{"total":69,"rows":[{"id":"2410.20542","title":"PaPaGei: Open Foundation Models for Optical Physiological Signals","created":"2024-10-27","cited_by_count":69,"area":null},{"id":"2410.13638","title":"Scaling Wearable Foundation Models","created":"2024-10-17","cited_by_count":63,"area":null},{"id":"2506.09108","title":"SensorLM: Learning the Language of Wearable Sensors","created":"2025-06-10","cited_by_count":49,"area":null},{"id":"2309.06236","title":"The first step is the hardest: Pitfalls of Representing and Tokenizing Temporal Data for Large Language Models","created":"2023-09-12","cited_by_count":34,"area":null},{"id":"2412.09758","title":"Toward Foundation Model for Multivariate Wearable Sensing of Physiological Signals","created":"2024-12-12","cited_by_count":32,"area":null},{"id":"2410.19818","title":"UniMTS: Unified Pre-training for Motion Time Series","created":"2024-10-18","cited_by_count":26,"area":null},{"id":"2506.05321","title":"LSM-2: Learning from Incomplete Wearable Sensor Data","created":"2025-06-05","cited_by_count":23,"area":null},{"id":"2411.18822","title":"RelCon: Relative Contrastive Learning for a Motion Foundation Model for Wearable Data","created":"2024-11-27","cited_by_count":20,"area":null},{"id":"2510.25785","title":"HiMAE: Hierarchical Masked Autoencoders Discover Resolution-Specific Structure in Wearable Time Series","created":"2025-10-28","cited_by_count":19,"area":null},{"id":"2407.06628","title":"Masked Video and Body-worn IMU Autoencoder for Egocentric Action Recognition","created":"2024-07-09","cited_by_count":18,"area":null},{"id":"2507.00191","title":"Beyond Sensor Data: Foundation Models of Behavioral Data from Wearables Improve Health Predictions","created":"2025-06-30","cited_by_count":18,"area":null},{"id":"2403.01133","title":"Evaluating Large Language Models as Virtual Annotators for Time-series Physical Sensing Data","created":"2024-03-02","cited_by_count":17,"area":null},{"id":"2411.14452","title":"Past, Present, and Future of Sensor-Based Human Activity Recognition Using Wearables: A Surveying Tutorial on a Still Challenging Task","created":"2024-11-11","cited_by_count":15,"area":null},{"id":"2407.01238","title":"Large Language Models are Zero-Shot Recognizers for Activities of Daily Living","created":"2024-07-01","cited_by_count":13,"area":null},{"id":"2603.00190","title":"OSF: On Pre-training and Scaling of Sleep Foundation Models","created":"2026-02-27","cited_by_count":12,"area":null},{"id":"2404.15349","title":"A Survey on Multimodal Wearable Sensor-based Human Action Recognition","created":"2024-04-14","cited_by_count":10,"area":null},{"id":"2411.15127","title":"PRIMUS: Pretraining IMU Encoders with Multimodal Self-Supervision","created":"2024-11-22","cited_by_count":10,"area":null},{"id":"2305.17154","title":"On convex decision regions in deep network representations","created":"2023-05-26","cited_by_count":7,"area":null},{"id":"2508.12213","title":"Towards Generalizable Human Activity Recognition: A Survey","created":"2025-08-17","cited_by_count":6,"area":null},{"id":"2501.16368","title":"Foundation Models for CPS-IoT: Opportunities and Challenges","created":"2025-01-22","cited_by_count":5,"area":null},{"id":"2502.03364","title":"Scaling laws in wearable human activity recognition","created":"2025-02-05","cited_by_count":4,"area":null},{"id":"2502.05783","title":"WatchGuardian: Enabling User-Defined Personalized Just-in-Time Intervention on Smartwatch","created":"2025-02-09","cited_by_count":4,"area":null},{"id":"2305.10740","title":"A benchmark for computational analysis of animal behavior, using animal-borne tags","created":"2023-05-18","cited_by_count":3,"area":null},{"id":"2305.13541","title":"ConvBoost: Boosting ConvNets for Sensor-based Activity Recognition","created":"2023-05-22","cited_by_count":3,"area":null},{"id":"2506.06917","title":"Graph-Based Physics-Guided Urban PM2.5 Air Quality Imputation with Constrained Monitoring Data","created":"2025-06-07","cited_by_count":3,"area":null},{"id":"2607.06617","title":"Inertia-1: An Open Exploration of Wearable Motion Foundation Models","created":"2026-07-07","cited_by_count":3,"area":null},{"id":"2209.00945","title":"IMG2IMU: Translating Knowledge from Large-Scale Images to IMU Sensing Applications","created":"2022-09-02","cited_by_count":2,"area":null},{"id":"2210.07340","title":"LEAVES: Learning Views for Time-Series Biobehavioral Data in Contrastive Learning","created":"2022-10-13","cited_by_count":2,"area":null},{"id":"2304.14912","title":"Human Activity Recognition Using Self-Supervised Representations of Wearable Data","created":"2023-04-26","cited_by_count":2,"area":null},{"id":"2307.01168","title":"Don't freeze: Finetune encoders for better Self-Supervised HAR","created":"2023-07-03","cited_by_count":2,"area":null},{"id":"2410.03211","title":"CUDLE: Learning Under Label Scarcity to Detect Cannabis Use in Uncontrolled Environments","created":"2024-10-04","cited_by_count":2,"area":null},{"id":"2502.09626","title":"Evidence for Phenotype-Driven Disparities in Freezing of Gait Detection and Approaches to Bias Mitigation","created":"2025-01-29","cited_by_count":2,"area":null},{"id":"2509.21673","title":"SlotFM: A Motion Foundation Model with Slot Attention for Diverse Downstream Tasks","created":"2025-09-25","cited_by_count":2,"area":null},{"id":"2603.10961","title":"Bio-Inspired Self-Supervised Learning for Wrist-worn Accelerometer Data","created":"2026-03-11","cited_by_count":2,"area":null},{"id":"2605.08296","title":"BenchHAR: Benchmarking Self-Supervised Learning for Generalizable Sensor-based Activity Recognition","created":"2026-05-08","cited_by_count":2,"area":null},{"id":"2212.13918","title":"Simple Yet Surprisingly Effective Training Strategies for LSTMs in Sensor-Based Human Activity Recognition","created":"2022-12-23","cited_by_count":1,"area":null},{"id":"2404.15305","title":"SelfReplay: Adapting Self-Supervised Sensory Models via Adaptive Meta-Task Replay","created":"2024-03-29","cited_by_count":1,"area":null},{"id":"2503.17978","title":"PIM: Physics-Informed Multi-task Pre-training for Improving Inertial Sensor-Based Human Activity Recognition","created":"2025-03-23","cited_by_count":1,"area":null},{"id":"2512.19713","title":"Reducing Label Dependency in Human Activity Recognition with Wearables: From Supervised Learning to Novel Weakly Self-Supervised Approaches","created":"2025-12-15","cited_by_count":1,"area":null},{"id":"2601.12227","title":"Learning Longitudinal Health Representations from EHR and Wearable Data","created":"2026-01-18","cited_by_count":1,"area":null},{"id":"2605.17618","title":"Prediction of Challenging Behaviors Associated with Profound Autism in a Classroom Setting Using Wearable Sensors","created":"2026-05-17","cited_by_count":1,"area":null},{"id":"2606.09605","title":"Next-Token Prediction Learns Generalisable Representations of Sleep Physiology","created":"2026-06-08","cited_by_count":1,"area":null},{"id":"2606.24985","title":"Retrieval-Augmented Personalization with Foundation Models for Wearable Stress Detection","created":"2026-06-23","cited_by_count":1,"area":null},{"id":"2607.16350","title":"Joint-Embedding Predictive Architecture for Sensor-based Activity Recognition","created":"2026-07-17","cited_by_count":1,"area":null},{"id":"2412.11286","title":"Detecting Daily Living Gait Amid Huntington's Disease Chorea using a Foundation Deep Learning Model","created":"2024-12-15","cited_by_count":0,"area":null},{"id":"2412.18735","title":"Automatic Self-supervised Learning for Social Recommendations","created":"2024-12-25","cited_by_count":0,"area":null},{"id":"2502.17477","title":"Frequency-Aware Masked Autoencoders for Human Activity Recognition using Accelerometers","created":"2025-02-17","cited_by_count":0,"area":null},{"id":"2503.15221","title":"A Vector-Quantized Foundation Model for Patient Behavior Monitoring","created":"2025-03-19","cited_by_count":0,"area":null},{"id":"2506.02260","title":"MoCA: Multi-modal Cross-masked Autoencoder for Time Series in Digital Health","created":"2025-06-02","cited_by_count":0,"area":null},{"id":"2506.09167","title":"Estimating Visceral Adiposity from Wrist-Worn Accelerometry","created":"2025-06-10","cited_by_count":0,"area":null},{"id":"2508.05175","title":"Human Activity Recognition from Smartphone Sensor Data for Clinical Trials","created":"2025-08-07","cited_by_count":0,"area":null},{"id":"2509.00221","title":"Speech Foundation Models Generalize to Time Series Tasks from Wearable Sensor Data","created":"2025-08-29","cited_by_count":0,"area":null},{"id":"2510.01712","title":"ActiNet: An Open-Source Tool for Activity Intensity Classification of Wrist-Worn Accelerometry Using Self-Supervised Deep Learning","created":"2025-10-02","cited_by_count":0,"area":null},{"id":"2510.09764","title":"Prototype-based Self-Supervised Multimodal Learning for PPG and Accelerometry Signals","created":"2025-10-10","cited_by_count":0,"area":null},{"id":"2601.17049","title":"Summary of the Unusual Activity Recognition Challenge for Developmental Disability Support","created":"2026-01-21","cited_by_count":0,"area":null},{"id":"2602.01910","title":"DomusFM: A Foundation Model for Event-Based Behavioral Monitoring in Smart-Homes","created":"2026-02-02","cited_by_count":0,"area":null},{"id":"2602.04266","title":"Aortic Valve Disease Screening from PPG via Physiology-Guided Self-Supervised Learning","created":"2026-02-04","cited_by_count":0,"area":null},{"id":"2602.11064","title":"Motion Capture is Not the Target Domain: Scaling Synthetic Data for Learning Motion Representations","created":"2026-02-11","cited_by_count":0,"area":null},{"id":"2603.05184","title":"Logi-PAR: Logic-Infused Patient Activity Recognition via Differentiable Rule","created":"2026-03-05","cited_by_count":0,"area":null},{"id":"2605.09173","title":"WavesFM: Hierarchical Representation Learning for Longitudinal Wearable Sensor Waveforms","created":"2026-05-09","cited_by_count":0,"area":null},{"id":"2606.14145","title":"Personal Care Utility: Health as Everyday Infrastructure","created":"2026-06-12","cited_by_count":0,"area":null},{"id":"2607.07775","title":"Idiobionics: The Unification of Privacy and Intelligent Robotic Prostheses","created":"2026-07-08","cited_by_count":0,"area":null},{"id":"2608.02946","title":"Sedentary Behavior Classification for Wearable Sensors with a CNN-BiLSTM Model","created":"2026-08-03","cited_by_count":0,"area":null},{"id":"2608.03391","title":"TimeRLM: Recursive Language Models Enable Precise Anomaly Localization in Long-Context Time-Series","created":"2026-08-04","cited_by_count":0,"area":null},{"id":"2608.13316","title":"Foundation models for movement data: Are they ready for prime-time?","created":"2026-08-13","cited_by_count":0,"area":null},{"id":"2609.29937","title":"When Temporal Perturbations Act Like Sensor Biases: Label-Free Auditing of Wearable Activity Recognizers","created":"2026-09-24","cited_by_count":0,"area":null},{"id":"2610.02711","title":"Characterizing the Performance Gap in Human Activity Recognition for Older Adults","created":"2026-10-02","cited_by_count":0,"area":null},{"id":"2610.17383","title":"Variable Input Masked Autoencoders for Wrist-Worn Accelerometry","created":"2026-10-12","cited_by_count":0,"area":null},{"id":"2610.19481","title":"IMU-Bench: Benchmarking Wrist IMU Representations Across Kinematic, Semantic and Clinical Tasks","created":"2026-10-14","cited_by_count":0,"area":null}],"next_cursor":null}