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

• 영문명: Application of Decision Tree to Classify Fall Risk Using Inertial Measurement Unit Sensor Data and Clinical Measurements
• 발행기관: 한국전문물리치료학회
• 저자명: Park Junwoo Choi Jongwon Lee Seyoung Lim Kitaek Choi Woochol Joseph
• 간행물 정보: 『한국전문물리치료학회지』제30권 제2호, 102~109쪽, 전체 8쪽
• 주제분류: 의약학 > 의학일반
• 파일형태: PDF
• 발행일자: 2023.05.30

영문 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.

목차

INTRODUCTION

MATERIALS AND METHODS

RESULTS

DISCUSSION

CONCLUSIONS

FUNDING

ACKNOWLEDGEMENTS

CONFLICTS OF INTEREST

AUTHOR CONTRIBUTION

ORCID

REFERENCES

키워드

Classification Decision tree Fall risk Gait Inertial measurement unit sensor