Application of Decision Tree to Classify Fall Risk Using Inertial Measurement Unit Sensor Data and Clinical Measurements

서지정보

ㆍ발행기관 : 한국전문물리치료학회 ㆍ수록지정보 : 한국전문물리치료학회지 / 30권 / 2호
ㆍ저자명 : Junwoo Park, Jongwon Choi, Seyoung Lee, Kitaek Lim, Woochol Joseph Choi

목차

INTRODUCTION
MATERIALS AND METHODS
1. Subjects
2. Experimental Protocol
3. Input Features
4. Data Analysis
RESULTS
DISCUSSION
CONCLUSIONS
FUNDING
ACKNOWLEDGEMENTS
CONFLICTS OF INTEREST
AUTHOR CONTRIBUTION
ORCID
REFERENCES

영어 초록

Background: While efforts have been made to differentiate fall risk in older adults using wearable devices and clinical methodologies, technologies are still infancy. We applied a decision tree (DT) algorithm using inertial measurement unit (IMU) sensor data and clinical measurements to generate high performance classification models of fall risk of older adults.
Objects: This study aims to develop a classification model of fall risk using IMU data and clinical measurements in older adults.
Methods: Twenty-six older adults were assessed and categorized into high and low fall risk groups. IMU sensor data were obtained while walking from each group, and features were extracted to be used for a DT algorithm with the Gini index (DT1) and the Entropy index (DT2), which generated classification models to differentiate high and low fall risk groups. Model’s performance was compared and presented with accuracy, sensitivity, and specificity.
Results: Accuracy, sensitivity and specificity were 77.8%, 80.0%, and 66.7%, respectively, for DT1; and 72.2%, 91.7%, and 33.3%, respectively, for DT2.
Conclusion: Our results suggest that the fall risk classification using IMU sensor data obtained during gait has potentials to be developed for practical use. Different machine learning techniques involving larger data set should be warranted for future research and development.

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