研究目的
To propose an imminent fall risk estimator for rollator users that can be automatically obtained on the fly, addressing the limitations of manual assessments by medical staff.
研究成果
The proposed fall risk estimator, which incorporates weight bearing on the rollator, shows coherence with traditional assessments like the Tinetti Mobility Test and walking speed. It provides a continuous, automatic method for imminent fall risk detection without requiring medical supervision, making it suitable for everyday use in various environments.
研究不足
The main drawback is that foot support needs to be estimated using only the user's weight and handlebar force sensors, which may not be accurate during all phases of walking. The study has a limited number of volunteers (n=10), which may affect generalizability.
1:Experimental Design and Method Selection:
The methodology involved asking volunteers with disabilities to move freely with a rollator equipped with sensors. A normalized ellipsoid model was developed to estimate fall risk based on centroid of support (CnS) calculations from sensor data.
2:Sample Selection and Data Sources:
10 volunteers with physical and/or neurological disabilities from a hospital were selected based on criteria of being able to walk autonomously with a rollator and support weight on it. Data included Tinetti Mobility Test scores, walking speed, and sensor readings.
3:List of Experimental Equipment and Materials:
i-Walker rollator with force sensors in handlebars, encoders in wheels, RGB-D camera, Raspberry Pi embedded system for data processing.
4:Experimental Procedures and Operational Workflow:
Volunteers walked freely in a rehabilitation room for 3 minutes, performing various maneuvers. Sensor data was collected and processed to compute CnS and fall risk estimates.
5:Data Analysis Methods:
Fall risk was estimated using a scale factor α derived from the ellipsoid model equation. Results were compared with Tinetti scores and walking speed using correlation analysis and plane fitting.
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