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LSM-2: Learning from incomplete wearable sensor data
July 22, 2025 Girish Narayanswamy and Maxwell A. Xu, Student Researchers, Google Research We introduce LSM-2 with Adaptive and Inherited Masking (AIM), a novel self-supervised learning approach that learns directly from incomplete wearable sensor data, achieving strong performance across classification, regression, and generative tasks without explicit imputation.
Wearable devices have revolutionized health monitoring by providing continuous, multimodal physiological and behavioral data — from heart signals and sleep patterns to activity levels and stress indicators. Due to advances in sensor technology, it is increasingly feasible to capture a large volume of data, but the cost of labeling remains high, requiring real-time user annotations or laborious clinical studies. The following researchers contributed to this work: Maxwell A. Xu, Girish Narayanswamy, Kumar Ayush, Dimitris Spathis, Shun Liao, Shyam Tailor, Ahmed Metwally, A. Ali Heydari, Yuwei Zhang, Jake Garrison, Samy Abdel-Ghaffar, Xuhai Xu, Ken Gu, Jacob Sunshine, Ming-Zher Poh, Yun Liu, Tim Althoff, Shrikanth Narayanan, Pushmeet Kohli, Mark Malhotra, Shwetak Patel, Yuzhe Yang, James M. Rehg, Xin Liu, and Daniel McDuff.
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