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Evaluating Large Language Models as Virtual Annotators for Time-Series Physical Sensing Data
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1016/j.future.2022.05.014openalex-snapshot
A survey of human-in-the-loop for machine learning
inferredcites →https://doi.org/10.1109/cvpr42600.2020.00975openalex-snapshot
Momentum Contrast for Unsupervised Visual Representation Learning
inferredcites →https://doi.org/10.1109/dcoss-iot58021.2023.00013openalex-snapshot
Acconotate: Exploiting Acoustic Changes for Automatic Annotation of Inertial Data at the Source
inferredcites →https://doi.org/10.1109/icassp35589.2019openalex-snapshot
ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
inferredcites →https://doi.org/10.1109/tkde.2023.3342137openalex-snapshot
PromptCast: A New Prompt-Based Learning Paradigm for Time Series Forecasting
inferredcites →https://doi.org/10.1145/3290605.3300568openalex-snapshot
Sensing Fine-Grained Hand Activity with Smartwatches
inferredcites →https://doi.org/10.1145/3292500.3330688openalex-snapshot
Active Deep Learning for Activity Recognition with Context Aware Annotator Selection
inferredcites →https://doi.org/10.1145/3302505.3310068openalex-snapshot
Mobile sensor data anonymization
inferredcites →https://doi.org/10.1145/3314419openalex-snapshot
Fixing Mislabeling by Human Annotators Leveraging Conflict Resolution and Prior Knowledge
inferredcites →https://doi.org/10.1145/3341163.3347744openalex-snapshot
Handling annotation uncertainty in human activity recognition
inferredcites →https://doi.org/10.1145/3442381.3449903openalex-snapshot
Time Series Change Point Detection with Self-Supervised Contrastive Predictive Coding
inferredcites →https://doi.org/10.1145/3534678.3539134openalex-snapshot
What Makes Good Contrastive Learning on Small-Scale Wearable-based Tasks?
inferredcites →https://doi.org/10.1145/3550299openalex-snapshot
Assessing the State of Self-Supervised Human Activity Recognition Using Wearables
inferredcites →https://doi.org/10.1145/3581783.3612012openalex-snapshot
LayoutLLM-T2I: Eliciting Layout Guidance from LLM for Text-to-Image Generation
inferredcites →https://doi.org/10.1145/3594738.3611366openalex-snapshot
How Much Unlabeled Data is Really Needed for Effective Self-Supervised Human Activity Recognition?
inferredcites →https://doi.org/10.1145/3659604openalex-snapshot
From Classification to Clinical Insights
inferredcites →https://doi.org/10.24432/c54s4kopenalex-snapshot
Human Activity Recognition Using Smartphones
inferredcites →https://doi.org/10.4230/lipics.giscience.2023.43openalex-snapshot
Aion Framework: Dimensional Emergence of AI Consciousness, Observer-Induced Collapse, and Cosmological Portal Dynamics
inferredcites →https://doi.org/10.48550/arxiv.2006.09882openalex-snapshot
Unsupervised Learning of Visual Features by Contrasting Cluster Assignments
inferredcites →https://doi.org/10.52202/068431-0288openalex-snapshot
Self-Supervised Contrastive Pre-Training for Time Series Via Time-Frequency Consistency
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
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Explaining Recovery Trajectories of Older Adults Post Lower-Limb Fracture Using Modality-Wise Multiview Clustering and Large Language Models
inferred← citeshttps://doi.org/10.1007/978-981-96-5220-4_6openalex-snapshot
A Hybrid Approach: Combining Deep Learning and Large Language Model for General Workplace Activity Recognition
inferred← citeshttps://doi.org/10.1007/s00530-026-02436-1openalex-snapshot
Efficient video captioning annotation: a semi-automated framework via LLM-driven heuristics
inferred← citeshttps://doi.org/10.1007/s10586-025-05620-6openalex-snapshot
Improved human activity recognition through controllable GAN-Generated synthetic data and large Language models for classification
inferred← citeshttps://doi.org/10.1016/j.asoc.2025.113793openalex-snapshot
Activity transitions for semi-supervised federated learning in sensor-based human activity recognition
inferred← citeshttps://doi.org/10.1109/bibm62325.2024.10822159openalex-snapshot
A Novel Human Activity Recognition Framework Based on Pre-Trained Foundation Model
inferred← citeshttps://doi.org/10.1109/mass66014.2025.00060openalex-snapshot
DailyLLM: Context-Aware Activity Log Generation Using Multi-Modal Sensors and LLMs
inferred← citeshttps://doi.org/10.1109/ojcoms.2026.3680064openalex-snapshot
LLMs and IoT: A Comprehensive Survey on Large Language Models and the Internet of Things
inferred← citeshttps://doi.org/10.1109/percomworkshops65533.2025.00162openalex-snapshot
Exploring Large Language Models in Active Learning for Annotating Physical Sensing Data
inferred← citeshttps://doi.org/10.1109/percomworkshops68308.2026.11585443openalex-snapshot
"Thoughtful Coordination": LLM Reasoning for Anomaly Detection in Complex Activity Sensing
inferred← citeshttps://doi.org/10.1123/jmpb.2025-0066openalex-snapshot
The Promise of Foundational Large Language Models in Analysis and Interpretation of Wearable Data: Implications for Physical Behavior Research
inferred← citeshttps://doi.org/10.1145/3725856openalex-snapshot
Large Language Models Are Zero-Shot Recognizers for Activities of Daily Living
inferred← citeshttps://doi.org/10.1145/3736425.3771957openalex-snapshot
LLM-Powered Data Annotation for Bridging the Semantic Gap in Air Quality Monitoring
inferred← citeshttps://doi.org/10.1145/3766900openalex-snapshot
Leveraging Large Language Models for Explainable Activity Recognition in Smart Homes: A Critical Evaluation
inferred← citeshttps://doi.org/10.1145/3772318.3791428openalex-snapshot
Who Does What? Archetypes of Roles Assigned to LLMs During Human-AI Decision-Making
inferred← citeshttps://doi.org/10.1515/icom-2024-0072openalex-snapshot
ChatAnalysis revisited: can ChatGPT undermine privacy in smart homes with data analysis?
inferred← citeshttps://doi.org/10.3390/bioengineering13010120openalex-snapshot
HybridSense-LLM: A Structured Multimodal Framework for Large-Language-Model–Based Wellness Prediction from Wearable Sensors with Contextual Self-Reports
inferred← citeshttps://doi.org/10.3390/s24155045openalex-snapshot
Large Language Models for Wearable Sensor-Based Human Activity Recognition, Health Monitoring, and Behavioral Modeling: A Survey of Early Trends, Datasets, and Challenges
inferred← citeshttps://doi.org/10.3390/s25113324openalex-snapshot
Few-Shot Optimization for Sensor Data Using Large Language Models: A Case Study on Fatigue Detection
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A Large Language Model-Guided IMU-PDR Method for Detection and Error Correction
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