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Author(s): Monge, J.
Raimundo, A.
Ribeiro, G.
Postolache, O.
Santos, J.
Date: 2023
Title: AI-based smart sensing and AR for gait rehabilitation assessment
Journal title: Information
Volume: 14
Number: 7
Reference: Monge, J., Raimundo, A., Ribeiro, G., Postolache, O., & Santos, J. (2023). AI-based smart sensing and AR for gait rehabilitation assessment. Information, 14(7), 355.
ISSN: 2078-2489
DOI (Digital Object Identifier): 10.3390/info14070355
Keywords: AI
IoT embedded systems
Machine learning
Physical rehabilitation
Smart sensing
Wearable devices
Abstract: 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.
Peerreviewed: yes
Access type: Open Access
Appears in Collections:IT-RI - Artigos em revistas científicas internacionais com arbitragem científica

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