Biosensor-based strategies for promoting innovation and assessing occupational health risks among college students
Abstract
Higher education was increasingly emphasizing innovation and entrepreneurship strategy, encouraging college students to develop creative talents. The intense needs of both academic and entrepreneurial tasks provide serious occupational health hazards that affect students’ physical and emotional health. This paper provides a novel approach to risk assessment and management that combines biosensor technology and artificial intelligence (AI). The emphasis is on applying a novel Flower Pollination Optimizer Tuned Gate refined Long Short Term Memory (FPO-GLSTM) method to monitor and forecast health issues, such as stress, physical strain, and other risk factors in education and entrepreneurship situations. Data were gathered using biosensors that monitored physiological characteristics, such as heart rate, blood pressure, and stress levels in real-time. To capture crucial health information, the data was pre-processed with min-max normalization, and features were extracted using the Discrete Wavelet Transform (DWT). The FPO-GLSTM model to forecast potential health hazards and make individualized recommendations. The outcomes demonstrate that the FPO-GLSTM-based model accurately and precisely forecasts health risks, including stress-induced conditions. AI and biosensor data integration offer a viable way to monitor and manage health risks for students, improving their health in educational and entrepreneurial environments.
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