TY  - JOUR
PY  - 2018//
TI  - 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
JO  - Sensors (Basel)
A1  - Cates, Benjamin
A1  - Sim, Taeyong
A1  - Heo, Hyun Mu
A1  - Kim, Bori
A1  - Kim, Hyunggun
A1  - Mun, Joung Hwan
SP  - s18041227
EP  - s18041227
VL  - 18
IS  - 4
N2  - 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.<p />  <p>Language: en</p>
LA  - en
SN  - 1424-8220
UR  - http://dx.doi.org/10.3390/s18041227
ID  - ref1
ER  -