterns and within-dataset variability across the examined recordings. Egrid quantified approximate grid alignment at the adopted tolerance, while P0 and cardinality served as complementary descriptors. Validation using an independently documented 24-bit dataset yielded Egrid = 1, despite low P0 and high cardinality, demonstrating that these indirect descriptors depend on signal and acquisition characteristics. The results show that nominal analog-to-digital converter (ADC) resolution and stored numerical precision alone cannot establish acquisition-level quantization integrity. Numerical auditing can therefore provide additional traceability when public datasets are used to investigate signal resolution and quantization effects.