<![CDATA[Gait-event features dataset intended to support machine-learning-based locomotion analysis, but delivered files are rarely checked against their own documentation or against standard values from the gait-analysis literature. We apply a six-stage, three-layer audit (provenance, structural/physical plausibility, signal validity) to “Robust Gait Event Detection Using GRF and EMG Signals,” a 208-row, 12-feature dataset describing four stance-phase gait events (Heel Strike, Loading Response, Mid-Stance, Heel Off) from 52 participants. Row count, feature count, and participant count all match the hosting page’s Instructions section. However, direct inspection finds two of the twelve documented features are exact duplicates under different names: the column labeled COV (coefficient of variation) is byte-identical to VAR (variance) rather than the standard STD/mean ratio, and the column labeled mode is byte-identical to minimum across all 208 rows, consistent with a computation error rather than genuine central-tendency estimation. An eight-row contiguous block is duplicated verbatim at a fixed 32-row offset, indicating the true unique sample is 50 participants (200 rows), not the documented 52 (208 rows). The file also provides no field distinguishing GRF-derived from EMG-derived features, despite both being named as contributing sources in the dataset’s title and abstract. Finally, while all twelve features discriminate strongly between the four event classes by one-way ANOVA, the percentage feature is exactly 100.000000 with zero variance for every Heel-Off row, consistent with a windowing artifact of the class definition rather than an independently varying measurement, and its per-class averages are inconsistent with standard stance-phase timing reported in the clinical gait-analysis literature.]]>
