Egocentric EMG-Force dataset: does EMG predict grip force?

FiftyOne 1.22.0 adds the egocentric-emg-force dataset to its Dataset Zoo, containing eight MCAP-recorded household-task episodes that combine chest-mounted RGB-D video, wrist IMU data, hand skeletons, eight-channel forearm EMG, and vision-derived per-finger force estimates. The article evaluates whether processed EMG envelopes predict these force estimates by rectifying, averaging, resampling, and smoothing EMG signals before performing per-hand, per-episode lag-search cross-correlations. Across 16 hand-episode pairs, the strongest observed correlation was 0.426, 13 pairs were below 0.3, and optimal lags varied from -230 to 200 milliseconds rather than consistently matching the dataset card’s documented 80-millisecond EMG lead. These results suggest that the force channel should be regarded as an independent estimate derived from pose and depth, rather than a direct substitute for physical force measurements or raw muscle activity. The post also describes how FiftyOne can query episode metadata and inspect multimodal streams, and presents the lag-search workflow as a reusable method for evaluating alignment and agreement between independently sampled MCAP sensor topics.

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