Advancing AI-Powered Wearables - A Novel Approach for Real-Time Health Monitoring

Bhavani Sankar Telaprolu

International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2023, vol. 9, issue 3, 722-727

Abstract: With a focus on heart rate and ECG signal analysis, this study investigates the function of wearable technology driven by artificial intelligence in real-time health monitoring. It investigates how well Transformer models and Long Short-Term Memory (LSTM) networks can increase prediction accuracy. This study offers a comparative analysis of these models using publicly accessible datasets and clearly defined evaluation metrics. Additionally, the study evaluates their performance using F1-score, recall, accuracy, and precision. The study also discusses clinical applicability and model interpretability. However, it's important to note that the study is limited by the scope and quality of the publicly available datasets. To improve wearable healthcare solutions, future research will concentrate on integrating multimodal sensor data, developing federated learning techniques for safe AI implementation, and improving real-time inference on edge AI platforms.

Keywords: AI-powered wearables; health monitoring; ECG analysis; LSTM; Transformer models; federated learning; real-time inference; multimodal sensor data (search for similar items in EconPapers)
Date: 2023
Note: Article URL: https://ijsrcseit.com/CSEIT2390383
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Persistent link: https://EconPapers.repec.org/RePEc:jbh:ijsrcs:v9:y2023:i3:id:hcseit2390383

DOI: 10.32628/CSEIT2390383

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