首页|Study Data from University of Tennessee Provide New Insights into Machine Learni ng (Utilizing Machine Learning for Context-Aware Digital Biomarker of Stress in Older Adults)
Study Data from University of Tennessee Provide New Insights into Machine Learni ng (Utilizing Machine Learning for Context-Aware Digital Biomarker of Stress in Older Adults)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Investigators discuss new findings in artificial intelligence. According to news reporting originating from Knoxville, Tennessee, by NewsRx correspondents, research stated, "Identifying stress in ol der adults is a crucial field of research in health and well-being. This allows us to take timely preventive measures that can help save lives." Funders for this research include University of Tennessee At Knoxville. Our news journalists obtained a quote from the research from University of Tenne ssee: "That is why a nonobtrusive way of accurate and precise stress detection i s necessary. Researchers have proposed many statistical measurements to associat e stress with sensor readings from digital biomarkers. With the recent progress of Artificial Intelligence in the healthcare domain, the application of machine learning is showing promising results in stress detection. Still, the viability of machine learning for digital biomarkers of stress is under-explored. In this work, we first investigate the performance of a supervised machine learning algo rithm (Random Forest) with manual feature engineering for stress detection with contextual information. The concentration of salivary cortisol was used as the g olden standard here. Our framework categorizes stress into No Stress, Low Stress , and High Stress by analyzing digital biomarkers gathered from wearable sensors . We also provide a thorough knowledge of stress in older adults by combining ph ysiological data obtained from wearable sensors with contextual clues from a str ess protocol. Our context-aware machine learning model, using sensor fusion, ach ieved a macroaverage F-1 score of 0.937 and an accuracy of 92.48% in identifying three stress levels. We further extend our work to get rid of the burden of manual feature engineering. We explore Convolutional Neural Network ( CNN)-based feature encoder and cortisol biomarkers to detect stress using contex tual information."
University of TennesseeKnoxvilleTenn esseeUnited StatesNorth and Central AmericaCyborgsEmerging TechnologiesEngineeringMachine Learning
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