Publications by authors named "P G Suresha"
Global warming-induced abiotic stresses, such as waterlogging, significantly threaten crop yields. Increased rainfall intensity in recent years has exacerbated waterlogging severity, especially in lowlands and heavy soils. Its intensity is projected to increase by 14-35% in the future, posing a serious risk to crop production and the achievement of sustainable development goals.
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- - The study investigates how the quality of sleep in individuals with Autism Spectrum Disorder (ASD) affects their behavior the following day, focusing on severe daytime challenges like aggression and self-injury.
- - Over two years, data from 14 individuals was gathered using a low-cost, privacy-friendly camera, with a total of over 2,000 nights recorded and analyzed for sleep patterns versus daytime behaviors.
- - An advanced machine learning model was developed, achieving 74% accuracy in predicting morning behaviors, suggesting that monitoring sleep quality could lead to better behavioral management and support for individuals with ASD.
Rett syndrome, a rare genetic neurodevelopmental disorder in humans, does not have an effective cure. However, multiple therapies and medications exist to treat symptoms and improve patients' quality of life. As research continues to discover and evaluate new medications for Rett syndrome patients, there remains a lack of objective physiological and motor activity-based (physio-motor) biomarkers that enable the measurement of the effect of these medications on the change in patients' Rett syndrome severity.
View Article and Find Full Text PDFThe non-contact patient monitoring paradigm moves patient care into their homes and enables long-term patient studies. The challenge, however, is to make the system non-intrusive, privacy-preserving, and low-cost. To this end, we describe an open-source edge computing and ambient data capture system, developed using low-cost and readily available hardware.
View Article and Find Full Text PDFBackground: The retrospective analysis of electroencephalogram (EEG) signals acquired from patients under general anesthesia is crucial in understanding the patient's unconscious brain's state. However, the creation of such database is often tedious and cumbersome and involves human labor. Hence, we developed a Raspberry Pi-based system for archiving EEG signals recorded from patients under anesthesia in operating rooms (ORs) with minimal human involvement.
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