iBVP Dataset: RGB-Thermal rPPG Dataset

iBVP Dataset: RGB-Thermal rPPG Dataset

The iBVP dataset is a collection of synchronized RGB and thermal infrared videos with PPG ground-truth signals acquired from an ear. It includes manual signal quality labels and dense signal-quality assessment using the SQA-PhysMD model. The dataset is designed to induce real-world variations in psycho-physiological states and head movement.

iBVP Dataset: RGB-Thermal rPPG Dataset

詳細な紹介

The iBVP dataset, introduced in the paper 'iBVP Dataset: RGB-Thermal rPPG Dataset with High Resolution Signal Quality Labels', provides a comprehensive set of synchronized RGB and thermal videos with PPG signals. It is ideal for researchers working on remote photoplethysmography (rPPG) and signal quality assessment. The dataset is available for academic research purposes and can be requested by submitting a signed EULA.

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Mental Health in Tech Survey
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Mental Health in Tech Survey

This dataset contains survey responses from individuals in the tech industry about their mental health, including questions about treatment, workplace resources, and attitudes towards discussing mental health in the workplace. By analyzing this dataset, we can better understand how prevalent mental health issues are among those who work in the tech sector—and what kinds of resources they rely upon to find help—so that more can be done to create a healthier working environment for all.

HeartLink - Empathetic Psychological Model
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HeartLink - Empathetic Psychological Model

HeartLink is an empathetic psychological model that uses a large language model fine-tuned on a large empathetic Q&A dataset. It can perceive users' emotions and experiences during conversations and provide empathetic responses using rich psychological knowledge, aiming to understand, comfort, and support users. The responses include emoji expressions to bridge the gap with users, offering psychological support and help during consultations.

IC-AnnoMI: In-Context MI Dialogues - GitHub Repository
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IC-AnnoMI: In-Context MI Dialogues - GitHub Repository

The IC-AnnoMI repository contains source code and a synthetic dataset generated through in-context zero-shot LLM prompting for mental health and therapeutic counselling. IC-AnnoMI is a project that generates contextual MI dialogues using large language models (LLMs). The project contains source code and a synthetic dataset generated through zero-shot prompts, aiming to address the data scarcity and inherent bias problems in mental health and therapeutic consultation.

キーワード

iBVP DatasetRGB-ThermalrPPGSignal QualityPPGSQA-PhysMDElectronics 2024

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