Early Mobility Recognition for Intensive Care Unit Patients Using Accelerometers
This addresses a healthcare problem for ICU patients by enabling better monitoring of early mobility, but it is incremental as it builds on existing human activity recognition methods.
The paper tackles early mobility recognition for ICU patients using accelerometers and an AI model, improving accuracy from 77.78% to 81.86% and reducing instability from 16.69% to 6.92% with feature engineering and segment voting.
With the development of the Internet of Things(IoT) and Artificial Intelligence(AI) technologies, human activity recognition has enabled various applications, such as smart homes and assisted living. In this paper, we target a new healthcare application of human activity recognition, early mobility recognition for Intensive Care Unit(ICU) patients. Early mobility is essential for ICU patients who suffer from long-time immobilization. Our system includes accelerometer-based data collection from ICU patients and an AI model to recognize patients' early mobility. To improve the model accuracy and stability, we identify features that are insensitive to sensor orientations and propose a segment voting process that leverages a majority voting strategy to recognize each segment's activity. Our results show that our system improves model accuracy from 77.78\% to 81.86\% and reduces the model instability (standard deviation) from 16.69\% to 6.92\%, compared to the same AI model without our feature engineering and segment voting process.