Mentor Areas
- Bioelectronic systems
- Device fabrication
- Biosensors
- Materials chemistry
Description:
The current standard-of-care for mental health assessment based on psychiatric consultation and questionnaires have several important limitations. The episodic nature of consultations fails to capture the continuous nature of mental health fluctuations. Self-report measures are inherently subjective and may be influenced by a person's current mood, willingness to report accurately, or even their understanding of the questions. As a result, these traditional approaches often fall short in swiftly identifying deteriorating mental health conditions, leading to delayed interventions and suboptimal outcomes. Recognizing the unmet clinical need for real-time and objective mental health monitoring, we propose a groundbreaking solution by developing a non-invasive wearable biosensor system empowered by artificial intelligence specifically tailored to analyze and predict mental health status. In contrast to existing episodic and reactive care models, our system strategically employs cutting-edge wearable technologies to ceaselessly gather a plethora of data related to objective mental health biomarkers such as hormones and metabolites, along with physiological responses such as speech patterns and behavioral indicators. Besides the high-accuracy biosensors, the as-collected data set will be further categorized and analyzed through algorithms based on artificial neural networks. Specifically, machine learning and deep learning algorithms can efficiently use statistical techniques to sift through biomarker data collected by sensor, identifying trends and anomalies of biomarker level, deciphering intricate patterns and correlations between biomarkers and mental states, while constantly refining their predictive accuracy. Together, the entire system will enable early detection of mental illness and timely interventions that will significantly enhance the current standard-of-care.
Preferred Qualifications
- Curiosity
- Commitment
- Professionalism
- Collegiality
Project Website
Learn more about the researcher and/or the project here. https://jianggroup.seas.upenn.edu/
Details:
Preferred Student Year
First-year, Second-Year, Junior, Senior
Academic Term
Fall, Spring, Summer
I prefer to have students start during the above term(s).Volunteer
Yes
Yes indicates that faculty are open to volunteers.Paid
No
Yes indicates that faculty are open to paying students they engage in their research, regardless of their work-study eligibility.Work Study
No
Yes indicates that faculty are open to hiring work-study-eligible students.