# UniHAR

Wearable activity recognition has been tied to a fixed sensor channel count, so moving to a new dataset meant redesigning the input layer. UniHAR projects heterogeneous sensor layouts into a shared feature space through channel-agnostic per-channel descriptors, then tokenizes at three temporal scales and mixes each scale with FFT-based filtering. One backbone reaches 96.35%, 94.45%, 96.44% and 99.23% accuracy on UCI-HAR, Opportunity, Daphnet and PAMAP2 with 0.7–0.9M parameters.

Wearable activity recognition has been tied to a fixed sensor channel count, so moving to a new dataset meant redesigning the input layer. UniHAR projects heterogeneous sensor layouts into a shared feature space through channel-agnostic per-channel descriptors, then tokenizes at three temporal scales and mixes each scale with FFT-based filtering. One backbone reaches 96.35%, 94.45%, 96.44% and 99.23% accuracy on UCI-HAR, Opportunity, Daphnet and PAMAP2 with 0.7–0.9M parameters.