Abstract
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. 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.
| Original language | English |
|---|---|
| Article number | e92037 |
| Journal | PLoS One |
| Volume | 9 |
| Issue number | 3 |
| DOIs | |
| Publication status | Published - Mar 21 2014 |
Fingerprint
ASJC Scopus subject areas
- Agricultural and Biological Sciences(all)
- Biochemistry, Genetics and Molecular Biology(all)
- Medicine(all)
Cite this
Pre-impact fall detection : Optimal sensor positioning based on a machine learning paradigm. / Martelli, Dario; Artoni, Fiorenzo; Monaco, Vito; Sabatini, Angelo Maria; Micera, Silvestro.
In: PLoS One, Vol. 9, No. 3, e92037, 21.03.2014.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Pre-impact fall detection
T2 - Optimal sensor positioning based on a machine learning paradigm
AU - Martelli, Dario
AU - Artoni, Fiorenzo
AU - Monaco, Vito
AU - Sabatini, Angelo Maria
AU - Micera, Silvestro
PY - 2014/3/21
Y1 - 2014/3/21
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. 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.
AB - 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. 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.
UR - http://www.scopus.com/inward/record.url?scp=84899097035&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84899097035&partnerID=8YFLogxK
U2 - 10.1371/journal.pone.0092037
DO - 10.1371/journal.pone.0092037
M3 - Article
VL - 9
JO - PLoS One
JF - PLoS One
SN - 1932-6203
IS - 3
M1 - e92037
ER -