Despite the increasing focus on the mental health of older adults and active senior populations, assessment tools still lag behind those for physical health monitoring. To bridge this gap, this study introduces an AI chatbot-based multimodal stress monitoring system that utilizes emotion recognition and heart rate variability (HRV). The system analyzes chatbot conversations, video, audio, and heart rate signals to assess facial expressions, speech emotions, and HRV, allowing for stress evaluation and user stratification into risk groups. Negative emotions are quantified and combined with HRV data to generate a stress score. Facial and speech emotion models were trained on the RAVDESS, CREMA, and TESS datasets, yielding 21,000 augmented samples through a BiLSTM network. Additionally, a deep learning-based HRV model utilized data from smartwatches to predict stress levels. By integrating facial, vocal, and HRV features through weighted fusion, the system produces a comprehensive stress index that categorizes users Healthy, Caution, Risk. This approach facilitates continuous monitoring at home, supporting early detection for preventive care and informed clinical decision-making.

Bell’s palsy is a disease that occurs primarily between ages of 15 and 60, especially in middle-aged individuals. Although this disease gradually recovers within weeks to months, recurrence and permanent sequelae are possible. Its causes are diverse and unclear. Appropriate treatment is unknown, threatening lives of patients with this condition. In this study, we measured the degree of facial paralysis in a model of Bell’s palsy patients using OpenCV and the H.B grade measurement method and classified measured values according to H.B grade classification. This enabled prediction of the type and risk of diseases that might occur depending on the degree of facial paralysis. Additionally, we utilized more coordinate data to confirm movement of facial muscles by region to address limitations of the Nottingham system measurement method. We graded the level of this movement to enable intuitive confirmation and confirmed differences between existing Nottingham system and the H.B grade. This simple system could determine the level of paralysis in patients with Bell’s palsy and their corresponding risk level for related diseases. It enables information on causative disease of patients with Bell’s palsy to be quickly obtained, enabling prompt treatment and support.

This study investigates epoxy filling rate in the capillary underfill process of flip-chip packaging when the air is not trapped. Various design features were considered, they include; the shape of soldering bump, inlet size, bump height and bump spacing. The geometric models were made by CATIA and the analysis was carried out using commercial CFD software (Moldex3D capillary underfill packaging). In order to improve the usability of the analysis, the spherical bump shape was authenticated by the means of believe as a rhombic shape, and the analysis results were verified. The inlet size did not in any way whatsoever affect the underfill process analysis. From the analysis, we concluded that the epoxy of center parts needs to fill 80% or more of the inside of the edge in order to keep away from the air trapping on the flip-chip. This result can be a guideline for the underfill process conditions that may not be a reason for the air trap in the flip-chip design and manufacturing.