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Visualization, Analytics, and Data Understanding / Time Series Semantic Retrieval and Trend Analysis

Wearable device activity recognition requires large amounts of labeled data that are difficult to obtain.

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

Similar questions

lightbulbPractical problemTime Series Semantic Retrieval and Trend Analysis

Welding engineers struggle to efficiently analyze multidimensional time series data, hindering quality monitoring.

IUI '25Cluster-Based Approach for Visual Anomaly Detection in Multivariate Welding Process Data Supported by User Guidance

lightbulbPractical problemTime Series Semantic Retrieval and Trend Analysis

Decision-makers and the public struggle to identify misleading charts in time-series data.

CHI '25Visualization Guardrails: Designing Interventions Against Cherry-Picking in Interactive Data Explorers

lightbulbPractical problemTime Series Semantic Retrieval and Trend Analysis

Non-technical experts struggle to participate in time-series model design, limiting practical application.

CHI '25Tempo: Helping Data Scientists and Domain Experts Collaboratively Specify Predictive Modeling Tasks

lightbulbPractical problemTime Series Semantic Retrieval and Trend Analysis

Users struggle to precisely find needed trend features when analyzing time series data with tools.

IUI '24SlopeSeeker: A Search Tool for Exploring a Dataset of Quantifiable Trends

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

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