Next Article in Journal
DMS-SLAM: A General Visual SLAM System for Dynamic Scenes with Multiple Sensors
Previous Article in Journal
Sensitivity of Vegetation Indices for Estimating Vegetative N Status in Winter Wheat
Open AccessArticle

Pre-Impact Detection Algorithm to Identify Tripping Events Using Wearable Sensors

1
The BioRobotics Institute, Scuola Superiore Sant’Anna, 56127 Pisa, Italy
2
Bertarelli Foundation Chair in Translational Neuroengineering, Center for Neuroprosthetics and Institute of Bioengineering, School of Engineering, Ecole Polytechnique Federale de Lausanne, 1015 Lausanne, Switzerland
3
IRCCS Fondazione Don Carlo Gnocchi, 20148 Milan, Italy
*
Author to whom correspondence should be addressed.
Sensors 2019, 19(17), 3713; https://doi.org/10.3390/s19173713
Received: 17 July 2019 / Revised: 22 August 2019 / Accepted: 26 August 2019 / Published: 27 August 2019
(This article belongs to the Section Biosensors)
This study aimed to investigate the performance of an updated version of our pre-impact detection algorithm parsing out the output of a set of Inertial Measurement Units (IMUs) placed on lower limbs and designed to recognize signs of lack of balance due to tripping. Eight young subjects were asked to manage tripping events while walking on a treadmill. An adaptive threshold-based algorithm, relying on a pool of adaptive oscillators, was tuned to identify abrupt kinematics modifications during tripping. Inputs of the algorithm were the elevation angles of lower limb segments, as estimated by IMUs located on thighs, shanks and feet. The results showed that the proposed algorithm can identify a lack of balance in about 0.37 ± 0.11 s after the onset of the perturbation, with a low percentage of false alarms (<10%), by using only data related to the perturbed shank. The proposed algorithm can hence be considered a multi-purpose tool to identify different perturbations (i.e., slippage and tripping). In this respect, it can be implemented for different wearable applications (e.g., smart garments or wearable robots) and adopted during daily life activities to enable on-demand injury prevention systems prior to fall impacts. View Full-Text
Keywords: pre-impact detection; tripping; wearable sensors; lower-limb biomechanics pre-impact detection; tripping; wearable sensors; lower-limb biomechanics
Show Figures

Figure 1

MDPI and ACS Style

Aprigliano, F.; Micera, S.; Monaco, V. Pre-Impact Detection Algorithm to Identify Tripping Events Using Wearable Sensors. Sensors 2019, 19, 3713.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop