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Found 6 papers, 2 papers with code
Evaluating Large Language Models as Virtual Annotators for Time-series Physical Sensing Data
Motivated by this observation, we perform a detailed study in this paper to assess whether the state-of-the-art (SOTA) LLMs can be used as virtual annotators for labeling time-series physical sensing data.
"Filling the Blanks'': Identifying Micro-activities that Compose Complex Human Activities of Daily Living
Naturally, a deeper understanding of these micro-activities can help develop more sophisticated human activity recognition (HAR) models and add explainability to their inferred conclusions.
Enhancing Efficiency in Multidevice Federated Learning through Data Selection
Federated learning (FL) in multidevice environments creates new opportunities to learn from a vast and diverse amount of private data.
AQuaMoHo: Localized Low-Cost Outdoor Air Quality Sensing over a Thermo-Hygrometer
Efficient air quality sensing serves as one of the essential services provided in any recent smart city.
Accoustate: Auto-annotation of IMU-generated Activity Signatures under Smart Infrastructure
This paper exploits the acoustic signatures generated from human activities to label the wearables' IMU data at the edge, thus mitigating resource requirement and data privacy concerns.
Exploiting Multi-modal Contextual Sensing for City-bus's Stay Location Characterization: Towards Sub-60 Seconds Accurate Arrival Time Prediction
However, providing prior information for transportation systems like public buses in real-time is inherently challenging because of the diverse nature of different stay-locations that a public bus stops.
