HARMES is the first large-scale dataset to combine wrist IMU, environmental, and audio sensors for recognizing 15 household activities across over 80 hours of data from 20 participants.
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OrganicHAR discovers 4-8 activity categories per user from sensor signals, achieves 79% accuracy on coarse activities with ambient sensors alone and cuts VLM queries by 90% by triggering video analysis only at detected pattern moments.
A multimodal machine learning framework fusing smartwatch audio and inertial sensing achieves macro F1 scores of 82% in lab and 77% in semi-naturalistic studies for detecting face-to-face conversations.
citing papers explorer
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HARMES: A Multi-Modal Dataset for Wearable Human Activity Recognition with Motion, Environmental Sensing and Sound
HARMES is the first large-scale dataset to combine wrist IMU, environmental, and audio sensors for recognizing 15 household activities across over 80 hours of data from 20 participants.
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OrganicHAR: Towards Activity Discovery in Organic Settings for Privacy Preserving Sensors Using Efficient Video Analysis
OrganicHAR discovers 4-8 activity categories per user from sensor signals, achieves 79% accuracy on coarse activities with ambient sensors alone and cuts VLM queries by 90% by triggering video analysis only at detected pattern moments.
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Detecting In-Person Conversations in Noisy Real-World Environments with Smartwatch Audio and Motion Sensing
A multimodal machine learning framework fusing smartwatch audio and inertial sensing achieves macro F1 scores of 82% in lab and 77% in semi-naturalistic studies for detecting face-to-face conversations.