# Human Activity Recognition

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.

Using the Multimodal Activity Sensing Dataset (MASD; 27 activities, 20 participants), the study tests whether ambient WiFi Channel State Information (CSI) can substitute for a body-worn inertial measurement unit (IMU) in human activity recognition; the authors find that the dataset's released machine-learning-ready files expose a degraded signed CSI representation rather than the documented amplitude, so they rebuild amplitude and antenna-ratio Doppler from the raw complex CSI, recover omitted participant identifiers, and re-evaluate subject-independently under a controlled multi-seed protocol, finding WiFi systematically weak: every backbone they try reaches only 6-11% weighted accuracy on the 27-class set against 73-80% for IMU, with usable accuracy on 0 of 27 activities; the corrected r

Using the Multimodal Activity Sensing Dataset (MASD; 27 activities, 20 participants), the study tests whether ambient WiFi Channel State Information (CSI) can substitute for a body-worn inertial measurement unit (IMU) in human activity recognition; the authors find that the dataset's released machine-learning-ready files expose a degraded signed CSI representation rather than the documented amplitude, so they rebuild amplitude and antenna-ratio Doppler from the raw complex CSI, recover omitted participant identifiers, and re-evaluate subject-independently under a controlled multi-seed protocol, finding WiFi systematically weak: every backbone they try reaches only 6-11% weighted accuracy on the 27-class set against 73-80% for IMU, with usable accuracy on 0 of 27 activities; the corrected r