TY  - JOUR
PY  - 2014//
TI  - Pre-impact fall detection: optimal sensor positioning based on a machine learning paradigm
JO  - PLoS one
A1  - Martelli, Dario
A1  - Artoni, Fiorenzo
A1  - Monaco, Vito
A1  - Sabatini, Angelo Maria
A1  - Micera, Silvestro
SP  - e92037
EP  - e92037
VL  - 9
IS  - 3
N2  - The aim of this study was to identify the best subset of body segments that provides for a rapid and reliable detection of the transition from steady walking to a slipping event. Fifteen healthy young subjects managed unexpected perturbations during walking. Whole-body 3D kinematics was recorded and a machine learning algorithm was developed to detect perturbation events. In particular, the linear acceleration of all the body segments was parsed by Independent Component Analysis and a Neural Network was used to classify walking from unexpected perturbations. The Mean Detection Time (MDT) was 351±123 ms with an Accuracy of 95.4%. The procedure was repeated with data related to different subsets of all body segments whose variability appeared strongly influenced by the perturbation-induced dynamic modifications. Accordingly, feet and hands accounted for most data information and the performance of the algorithm were slightly reduced using their combination. <br><br>RESULTS support the hypothesis that, in the framework of the proposed approach, the information conveyed by all the body segments is redundant to achieve effective fall detection, and suitable performance can be obtained by simply observing the kinematics of upper and lower distal extremities. Future studies are required to assess the extent to which such results can be reproduced in older adults and in different experimental conditions.<p /> <p>Language: en</p>
LA  - en
SN  - 1932-6203
UR  - http://dx.doi.org/10.1371/journal.pone.0092037
ID  - ref1
ER  -