Implementation of IoT-Enabled Smart Devices for Real-Time Health Monitoring in Nursing Practice

IoT technologies further help transform medical patient observation that makes healthcare distributed systems favorable for nursing practice. In this research, an advanced analytical system is developed comprising Deep Learning model based on Transformer, for the purpose to increase the speed and accuracy in health data processing from IoT smart devices. Self attention mechanisms in Transformer models are used by the healthcare specialist to extract better features by which they achieve accurate predictions for the measurements of heart rate and oxygen saturation and body temperature. Titanic is implemented based on deep learning, so it implements TensorFlow as its deep learning platform to effectively deploy this solution because TensorFlow provides powerful flexible deep learning infrastructure with IoT ecosystem integration. The proposal of the system provides novel advantages to nursing that promote the early recognition of aberrations so that nurses can deliver rapid medical assistance. The novel system is capable of empowering nurses to take such decisions on the basis of the data and maintain constant observation. The next stage of development narrows the focus to strengthening the model stability and continuing to add new health data for improved health observation capabilities.