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Sensors 2018, 18(4), 1227; https://doi.org/10.3390/s18041227

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

1
Department of Bio-Mechatronic Engineering, College of Biotechnology and Bioengineering, Sungkyunkwan University, 2066 Seobu-ro, Jangan-gu, Suwon, Gyeonggi 16419, Korea
2
Department of Research and Development, Biomaterial Team, Medical Device Development Center, KBIO HEALTH, 123 Osongsaengmyung-ro, Osong-eub, Heungdeok-gu, Cheongju, Chungbuk 28160, Korea
These authors contributed equally to this work.
*
Authors to whom correspondence should be addressed.
Received: 14 February 2018 / Revised: 10 April 2018 / Accepted: 14 April 2018 / Published: 17 April 2018
(This article belongs to the Section Physical Sensors)
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Abstract

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. View Full-Text
Keywords: fall detection; high acceleration activities; insole sensor system; machine learning fall detection; high acceleration activities; insole sensor system; machine learning
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Cates, B.; Sim, T.; Heo, H.M.; Kim, B.; Kim, H.; Mun, J.H. 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 2018, 18, 1227.

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