Page 527 - Emerging Trends and Innovations in Web-Based Applications and Technologies

International Journal of Trend in Scientific Research and Development (IJTSRD) @ www.ijtsrd.com eISSN: 2456-6470
sensations, symptoms, and behaviors, helping to overcome track variables such as physical activity, heart rate, and
recall biases in traditional self-reporting methods. Wearable social interactions. These sensors must balance technical
devices, smartphones, and AI-driven chatbots have shown considerations like power consumption and sampling rates
promise in reducing depressive and anxious symptoms, with user privacy and intrusiveness concerns.
improving overall mental health outcomes. Multimodal Network Layer: Transfers collected data securely via
sensing, combining accelerometers, heart rate monitors, GPS, Bluetooth, Wi-Fi, or cellular networks while ensuring data
and social interaction data, has proven effective in analyzing encryption and user privacy.
mood and behavior. AI techniques, including supervised,
unsupervised, and reinforcement learning, are integral in Analysis Layer: Processes raw sensor data using AI
processing these complex datasets, enabling accurate methods, including data labeling, preprocessing, and
predictions of mental health conditions. attribute extraction. Techniques like PCA and dimensionality
reduction help filter and interpret the data. Machine learning
Autonomous Psychological Health Monitoring (APHM)
Systems models (user-dependent, user-independent, or hybrid)
further refine the predictions. Tools like Weka, Scikit-learn,
APHM systems leverage wearable and mobile technologies
and InSTIL support this analysis.
to autonomously track psychological and physiological
parameters. These systems operate through a multi-layered Application Layer: Focuses on practical applications like
architecture: remote psychological health monitoring, fall detection,
emotion prediction, and well-being tracking. These systems
Sensing Layer: Utilizes environmental and physiological
enable caregivers and healthcare providers to make
sensors (e.g., accelerometers, gyroscopes, and biosensors) to
informed decisions and offer timely interventions.

Figure 1. Autonomous psychological health monitoring (APHM) systems-multi-tiered design

IJTSRD | Special Issue on Emerging Trends and Innovations in Web-Based Applications and Technologies Page 517