DEVELOPMENT OF A WEARABLE DEVICE FOR ANXIETY MONITORING USING GALVANIC SKIN RESPONSE AND PHOTOPLETHYSMOGRAPHY

• Main Author: Ramadhani Putri Naja, Diandra
• Format: Final Project
• Language: Indonesia
• Online Access: https://digilib.itb.ac.id/gdl/view/86056
• Institution: Institut Teknologi Bandung

Mental health, particularly anxiety disorders, has gained increasing global attention, with approximately 970 million people affected in 2019, and the numbers rising during the COVID- 19 pandemic. Anxiety disorders, which impact the psychological, emotional, and physiological aspects of individuals, can disrupt daily life and elevate the risk of serious health issues if left untreated. Early detection is crucial, and the advancement of wearable device technology enables the monitoring of physiological signals, such as heart rate and skin response, to detect signs of anxiety. Therefore, this research aims to design a wearable device capable of detecting anxiety based on data from PPG and GSR sensors, helping individuals monitor their mental health and providing early warnings when high levels of anxiety are detected. The developed wearable device system processes data directly on the ESP-WROOM-32 microcontroller, from data acquisition to anxiety prediction. Through acquisition and pre-processing, this device can extract key features such as Heart Rate Variability (HRV) and Skin Conductance Response (SCR) related to anxiety. Testing showed that the RR Interval data obtained using the PPG sensor demonstrated a Mean Absolute Error (MAE) of 9,03 ms compared to RR interval data from Biopac. Meanwhile, the GSR data exhibited similar trends between the utilized sensor and Biopac. Additionally, a model trained using the HRV dataset with a neural network approach achieved 92% accuracy. The testing results also indicated that the performance of the converted model running on the microcontroller was comparable to the model executed in Python. However, the microcontroller's memory limitations necessitate algorithm simplification to maintain performance and accuracy. Further studies are needed to address these challenges and enhance the accuracy of wearable devices in anxiety detection.