Sequential Human Gait Classification With Distributed Radar Sensor Fusion

• Published in: IEEE sensors journal Vol. 21; no. 6; pp. 7590 - 7603
• Main Authors: Li, Haobo, Mehul, Ajay, Le Kernec, Julien, Gurbuz, Sevgi Z., Fioranelli, Francesco
• Format: Journal Article
• Language: English
• Published: New York IEEE 15.03.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: 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
• DOI: 10.1109/JSEN.2020.3046991

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.