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Sensors, Vol. 18, Pages 1227: A Novel Detection Model and Its Optimal Features to Classify Falls from Low- and High-Acceleration Activities of Daily Life Using an Insole Sensor System

Sensors, Vol. 18, Pages 1227: A Novel Detection Model and Its Optimal Features to Classify Falls from Low- and High-Acceleration Activities of Daily Life Using an Insole Sensor System

Sensors doi: 10.3390/s18041227

Authors: Benjamin Cates Taeyong Sim Hyun Mu Heo Bori Kim Hyunggun Kim Joung Hwan Mun

In order to overcome the current limitations in current threshold-based and machine learning-based fall detectors, an insole system and novel fall classification model were created. Because high-acceleration activities have a high risk for falls, and because of the potential damage that is associated with falls during high-acceleration activities, four low-acceleration activities, four high-acceleration activities, and eight types of high-acceleration falls were performed by twenty young male subjects. Encompassing a total of 800 falls and 320 min of activities of daily life (ADLs), the created Support Vector Machine model’s Leave-One-Out cross-validation provides a fall detection sensitivity (0.996), specificity (1.000), and accuracy (0.999). These classification results are similar or superior to other fall detection models in the literature, while also including high-acceleration ADLs to challenge the classification model, and simultaneously reducing the burden that is associated with wearable sensors and increasing user comfort by inserting the insole system into the shoe.

Authors:   Cates, Benjamin ; Sim, Taeyong ; Heo, Hyun Mu; Kim, Bori ; Kim, Hyunggun ; Mun, Joung Hwan
Journal:   Sensors
Volume:   18
edition:   4
Year:   2018
Pages:   1227
DOI:   10.3390/s18041227
Publication date:   17-Apr-2018
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