Search Results for author: Amir M. Rahmani

Found 27 papers, 1 papers with code

Graph-Augmented LLMs for Personalized Health Insights: A Case Study in Sleep Analysis

no code implementations24 Jun 2024 Ajan Subramanian, Zhongqi Yang, Iman Azimi, Amir M. Rahmani

Health monitoring systems have revolutionized modern healthcare by enabling the continuous capture of physiological and behavioral data, essential for preventive measures and early health intervention.

Feature Importance RAG

Empathy Through Multimodality in Conversational Interfaces

no code implementations8 May 2024 Mahyar Abbasian, Iman Azimi, Mohammad Feli, Amir M. Rahmani, Ramesh Jain

Agents represent one of the most emerging applications of Large Language Models (LLMs) and Generative AI, with their effectiveness hinging on multimodal capabilities to navigate complex user environments.

Emotional Intelligence Multimodal Emotion Recognition +1

ALCM: Autonomous LLM-Augmented Causal Discovery Framework

no code implementations2 May 2024 Elahe Khatibi, Mahyar Abbasian, Zhongqi Yang, Iman Azimi, Amir M. Rahmani

This study not only shows the effectiveness of the ALCM but also underscores new research directions in leveraging the causal reasoning capabilities of LLMs.

Causal Discovery Causal Inference

Impact of Physical Activity on Quality of Life During Pregnancy: A Causal ML Approach

no code implementations25 Feb 2024 Kianoosh Kazemi, Iina Ryhtä, Iman Azimi, Hannakaisa Niela-Vilen, Anna Axelin, Amir M. Rahmani, Pasi Liljeberg

Our result shows that performing adequate physical activity during pregnancy and postpartum improves the QoL by units of 7. 3 and 3. 4 on average in physical health and psychological domains, respectively.

Causal Discovery Causal Inference +1

ChatDiet: Empowering Personalized Nutrition-Oriented Food Recommender Chatbots through an LLM-Augmented Framework

no code implementations18 Feb 2024 Zhongqi Yang, Elahe Khatibi, Nitish Nagesh, Mahyar Abbasian, Iman Azimi, Ramesh Jain, Amir M. Rahmani

The personal model leverages causal discovery and inference techniques to assess personalized nutritional effects for a specific user, whereas the population model provides generalized information on food nutritional content.

Causal Discovery Food recommendation +1

Differential Private Federated Transfer Learning for Mental Health Monitoring in Everyday Settings: A Case Study on Stress Detection

no code implementations16 Feb 2024 Ziyu Wang, Zhongqi Yang, Iman Azimi, Amir M. Rahmani

Mental health conditions, prevalent across various demographics, necessitate efficient monitoring to mitigate their adverse impacts on life quality.

Federated Learning Privacy Preserving +1

Optimizing Warfarin Dosing Using Contextual Bandit: An Offline Policy Learning and Evaluation Method

no code implementations16 Feb 2024 Yong Huang, Charles A. Downs, Amir M. Rahmani

Warfarin, an anticoagulant medication, is formulated to prevent and address conditions associated with abnormal blood clotting, making it one of the most prescribed drugs globally.

Decision Making

Food Recommendation as Language Processing (F-RLP): A Personalized and Contextual Paradigm

no code implementations12 Feb 2024 Ali Rostami, Ramesh Jain, Amir M. Rahmani

State-of-the-art rule-based and classification-based food recommendation systems face significant challenges in becoming practical and useful.

Food recommendation Recommendation Systems

Robust CNN-based Respiration Rate Estimation for Smartwatch PPG and IMU

no code implementations10 Jan 2024 Kianoosh Kazemi, Iman Azimi, Pasi Liljeberg, Amir M. Rahmani

The increasing popularity of smartwatches, equipped with various sensors including PPG, has prompted the need for a robust RR estimation method.

Context-Aware Stress Monitoring using Wearable and Mobile Technologies in Everyday Settings

no code implementations14 Dec 2023 Seyed Amir Hossein Aqajari, Sina Labbaf, Phuc Hoang Tran, Brenda Nguyen, Milad Asgari Mehrabadi, Marco Levorato, Nikil Dutt, Amir M. Rahmani

We achieved the F1-score of 70\% with a Random Forest classifier using both PPG and contextual data, which is considered an acceptable score in models built for everyday settings.

Reducing Intraspecies and Interspecies Covariate Shift in Traumatic Brain Injury EEG of Humans and Mice Using Transfer Euclidean Alignment

no code implementations3 Oct 2023 Manoj Vishwanath, Steven Cao, Nikil Dutt, Amir M. Rahmani, Miranda M. Lim, Hung Cao

We tested the robustness of this transfer learning technique on various rule-based classical machine learning models as well as the EEGNet-based deep learning model by evaluating on different datasets, including human and mouse data in a binary classification task of detecting individuals with versus without traumatic brain injury (TBI).

Binary Classification EEG +1

Conversational Health Agents: A Personalized LLM-Powered Agent Framework

1 code implementation3 Oct 2023 Mahyar Abbasian, Iman Azimi, Amir M. Rahmani, Ramesh Jain

openCHA includes an orchestrator to plan and execute actions for gathering information from external sources, essential for formulating responses to user inquiries.

Heart Rate Variability

Foundation Metrics for Evaluating Effectiveness of Healthcare Conversations Powered by Generative AI

no code implementations21 Sep 2023 Mahyar Abbasian, Elahe Khatibi, Iman Azimi, David Oniani, Zahra Shakeri Hossein Abad, Alexander Thieme, Ram Sriram, Zhongqi Yang, Yanshan Wang, Bryant Lin, Olivier Gevaert, Li-Jia Li, Ramesh Jain, Amir M. Rahmani

The purpose of this paper is to explore state-of-the-art LLM-based evaluation metrics that are specifically applicable to the assessment of interactive conversational models in healthcare.


Edge-centric Optimization of Multi-modal ML-driven eHealth Applications

no code implementations4 Aug 2022 Anil Kanduri, Sina Shahhosseini, Emad Kasaeyan Naeini, Hamidreza Alikhani, Pasi Liljeberg, Nikil Dutt, Amir M. Rahmani

Smart eHealth applications deliver personalized and preventive digital healthcare services to clients through remote sensing, continuous monitoring, and data analytics.

Efficient Personalized Learning for Wearable Health Applications using HyperDimensional Computing

no code implementations1 Aug 2022 Sina Shahhosseini, Yang Ni, Hamidreza Alikhani, Emad Kasaeyan Naeini, Mohsen Imani, Nikil Dutt, Amir M. Rahmani

Considering the significant role of wearable devices in monitoring human body parameters, on-device learning can be utilized to build personalized models for behavioral and physiological patterns, and provide data privacy for users at the same time.

BIG-bench Machine Learning Privacy Preserving

Online Learning for Orchestration of Inference in Multi-User End-Edge-Cloud Networks

no code implementations21 Feb 2022 Sina Shahhosseini, Dongjoo Seo, Anil Kanduri, Tianyi Hu, Sung-soo Lim, Bryan Donyanavard, Amir M. Rahmani, Nikil Dutt

To this end, we propose a reinforcement-learning-based computation offloading solution that learns optimal offloading policy considering deep learning model selection techniques to minimize response time while providing sufficient accuracy.

Cloud Computing Decision Making +2

Novel Blood Pressure Waveform Reconstruction from Photoplethysmography using Cycle Generative Adversarial Networks

no code implementations24 Jan 2022 Milad Asgari Mehrabadi, Seyed Amir Hossein Aqajari, Amir Hosein Afandizadeh Zargari, Nikil Dutt, Amir M. Rahmani

Continuous monitoring of blood pressure (BP)can help individuals manage their chronic diseases such as hypertension, requiring non-invasive measurement methods in free-living conditions.

Generative Adversarial Network Translation

Personalized Stress Monitoring using Wearable Sensors in Everyday Settings

no code implementations31 Jul 2021 Ali Tazarv, Sina Labbaf, Stephanie M. Reich, Nikil Dutt, Amir M. Rahmani, Marco Levorato

Since stress contributes to a broad range of mental and physical health problems, the objective assessment of stress is essential for behavioral and physiological studies.

Heart Rate Variability Photoplethysmography (PPG)

An Accurate Non-accelerometer-based PPG Motion Artifact Removal Technique using CycleGAN

no code implementations22 Jun 2021 Amir Hosein Afandizadeh Zargari, Seyed Amir Hossein Aqajari, Hadi Khodabandeh, Amir M. Rahmani, Fadi Kurdahi

A photoplethysmography (PPG) is an uncomplicated and inexpensive optical technique widely used in the healthcare domain to extract valuable health-related information, e. g., heart rate variability, blood pressure, and respiration rate.

Generative Adversarial Network Heart Rate Variability +1

An End-to-End and Accurate PPG-based Respiratory Rate Estimation Approach Using Cycle Generative Adversarial Networks

no code implementations3 May 2021 Seyed Amir Hossein Aqajari, Rui Cao, Amir Hosein Afandizadeh Zargari, Amir M. Rahmani

In this paper, we present an end-to-end and accurate pipeline for RR estimation using Cycle Generative Adversarial Networks (CycleGAN) to reconstruct respiratory signals from raw PPG signals.

Photoplethysmography (PPG) Respiratory Rate Estimation

Personal Mental Health Navigator: Harnessing the Power of Data, Personal Models, and Health Cybernetics to Promote Psychological Well-being

no code implementations15 Dec 2020 Amir M. Rahmani, Jocelyn Lai, Salar Jafarlou, Asal Yunusova, Alex. P. Rivera, Sina Labbaf, Sirui Hu, Arman Anzanpour, Nikil Dutt, Ramesh Jain, Jessica L. Borelli

Traditionally, the regime of mental healthcare has followed an episodic psychotherapy model wherein patients seek care from a provider through a prescribed treatment plan developed over multiple provider visits.

Management Sociology

The Causality Inference of Public Interest in Restaurants and Bars on COVID-19 Daily Cases in the US: A Google Trends Analysis

no code implementations27 Jul 2020 Milad Asgari Mehrabadi, Nikil Dutt, Amir M. Rahmani

Our results showed for states/territories with higher numbers of daily cases, the historical trends in search queries related to bars and restaurants, which mainly happened after re-opening, significantly affect the daily new cases, on average.

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