lightbulbPractical problemTime Series Semantic Retrieval and Trend Analysis
Welding engineers struggle to efficiently analyze multidimensional time series data, hindering quality monitoring.Direction: Visualization, Analytics, and Data Understanding
Time Series Semantic Retrieval and Trend Analysis
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27 items
lightbulbPractical problemTime Series Semantic Retrieval and Trend Analysis
Decision-makers and the public struggle to identify misleading charts in time-series data.lightbulbPractical problemTime Series Semantic Retrieval and Trend Analysis
Non-technical experts struggle to participate in time-series model design, limiting practical application.lightbulbPractical problemTime Series Semantic Retrieval and Trend Analysis
Users struggle to precisely find needed trend features when analyzing time series data with tools.lightbulbPractical problemTime Series Semantic Retrieval and Trend Analysis
Doctors and users struggle to understand the rationale behind time-series classification results.UbiComp '24Time for an Explanation: A Mini-Review of Explainable Physio-Behavioural Time-Series Classification
lightbulbPractical problemTime Series Semantic Retrieval and Trend Analysis
Wearable devices have insufficient accuracy and robustness for complex activity recognition.UbiComp '24rTsfNet: A DNN Model with Multi-head 3D Rotation and Time Series Feature Extraction for IMU-based Human Activity Recognition
lightbulbPractical problemTime Series Semantic Retrieval and Trend Analysis
Doctors struggle to accurately understand patient emotions in complex emotional scenarios.UbiComp '24pSCOUTER: Time-Series Emotion Classification Using Contactless Measured Multimodal Biosignals in Medical Diagnosis
lightbulbPractical problemTime Series Semantic Retrieval and Trend Analysis
Stress detection on wearables is resource-intensive, limiting real-time monitoring and privacy.UbiComp '24TinyStressNAS: Automated Feature Selection and Model Generation for On-device Stress Detection
lightbulbPractical problemTime Series Semantic Retrieval and Trend Analysis
Users struggle to achieve precise thermal comfort experiences under dynamically changing temperatures.lightbulbPractical problemTime Series Semantic Retrieval and Trend Analysis
VR users often cannot achieve immersive experiences due to cybersickness (e.g., eye strain, dizziness).UbiComp '24Early Prediction of Cybersickness in Virtual Reality Using a Large Language Model for Multimodal Time Series Data
lightbulbPractical problemTime Series Semantic Retrieval and Trend Analysis
Automated 3D pose reconstruction methods have large errors in complex motions and occlusions, failing to meet animation and motion analysis needs.lightbulbPractical problemTime Series Semantic Retrieval and Trend Analysis
VR users often miss important notifications, affecting experience and task completion.lightbulbPractical problemTime Series Semantic Retrieval and Trend Analysis
Semantic labels are inconsistent across teams in large organizations, hindering analysis efficiency and label standardization.lightbulbPractical problemTime Series Semantic Retrieval and Trend Analysis
Users struggle to accurately predict mental health conditions through wearable devices.UbiComp '23Can Data Augmentation Improve Daily Mood Prediction from Wearable Data? An Empirical Study
lightbulbPractical problemTime Series Semantic Retrieval and Trend Analysis
Factories struggle to verify whether employees complete packaging tasks as required.UbiComp '23Human activity recognition for packing processes using CNN-biLSTM
lightbulbPractical problemTime Series Semantic Retrieval and Trend Analysis
Wearable devices require frequent battery replacement, which is inconvenient and environmentally harmful.UbiComp '23Eco-Friendly Sensing for Human Activity Recognition
lightbulbPractical problemTime Series Semantic Retrieval and Trend Analysis
Existing smart devices struggle to efficiently and accurately recognize users' transportation modes.UbiComp '23Enhancing Transportation Mode Detection using Multi-scale Sensor Fusion and Spatial-topological Attention
lightbulbPractical problemTime Series Semantic Retrieval and Trend Analysis
Smartwatches struggle to accurately recognize gestures in dynamic usage environments.lightbulbPractical problemTime Series Semantic Retrieval and Trend Analysis
In VR, recognition errors and user errors degrade interaction experience, and existing systems struggle to quickly classify error types.lightbulbPractical problemTime Series Semantic Retrieval and Trend Analysis
Existing models for multimodal sensor data are inefficient and require extensive annotation.UbiComp '22COCOA: Cross Modality Contrastive Learning for Sensor Data
lightbulbPractical problemTime Series Semantic Retrieval and Trend Analysis
Traditional psychological screening tools require active user participation and may be biased, making them unsuitable for large-scale deployment.UbiComp '22DepreST-CAT: Retrospective Smartphone Call and Text Logs Collected during the COVID-19 Pandemic to Screen for Mental Illnesses
lightbulbPractical problemTime Series Semantic Retrieval and Trend Analysis
Blind users struggle to obtain trends and insights from complex data through visualization tools.lightbulbPractical problemTime Series Semantic Retrieval and Trend Analysis
Users waste substantial time on time series data annotation due to label fragmentation.UbiComp '21Reducing Label Fragmentation During Time-series Data Annotation to Reduce Annotation Costs
lightbulbPractical problemTime Series Semantic Retrieval and Trend Analysis
Existing methods struggle to accurately identify indoor or low-speed transportation modes such as walking and bus travel.UbiComp '21Transition-points-based Segmentation and Hierarchical Classification for Locomotion and Transportation Recognition on Radio-data
lightbulbPractical problemTime Series Semantic Retrieval and Trend Analysis
Increasing scale and complexity of sensor networks lead to connectivity problems and degraded communication quality.UbiComp '21Data-driven Clustering in Ad-hoc Networks based on Community Detection
lightbulbPractical problemTime Series Semantic Retrieval and Trend Analysis
Wearable device activity recognition requires large amounts of labeled data that are difficult to obtain.UbiComp '21Contrastive Predictive Coding for Human Activity Recognition
lightbulbPractical problemTime Series Semantic Retrieval and Trend Analysis
Domain experts struggle to compare performance and feature impacts across different time series prediction models.Related papers
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