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학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
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In recent years, falls among elderly people have gained serious attention as a major cause of injuries. Falls often lead to fatal consequences due to lack of prompt response and rescue. Therefore, a moreaccurate fall detection system and an effective feature extraction technique are required to preventand reduce the risk of such incidents. In this paper, we proposed an efficient feature extractiontechnique based on multiple sliding windows and validated it through a series of experiments usingsupervised learning algorithms. The experiments were conducted using the public datasets obtainedfrom tri-axial accelerometers. The results depicted that extraction of the feature from adjacentsliding windows led to high accuracy in supervised machine learning-based fall detection. Also, theexperiments conducted in this study suggested that the best accuracy can be achieved by keepingthe window size as small as 2 seconds. With the kNN classifier and dataset from wearable sensors,the experiments achieved accuracy rates of 94%.
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참고문헌 (19)
참고문헌 신청
WHO / Falls
Mao A. / 2017 / Highly portable, Sensor-Based System for Human Fall Monitoring, Sensors / Sensors
He, J. / 2017 / An unobtrusive fall detection and alerting system based on Kalman filter and Bayes network classifier / Sensors
Putra, P. / 2018 / An Event Triggered Machine Learning Approach for Accelerometer-Based Fall Detection / Sensors
Diep, N. / 2013 / A classifier based approach to real-time fall detection using low-cost wearable sensors / Proc. Of International Conference on Soft Computing and Pattern Recognition(SoCPaR) :105~110
Mao A. / 2017 / Highly portable, Sensor-Based System for Human Fall Monitoring, Sensors / Sensors
He, J. / 2017 / An unobtrusive fall detection and alerting system based on Kalman filter and Bayes network classifier / Sensors
Putra, P. / 2018 / An Event Triggered Machine Learning Approach for Accelerometer-Based Fall Detection / Sensors
Diep, N. / 2013 / A classifier based approach to real-time fall detection using low-cost wearable sensors / Proc. Of International Conference on Soft Computing and Pattern Recognition(SoCPaR) :105~110
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