Multimodal Sensing for Mental States
What You Need to Know Before You Apply
What is the purpose of this trial?
This trial aims to develop new tools for understanding mental states using advanced technology, such as wearable sensors and virtual interactions. The focus is on creating tools to monitor thoughts and feelings in everyday social settings and beyond. Two groups are involved: healthy adults and patients with drug-resistant epilepsy who already have special brain monitoring devices. Ideal candidates include healthy adults who can give consent and epilepsy patients with implanted electrodes. As an unphased study, this trial offers participants the opportunity to contribute to groundbreaking research that could revolutionize mental health monitoring.
Will I have to stop taking my current medications?
The trial information does not specify whether you need to stop taking your current medications. It seems likely that you can continue your current treatment, especially since the study involves patients with drug-resistant epilepsy who already have implanted electrodes.
What prior data suggests that these monitoring tools are safe for use in humans?
Research has shown that using wearable sensors and machine learning to monitor mental states is generally safe. Studies have found that these devices can track stress and mental fatigue by collecting information from the body, such as heart rate and skin temperature, without causing harm.
These sensors are commonly used in everyday life and are well-tolerated by most people. Reports of major side effects from these technologies do not exist. Designed to be non-invasive, they do not require surgery or anything entering the body, which enhances their safety.
Overall, this technology has been used widely in similar studies without safety issues. Based on past research, participants can feel confident about the safety of these tools.12345Why are researchers excited about this trial?
Researchers are excited about this trial because it explores a cutting-edge approach to understanding mental states using a combination of neural, physiological, and behavioral sensing, all enhanced by machine learning technology. Unlike traditional methods that often rely on subjective assessments or singular data streams, this multimodal technique integrates various data types to provide a more comprehensive and objective understanding of mental health conditions. This could lead to more accurate diagnoses and personalized treatments, offering a significant leap forward in mental health care.
What evidence suggests that these multimodal sensing tools are effective for assessing mental states?
Research has shown that advanced technology, which combines different types of data, is transforming the detection and monitoring of mental health disorders. These systems gather information from various sources, such as sensors that track physical and chemical signals, to enhance understanding of mental health. For instance, one study examined 184 cases where technology identified mental health issues by analyzing data like speech and behavior. Early results suggested that these methods can detect mental disorders early, potentially improving management. By integrating various data types, these new tools aim to provide a more complete picture of mental health, facilitating the identification of problems before they become serious. Participants in this trial will engage in behavioral tasks to assess mental states using novel multimodal neural, physiological, and behavioral sensing, along with machine learning techniques.26789
Who Is on the Research Team?
Maryam Shanechi, PhD
Principal Investigator
University of Southern California
Are You a Good Fit for This Trial?
This trial is for healthy individuals and patients with drug-resistant epilepsy who have intracranial EEG electrodes implanted for seizure monitoring. Participants should be able to undergo a cold pressor test and interact with a virtual human platform.Inclusion Criteria
Timeline for a Trial Participant
Screening
Participants are screened for eligibility to participate in the trial
Experimental Assessment
Participants undergo behavioral tasks and self-report assessments using various scales to measure mental states
Neural and Physiological Monitoring
In epilepsy patients, iEEG activity measurements are obtained; physiological data such as heart rate variability, skin conductance, and cortisol levels are collected
Follow-up
Participants are monitored for any delayed effects or additional data collection post-assessment
What Are the Treatments Tested in This Trial?
Interventions
- Novel Multimodal Neural, Physiological, and Behavioral Sensing and Machine Learning for Mental States
Trial Overview
The study is testing new tools that include wearable sensors, virtual human interactions, audiovisual recognition software, and machine learning to monitor mental states during social processes in both healthy subjects and epilepsy patients.
How Is the Trial Designed?
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Treatment groups
Experimental Treatment
Find a Clinic Near You
Who Is Running the Clinical Trial?
University of Southern California
Lead Sponsor
National Institute of Mental Health (NIMH)
Collaborator
Citations
Multimodal Machine Learning in Mental Health: A Survey ...
Multimodal machine learning (MML) is rapidly reshaping the way mental-health disorders are detected, characterized, and longitudinally monitored.
Evaluating machine learning-enabled and multimodal data ...
This study aims to develop and evaluate a multimodal data-driven AI system for personalized exercise prescriptions, targeting individuals with mental illnesses.
Mental health monitoring with multimodal sensing and ...
This paper surveys recent research works in mental health monitoring systems (MHMS) using sensor data and machine learning.
Machine Learning for Multimodal Mental Health Detection
We systematically reviewed 184 studies to assess feature extraction, feature fusion, and ML methodologies applied to detect MH disorders from passively sensed ...
Early detection of mental health disorders using machine ...
This study presents a novel framework for the early detection of mental illness disorders using a multi-modal approach combining speech and behavioral data.
A deep learning approach to stress recognition through ...
We propose a novel approach using deep learning models that classify mental stress states (stress, baseline, amusement) based on multimodal physiological ...
Multimodal integration for data-driven classification of ...
This study proposes a novel approach using three machine learning models and multimodal data fusion to classify mental fatigue states.
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researchgate.net
researchgate.net/publication/382911360_A_Novel_Integrating_Multimodel_Sensor_Data_with_Machine_Learning_for_Stress_DetectionA Novel Integrating Multimodel Sensor Data with Machine ...
Thisstudy aimed to investigate using indicators from wearable devices wornon the wrist to predict stress levels. We analyzed data from the ...
Multi-modal deep-attention-BiLSTM based early detection ...
Addressing this gap, this study proposes a multi-modal deep learning framework that integrates both linguistic and temporal features from social ...
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