A mental health monitoring system based on intelligent algorithms and biosensors: Algorithm behaviour analysis and intervention strategies

Introduction: Mental health monitoring encompasses the systematic observation and assessment of an individual’s psychological well-being, aiming to detect, understand and manage mental health conditions. It involves various techniques and interventions tailored to support individuals in achieving and maintaining optimal mental wellness. Recent advancements include the use of biosensors and biomechanics to analyze physiological signals that correlate with mental states, providing a more comprehensive understanding of psychological well-being. Aim: The aim of this research is to construct an innovative mental health monitoring system established on intelligent algorithms and biomechanical data through behaviour analysis and intervention strategies. Methodology: We propose a novel Snow Ablation-driven Bi-directional Fine-tuned Recurrent Neural Network (SA-BFRNN) to identify the state of mental health. In addition to behavioural data, biosensors are employed to collect real-time physiological signals such as heart rate variability, skin conductance, and muscle tension, offering objective markers of mental stress and anxiety. These biomechanical inputs are integrated into the system for multi-modal analysis. We employ the SA algorithm, iteratively removing less influential connections and nodes based on their impact on model performance. This process enhances network efficiency and generalization capabilities, refining the BFRNN for mental health state identification. Utilizing a questionnaire with 25 questions, administered to a selected group of 756 individuals, we validate our proposed model. Biosensor data is synchronized with questionnaire responses to improve the precision of mental state identification. Clustering-derived labels are validated with mean opinion score. These labels inform classifiers for individual mental health prediction, aligning with our objective of robust mental health assessment through data-driven approaches. SA-BFRNN integrates both forward and backward temporal information, enhancing its ability to discern subtle patterns in behaviour. Through iterative fine-tuning, our network learns to adapt to diverse datasets, enabling precise identification of mental health states. Research findings: In the result evaluation phase, we thoroughly examine how well our proposed SA-BFRNN model recognizes various states of mental health across different parameters. Our findings also highlight the significance of incorporating biomechanics, where biosensor data showed a strong correlation with mental health indicators, thereby augmenting the accuracy of the system. Our findings emphasize the efficacy of the SA-BFRNN technique, as demonstrated by its overall performance in terms of recall (92.56%), accuracy (90.13%), F1-score (88.16%) and precision (89.23%). Our experimental results unequivocally demonstrate that our proposed model performed better than other traditional approaches in classifying contents from multimodal data, showing notable enhancements in accuracy and robustness, particularly under dynamic conditions.