AI-Optimized Training Recommendations with Biometric Data for HR and Finance Excellence

Abstract

The amalgamation of artificial intelligence (AI) and biometric data is revolutionizing training suggestions in the fields of HR and Finance. This study presents an AI-optimized training system that uses deep learning and reinforcement learning algorithms to evaluate biometric data from wearable devices. The data includes heart rate, stress levels, activity levels, and sleep patterns. The technology generates customized training programs and adapts them in real-time depending on immediate feedback. The training dataset comprises heart rate measurements (70 ± 10 bpm), stress level readings (5 ± 2 GSR units), activity level data (5000 ± 2000 steps), and sleep duration records (7 ± 1 hours). The model attained the model achieved an accuracy rate of 92%, a precision rate of 91%, a recall rate of 89%, and an F1-score of 90%, surpassing the expected performance of earlier systems. In addition, it enhanced employee engagement and performance by 15% and decreased training time by 20%. These findings emphasize the system's efficacy in improving training outcomes and its potential influence on HR and Finance. Subsequent efforts will prioritize enlarging the dataset, using sophisticated AI methods, and resolving ethical considerations related to biometric data.

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