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@article{183109,
        author = {SANIDP NIRANJAN VENDE and DR. Anubhav Kumar Prasad},
        title = {AI-Powered Multimodal Health Insight System Using Wearable, Voice, and Textual Data Fusion for Early Detection of Chronic Conditions in Athletes},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {3},
        pages = {372-377},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=183109},
        abstract = {The early detection of chronic conditions in athletes is crucial for maintaining peak performance and preventing long-term health complications. Traditional health monitoring systems rely on single-modal approaches, limiting their ability to capture the complex, multifaceted nature of athlete health status. This paper presents a novel AI-powered multimodal health insight system that integrates wearable sensor data, voice biomarkers, and textual feedback to provide comprehensive health monitoring for sportspersons and athletes. Our system employs advanced deep learning architectures including convolutional neural networks (CNNs) for wearable data processing, wav2vec models for voice analysis, and BERT-based transformers for natural language understanding. A novel attention-based fusion mechanism combines these modalities to achieve superior performance in detecting early signs of chronic conditions such as overtraining syndrome, cardiovascular irregularities, and mental health deterioration. Experimental validation on a dataset of 450 professional athletes across multiple sports demonstrates that our multimodal approach achieves 94.2% accuracy in early condition detection, significantly outperforming unimodal baselines by 12-18%. The system's explainable AI components provide actionable insights for coaches and sports medicine professionals, enabling proactive intervention strategies.},
        keywords = {Multimodal AI, Health Monitoring, Sports Medicine, Wearable Computing, Voice Biomarkers, Early Detection.},
        month = {July},
        }