Representation Learning for Wearable-Based Applications in the Case of Missing Data
Wearable devices continuously collect sensor data and use it to infer an individual's behavior, such as sleep, physical activity, and emotions. Despite the significant interest and advancements in this field, modeling multimodal sensor data in real-world environments is still challenging due to low data quality and limited data annotations. In this work, we investigate representation learning for imputing missing wearable data and compare it with state-of-the-art statistical approaches. We investigate the performance of the transformer model on 10 physiological and behavioral signals with different masking ratios. Our results show that transformers outperform baselines for missing data imputation of signals that change more frequently, but not for monotonic signals. We further investigate the impact of imputation strategies and masking rations on downstream classification tasks. Our study provides insights for the design and development of masking-based self-supervised learning tasks and advocates the adoption of hybrid-based imputation strategies to address the challenge of missing data in wearable devices.
Code (0)
등록된 구현이 없습니다.
Tasks
ImputationRepresentation LearningSelf-Supervised LearningSimilar Papers 제목 키워드 기반
Early Detection of Patient Deterioration from Real-Time Wearable Monitoring System
Early detection of patient deterioration is crucial for reducing mortality rates. Heart rate data has shown promise in assessing patient health, and wearable devices offer a cost-effective solution for real-time monitori…
DiagnosticMissing ValuesTime SeriesDynImp: Dynamic Imputation for Wearable Sensing Data Through Sensory and Temporal Relatedness
In wearable sensing applications, data is inevitable to be irregularly sampled or partially missing, which pose challenges for any downstream application. An unique aspect of wearable data is that it is time-series data …
Activity RecognitionDenoisingImputationTime Series+1LSM-2: Learning from Incomplete Wearable 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…
DiagnosticImputationSelf-Supervised LearningLearning Longitudinal Health Representations from EHR and Wearable Data
Foundation models trained on electronic health records show strong performance on many clinical prediction tasks but are limited by sparse and irregular documentation. Wearable devices provide dense continuous physiologi…
VCR: Learning Valid Contextual Representation for Incomplete Wearable Signals
Wearable devices enable continuous health monitoring from multimodal signals, but real-world deployment is hindered by limited labeled data and pervasive sensor incompleteness. While large-scale self-supervised pretraini…