Fall-Detection Algorithm Using Plantar Pressure and Acceleration Data

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 acquire...

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Vydáno v:International journal of precision engineering and manufacturing Ročník 21; číslo 4; s. 725 - 737
Hlavní autoři: Lee, Chang Min, Park, Jisu, Park, Shinsuk, Kim, Choong Hyun
Médium: Journal Article
Jazyk:angličtina
Vydáno: Seoul Korean Society for Precision Engineering 01.04.2020
Springer Nature B.V
Témata:
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
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Shrnutí: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.
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ISSN:2234-7593
2005-4602
DOI:10.1007/s12541-019-00268-w