LSM-2: Learning from Incomplete Wearable Sensor Data
This addresses the challenge of missing data in wearable sensors for real-world applications, offering a more reliable method for health monitoring and diagnostics.
The paper tackled the problem of learning from incomplete wearable sensor data by introducing LSM-2 with Adaptive and Inherited Masking (AIM), a self-supervised approach that achieves state-of-the-art performance across classification, regression, and generative tasks without explicit imputation, pre-trained on 40M hours of multimodal sensor data.
Foundation models, a cornerstone of recent advancements in machine learning, have predominantly thrived on complete and well-structured data. Wearable sensor data frequently suffers from significant missingness, posing a substantial challenge for self-supervised learning (SSL) models that typically assume complete data inputs. This paper introduces the second generation of Large Sensor Model (LSM-2) with Adaptive and Inherited Masking (AIM), a novel SSL approach that learns robust representations directly from incomplete data without requiring explicit imputation. AIM's core novelty lies in its use of learnable mask tokens to model both existing ("inherited") and artificially introduced missingness, enabling it to robustly handle fragmented real-world data during inference. Pre-trained on an extensive dataset of 40M hours of day-long multimodal sensor data, our LSM-2 with AIM achieves the best performance across a diverse range of tasks, including classification, regression and generative modeling. Furthermore, LSM-2 with AIM exhibits superior scaling performance, and critically, maintains high performance even under targeted missingness scenarios, reflecting clinically coherent patterns, such as the diagnostic value of nighttime biosignals for hypertension prediction. This makes AIM a more reliable choice for real-world wearable data applications.