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.
This paper deals with the development of a passive modular hip exoskeleton system aimed at preventing musculoskeletal low back pain, which commonly occurs in heavy weight transport workers, by improving back muscle strength. The passive exoskeleton system has the advantage of being lightweight, making it suitable for modular exoskeleton systems. The cam and spring actuator designed in this study was applied to the passive modular exoskeleton system to build human hip and lumbar muscle strength. In order to evaluate the effectiveness of the passive modular exoskeleton system, a test was performed in which a subject lifted a 15 kg weight three times in a stoop posture, using heart rate measurement and Borg scale recording. According to the results, all subjects showed 26.83% lower maximum heart rate and 34.73% lower average heart rate than those who did not wear the system, and Borg scale evaluation result was lower. All subjects wore this system and did not experience back pain during the experiment. Through this study, we validated the effectiveness of the passive modular exoskeleton system and proved that this system can build the strength of industrial workers and be a solution to prevent musculoskeletal lumbar disease.
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