Deep Learning Methods for Speed Estimation of Bipedal Motion from Wearable IMU Sensors
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23220%2F22%3A43965368" target="_blank" >RIV/49777513:23220/22:43965368 - isvavai.cz</a>
Result on the web
<a href="https://www.mdpi.com/1424-8220/22/10/3865/htm" target="_blank" >https://www.mdpi.com/1424-8220/22/10/3865/htm</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.3390/s22103865" target="_blank" >10.3390/s22103865</a>
Alternative languages
Result language
angličtina
Original language name
Deep Learning Methods for Speed Estimation of Bipedal Motion from Wearable IMU Sensors
Original language description
The estimation of the speed of human motion from wearable IMU sensors is required in applications such as pedestrian dead reckoning. In this paper, we test deep learning methods for the prediction of the motion speed from raw readings of a low-cost IMU sensor. Each subject was observed using three sensors at the shoe, shin, and thigh. We show that existing general-purpose architectures outperform classical feature-based approaches and propose a novel architecture tailored for this task. The proposed architecture is based on a semi-supervised variational auto-encoder structure with innovated decoder in the form of a dense layer with a sinusoidal activation function. The proposed architecture achieved the lowest average error on the test data. Analysis of sensor placement reveals that the best location for the sensor is the shoe. Significant accuracy gain was observed when all three sensors were available. All data acquired in this experiment and the code of the estimation methods are available for download.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
20201 - Electrical and electronic engineering
Result continuities
Project
<a href="/en/project/FW01010189" target="_blank" >FW01010189: Virtual Reality Toolset for a realistic simulation with dynamic motion platforms</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2022
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
SENSORS
ISSN
1424-8220
e-ISSN
1424-8220
Volume of the periodical
22
Issue of the periodical within the volume
10
Country of publishing house
CH - SWITZERLAND
Number of pages
16
Pages from-to
1-16
UT code for WoS article
000802493900001
EID of the result in the Scopus database
2-s2.0-85130456586