Lee, Chang Min Park, Jisu Park, Shinsuk Kim, Choong Hyun 2024-01-19T18:00:53Z 2024-01-19T18:00:53Z 2021-09-05 2020-04 2234-7593 https://pubs.kist.re.kr/handle/201004/118810 In this study, experiments are conducted for four types of falls and eight types of activities of daily living with an integrated sensor system that uses both an inertial measurement unit and a plantar-pressure measurement unit and the fall-detection performance is evaluated by analyzing the acquired data with the threshold method and the decision-tree method. In general, the decision-tree method shows better performance than the threshold method, and the fall-detection accuracy increases when the acceleration and center-of-pressure (COP) data are used together, rather than when each data point is used separately. The results show that the fall-detection algorithm that applies both acceleration and COP data to the decision-tree method has a fall-detection accuracy of 95% or higher and a sufficient lead time of 317 ms on average. English 한국정밀공학회 Fall-Detection Algorithm Using Plantar Pressure and Acceleration Data Article 10.1007/s12541-019-00268-w 1 International Journal of Precision Engineering and Manufacturing, v.21, no.4, pp.725 - 737 International Journal of Precision Engineering and Manufacturing 21 4 725 737 N scie scopus kci ART002574418 000522450300014 2-s2.0-85076618996 Engineering, Manufacturing Engineering, Mechanical Engineering Article PRE-IMPACT DETECTION SENSOR SYSTEM Activities of daily living Center of pressure Decision tree Fall detection Force sensing resistor Inertial measurement unit