AI-Based Smart Sensing and AR for Gait Rehabilitation Assessment

Health monitoring is crucial in hospitals and rehabilitation centers. Challenges can affect the reliability and accuracy of health data. Human error, patient compliance concerns, time, money, technology, and environmental factors might cause these issues. In order to improve patient care, healthcare providers must address these challenges. We propose a non-intrusive smart sensing system that uses a SensFloor smart carpet and an inertial measurement unit (IMU) wearable sensor on the user's back to monitor position and gait characteristics. Furthermore, we implemented machine learning (ML) algorithms to analyze the data collected from the SensFloor and IMU sensors. The system generates real-time data that are stored in the cloud and are accessible to physical therapists and patients. Additionally, the system's real-time dashboards provide a comprehensive analysis of the user's gait and balance, enabling personalized training plans with tailored exercises and better rehabilitation outcomes. Using non-invasive smart sensing technology, our proposed solution enables healthcare facilities to monitor patients' health and enhance their physical rehabilitation plans.

Automated summary

Health monitoring is crucial in healthcare facilities, but challenges such as human error, patient compliance concerns, and limited resources affect the reliability of health data. To address these issues, we propose a non-intrusive smart sensing system using SensFloor smart carpet and an IMU wearable sensor to monitor position and gait characteristics. Machine learning algorithms are utilized to analyze the collected data and generate real-time, cloud-stored information accessible to medical professionals and patients. The system's real-time dashboards provide comprehensive analysis, enabling personalized training plans and better rehabilitation outcomes.