AI-Based Smart Sensing and AR for Gait Rehabilitation Assessment

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.

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“…Meanwhile, other methodological issues, such as the negative effects associated with technology or ethical aspects, are completely underestimated despite their potentially crucial role in a proper evaluation [22]. In the field of studies concerning reliability, efficacy, and efficiency [44,45,69,146], it is widely recognised that properly addressing the methodological issues could produce a rigorous evaluation process.…”

“…In the context of clinical research, reliability indicates the extent to which the study's results are replicable and stable over time, under similar conditions, and with different researchers [43]. Ensuring the reliability of clinical research is crucial for establishing the efficacy and effectiveness of the study outcomes, which, in turn, influences their reproducibility and generalisability [44,45].…”

“…However, the lack of reproducibility and generalisability of the results obtained from an intervention still seems to be overlooked. This also happens because of the lack of reporting on the reliability of intervention results in studies [39,40,44]. In fact, especially in the clinical setting, the effectiveness, efficiency, and generalisability of interventions in real-life settings are paramount, making accurate evaluation of them crucial [44,45].…”

“…To be efficient and effective, an intervention must be evidence-based. Indeed, an evidence-based intervention would ensure that the clinical and scientific reliability has been evaluated and defined according to standard criteria [40,42,44]. A rigorous evaluation process is the gold standard for demonstrating that an intervention is evidence-based [45,60].…”