Fall-Detection Algorithm Using Plantar Pressure and Acceleration Data

• Published in: International journal of precision engineering and manufacturing Vol. 21; no. 4; pp. 725 - 737
• Main Authors: Lee, Chang Min, Park, Jisu, Park, Shinsuk, Kim, Choong Hyun
• Format: Journal Article
• Language: English
• Published: Seoul Korean Society for Precision Engineering 01.04.2020; Springer Nature B.V
• Subjects: Algorithms; Data acquisition; Data points; Decision analysis; Decision trees; Engineering; Fall detection; Industrial and Production Engineering; Inertial platforms; Lead time; Materials Science; Performance evaluation; Plantar pressure; Pressure measurement; Regular Paper; Center of pressure; Force sensing resistor; Activities of daily living; Inertial measurement unit; Decision tree; Fall detection
• ISSN: 2234-7593; 2005-4602
• DOI: 10.1007/s12541-019-00268-w

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.