Sensor-Infused Emperor Penguin Optimized Deep Maxout Network for Paralyzed Person Monitoring

Paralyzed people must maintain proper posture to prevent respiratory problems, pressure sores, and muscular contractures. A paralyzed person may slip out of their wheelchair several times as a result of poor sitting position. To overcome these challenges, a novel Emperor Penguin optimized sensor-Infused wheelChair (EPIC) framework has been proposed to constantly monitor the real-time posture of the paralyzed person’s health state. The proposed model aims to monitor wheelchair posture and health in real time, which can automatically detect posture issues, providing timely alerts and feedback to the user via a mobile application. The proposed framework utilizes the emperor penguin optimizer (EPO) algorithm for feature selection to improve the accuracy of posture detection. A deep maxout network (DMN) analyzes the features to predict the posture of the wheelchair user patient. The proposed EPIC framework has been assessed using a Python simulator. The effectiveness of the proposed EPIC framework has been determined using evaluation metrics, such as precision, specificity, accuracy, and sensitivity. The proposed EPIC technique advances the overall accuracy by 10.1%, 7.73%, and 2.84% better than posture recognition, graphic user interface (GUI), and independent component analysis-kantorovich distance (ICA-KD), respectively.