Sequential Human Gait Classification With Distributed Radar Sensor Fusion

This paper presents different information fusion approaches to classify human gait patterns and falls in a radar sensors network. The human gaits classified in this work are both individual and sequential, continuous gait collected by a FMCW radar and three UWB pulse radar placed at different spatia...

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Vydáno v:IEEE sensors journal Ročník 21; číslo 6; s. 7590 - 7603
Hlavní autoři: Li, Haobo, Mehul, Ajay, Le Kernec, Julien, Gurbuz, Sevgi Z., Fioranelli, Francesco
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York IEEE 15.03.2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Algorithms
Classification
Classifiers
Data integration
Decision trees
Doppler radar
fall detection
Gait
gait analysis
Legged locomotion
machine learning
Multisensor fusion
Neural networks
Pulse radar
Radar
RF sensing
Sensor fusion
Sensor phenomena and characterization
Sensors
Ultra wideband radar
Ultrawideband radar
Walking
ISSN:1530-437X, 1558-1748
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Shrnutí:This paper presents different information fusion approaches to classify human gait patterns and falls in a radar sensors network. The human gaits classified in this work are both individual and sequential, continuous gait collected by a FMCW radar and three UWB pulse radar placed at different spatial locations. Sequential gaits are those containing multiple gait styles performed one after the other, with natural transitions in between, including fall events developing from walking gait in some cases. The proposed information fusion approaches operate at signal and decision level. For the signal level combination, a simple trilateration algorithm is implemented on the range data from the 3 UWB radar sensors, achieving good classification results with the proposed Bi-LSTM (Bidirectional LSTM neural network) as classifier, without exploiting conventional micro-Doppler information. For the decision level fusion, the classification results of individual radars using the Bi-LSTM network are combined with a robust Naive Bayes Combiner (NBC), and this showed subsequent improvement compared to the single radar case thanks to multi-perspective views of the subjects. Compared to conventional SVM and Random Forest classifiers, the proposed approach yields +20% and +17% improvement in the classification accuracy of individual gaits for the range-only trilateration method and NBC decision fusion method, respectively. When classifying sequential gaits, the overall accuracy for the two proposed methods reaches 93% and 90%, with validation via a 'leaving one participant out' approach to test the robustness with subjects unknown to the network.
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ISSN:1530-437X
1558-1748
DOI:10.1109/JSEN.2020.3046991