Green Reinforcement Learning for Adaptive Mental Health Support Systems using multimodal recognition

The advancement in the blending computer technology, Spatial Computing, machine learning into mental health wellbeing has enhanced individualized care and management, early diagnosis, and constant tracking of client’s mental wellbeing. However, AI models and machine learning often require prominent algorithmic operations, computational procedures, which results in flexibility and reliability concern. This research paper is a qualitative study that examines the progression and significance of a Green Reinforcement Learning (GRL) model for psychological support systems combining multimodal recognition techniques.This study inquires how endurable AI techniques can be used in mental health clinical settings to decrease power expenditure while increasing sensibility to clients’ excitable and intellectual states of mind. In this study, semi-structured interview methods are used to collect data, which is developed with the help of AI tools, professionals, psychologist and the use of other psychological as well as digital assessment tools.The suggested paper applies behavioral reinforcement techniques to compare the likely outcome and energy consumption, emancipating actual therapy with reduced environmental and operational cost. Findings suggest that GRL can promote both ecological and emotional sustainability, offering adaptive interventions that align with psychological and community needs.

• Publication year

  2025

• Venue

  2025 2nd International Conference on Artificial Intelligence for Innovations in Healthcare Industries (ICAIIHI)

• Publication date

  2025-12-04

• Fields of study

  Not labeled

• Identifiers
  DOI 10.1109/ICAIIHI67124.2025.11403592
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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