Unmasking Parkinson's Disease with Smile: An AI-enabled Screening Framework

no code implementations • 3 Aug 2023 • Tariq Adnan, Md Saiful Islam, Wasifur Rahman, Sangwu Lee, Sutapa Dey Tithi, Kazi Noshin, Imran Sarker, M Saifur Rahman, Ehsan Hoque

Parkinson's disease (PD) diagnosis remains challenging due to lacking a reliable biomarker and limited access to clinical care.

Using AI to Measure Parkinson's Disease Severity at Home

no code implementations • 30 Mar 2023 • Md Saiful Islam, Wasifur Rahman, Abdelrahman Abdelkader, Phillip T. Yang, Sangwu Lee, Jamie L. Adams, Ruth B. Schneider, E. Ray Dorsey, Ehsan Hoque

We present an artificial intelligence system to remotely assess the motor performance of individuals with Parkinson's disease (PD).

TextMI: Textualize Multimodal Information for Integrating Non-verbal Cues in Pre-trained Language Models

no code implementations • 27 Mar 2023 • Md Kamrul Hasan, Md Saiful Islam, Sangwu Lee, Wasifur Rahman, Iftekhar Naim, Mohammed Ibrahim Khan, Ehsan Hoque

Our approach, TextMI, significantly reduces model complexity, adds interpretability to the model's decision, and can be applied for a diverse set of tasks while achieving superior (multimodal sarcasm detection) or near SOTA (multimodal sentiment analysis and multimodal humor detection) performance.

Humor Detection Multimodal Sentiment Analysis +1

Auto-Gait: Automatic Ataxia Risk Assessment with Computer Vision on Gait Task Videos

1 code implementation • 15 Mar 2022 • Wasifur Rahman, Masum Hasan, Md Saiful Islam, Titilayo Olubajo, Jeet Thaker, Abdelrahman Abdelkader, Phillip Yang, Tetsuo Ashizawa, Ehsan Hoque

In this paper, we investigated whether we can 1) detect participants with ataxia-specific gait characteristics (risk-prediction), and 2) assess severity of ataxia from gait (severity-assessment) using computer vision.

Clinical Knowledge Feature Importance

DBATES: DataBase of Audio features, Text, and visual Expressions in competitive debate Speeches

no code implementations • 26 Mar 2021 • Taylan K. Sen, Gazi Naven, Luke Gerstner, Daryl Bagley, Raiyan Abdul Baten, Wasifur Rahman, Kamrul Hasan, Kurtis G. Haut, Abdullah Mamun, Samiha Samrose, Anne Solbu, R. Eric Barnes, Mark G. Frank, Ehsan Hoque

In this work, we present a database of multimodal communication features extracted from debate speeches in the 2019 North American Universities Debate Championships (NAUDC).

Detecting Parkinson's Disease From an Online Speech-task

no code implementations • 2 Sep 2020 • Wasifur Rahman, Sangwu Lee, Md. Saiful Islam, Victor Nikhil Antony, Harshil Ratnu, Mohammad Rafayet Ali, Abdullah Al Mamun, Ellen Wagner, Stella Jensen-Roberts, Max A. Little, Ray Dorsey, Ehsan Hoque

In this paper, we envision a web-based framework that can help anyone, anywhere around the world record a short speech task, and analyze the recorded data to screen for Parkinson's disease (PD).

Integrating Multimodal Information in Large Pretrained Transformers

1 code implementation • ACL 2020 • Wasifur Rahman, Md. Kamrul Hasan, Sangwu Lee, Amir Zadeh, Chengfeng Mao, Louis-Philippe Morency, Ehsan Hoque

It does so by generating a shift to internal representation of BERT and XLNet; a shift that is conditioned on the visual and acoustic modalities.

Multimodal Sentiment Analysis Question Answering

UR-FUNNY: A Multimodal Language Dataset for Understanding Humor

no code implementations • IJCNLP 2019 • Md. Kamrul Hasan, Wasifur Rahman, Amir Zadeh, Jianyuan Zhong, Md. Iftekhar Tanveer, Louis-Philippe Morency, Mohammed, Hoque

The dataset and accompanying studies, present a framework in multimodal humor detection for the natural language processing community.

Humor Detection