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In an age characterized by the widespread adoption of wearable technology and growing awareness of mental health issues, this presentation introduces an innovative method for predicting and addressing mental states in realtime. Our study utilizes data from wearable sensors like heart rate monitors and oxygen saturation sensors to continuously track individuals’ physiological reactions. We suggest a comprehensive system that merges AutoRegressive Integrated Moving Average (ARIMA) modeling with a Random Forest-Based Ensemble Approach to offer instantaneous understanding of users’ mental conditions.

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Predicting and Addressing Mental States in Real-Time Using Wearable Vital Data

  • 会议论文

  • 发表日期：

    2026年

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  • Nagaratna P. Hegde,
  • Sireesha Vikkurty,
  • Guru Sai Shreesh,
  • Tarun Krishna Dodda

摘要

In an age characterized by the widespread adoption of wearable technology and growing awareness of mental health issues, this presentation introduces an innovative method for predicting and addressing mental states in realtime. Our study utilizes data from wearable sensors like heart rate monitors and oxygen saturation sensors to continuously track individuals’ physiological reactions. We suggest a comprehensive system that merges AutoRegressive Integrated Moving Average (ARIMA) modeling with a Random Forest-Based Ensemble Approach to offer instantaneous understanding of users’ mental conditions.