首页|Research Conducted at University of Southern California (USC) Has Updated Our Kn owledge about Machine Learning (Stress Appraisal In the Workplace and Its Associ ations With Productivity and Mood: Insights From a Multimodal Machine Learning . ..)
Research Conducted at University of Southern California (USC) Has Updated Our Kn owledge about Machine Learning (Stress Appraisal In the Workplace and Its Associ ations With Productivity and Mood: Insights From a Multimodal Machine Learning . ..)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Data detailed on Machine Learning have been presented. According to news reporting from Los Angeles, California, by Ne wsRx journalists, research stated, "Previous studies have primarily focused on p redicting stress arousal, encompassing physiological, behavioral, and psychologi cal responses to stressors, while neglecting the examination of stress appraisal . Stress appraisal involves the cognitive evaluation of a situation as stressful or non-stressful, and as a threat/pressure or a challenge/opportunity." Funders for this research include National Science Foundation, National Science Foundation, Army Research Office, Pilot Project Research Training Program of the Southern California NIOSH Education and Research Center. The news correspondents obtained a quote from the research from the University o f Southern California (USC), "In this study, we investigated several research qu estions related to the association between states of stress appraisal (i.e., bor edom, eustress, coexisting eustress-distress, distress) and various factors such as stress levels, mood, productivity, physiological and behavioral responses, a s well as the most effective ML algorithms and data signals for predicting stres s appraisal. The results support the Yerkes-Dodson law, showing that a moderate stress level is associated with increased productivity and positive mood, while low and high levels of stress are related to decreased productivity and negative mood, with distress overpowering eustress when they coexist. Changes in stress appraisal relative to physiological and behavioral features were examined throug h the lenses of stress arousal, activity engagement, and performance. An XGBOOST model achieved the best prediction accuracies of stress appraisal, reaching 82. 78% when combining physiological and behavioral features and 79.55 % using only the physiological dataset. The small accuracy differe nce of 3% indicates that physiological data alone may be adequate to accurately predict stress appraisal, and the feature importance results ident ified electrodermal activity, skin temperature, and blood volume pulse as the mo st useful physiologic features. Implementing these models within work environmen ts can serve as a foundation for designing workplace policies, practices, and st ress management strategies that prioritize the promotion of eustress while reduc ing distress and boredom."
Los AngelesCaliforniaUnited StatesNorth and Central AmericaCyborgsEmerging TechnologiesMachine LearningUni versity of Southern California (USC)
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