Abnormal gait detection and classification using micro-Doppler radar signatures

Donald L. Hall, Tyler D. Ridder, Ram Mohan Narayanan

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Micro-Doppler radars have been used to accurately detect and classify human activities in various scenarios. This paper discusses the development of an X-Band micro-Doppler radar in detecting abnormal signatures embedded in micro-Doppler responses from activities such as walking, jogging, and jumping. To synthesize the condition of an abnormal gait, five test subjects were asked to perform the activities while wearing shoes, being barefoot, and wearing shoes accompanied by a heel lift insert in one shoe. Abnormal gait detection was performed using micro-Doppler responses of the various activities measured over two different look angles. The Short-Time Fourier Transform (STFT) was used to analyze the micro-Doppler characteristics along with Cadence Frequency Diagrams (CFD). Feature extraction was performed on the micro-Doppler responses for acquiring unique hand-crafted features commonly used in literature for gait analysis by micro- Doppler radar measurements. A secondary analysis utilized dimensionality reduction through Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) to extract unique signatures and perform abnormal gait classification. An in-depth evaluation was performed on the different feature sets by the Weight K-Nearest-Neighbor (WKNN) and Support Vector Machine (SVM) classifier algorithms that determined the feasibility of discriminating between individuals performing the activities. The research allows for future determination of abnormal motion in specific activities via micro-Doppler response and machine learning that can further emphasize the ability of micro-Doppler radar to perform abnormal gait classification.

Original languageEnglish (US)
Title of host publicationRadar Sensor Technology XXIII
EditorsKenneth I. Ranney, Armin Doerry
PublisherSPIE
ISBN (Electronic)9781510626713
DOIs
StatePublished - Jan 1 2019
EventRadar Sensor Technology XXIII 2019 - Baltimore, United States
Duration: Apr 15 2019Apr 17 2019

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Volume11003
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X

Conference

ConferenceRadar Sensor Technology XXIII 2019
CountryUnited States
CityBaltimore
Period4/15/194/17/19

Fingerprint

radar signatures
gait
Doppler radar
Gait
Doppler
Radar
Signature
shoes
Gait analysis
Radar measurement
Discriminant analysis
Principal component analysis
Support vector machines
Learning systems
Feature extraction
Fourier transforms
Classifiers
signatures
radar measurement
machine learning

All Science Journal Classification (ASJC) codes

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering

Cite this

Hall, D. L., Ridder, T. D., & Narayanan, R. M. (2019). Abnormal gait detection and classification using micro-Doppler radar signatures. In K. I. Ranney, & A. Doerry (Eds.), Radar Sensor Technology XXIII [110030Q] (Proceedings of SPIE - The International Society for Optical Engineering; Vol. 11003). SPIE. https://doi.org/10.1117/12.2519663
Hall, Donald L. ; Ridder, Tyler D. ; Narayanan, Ram Mohan. / Abnormal gait detection and classification using micro-Doppler radar signatures. Radar Sensor Technology XXIII. editor / Kenneth I. Ranney ; Armin Doerry. SPIE, 2019. (Proceedings of SPIE - The International Society for Optical Engineering).
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abstract = "Micro-Doppler radars have been used to accurately detect and classify human activities in various scenarios. This paper discusses the development of an X-Band micro-Doppler radar in detecting abnormal signatures embedded in micro-Doppler responses from activities such as walking, jogging, and jumping. To synthesize the condition of an abnormal gait, five test subjects were asked to perform the activities while wearing shoes, being barefoot, and wearing shoes accompanied by a heel lift insert in one shoe. Abnormal gait detection was performed using micro-Doppler responses of the various activities measured over two different look angles. The Short-Time Fourier Transform (STFT) was used to analyze the micro-Doppler characteristics along with Cadence Frequency Diagrams (CFD). Feature extraction was performed on the micro-Doppler responses for acquiring unique hand-crafted features commonly used in literature for gait analysis by micro- Doppler radar measurements. A secondary analysis utilized dimensionality reduction through Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) to extract unique signatures and perform abnormal gait classification. An in-depth evaluation was performed on the different feature sets by the Weight K-Nearest-Neighbor (WKNN) and Support Vector Machine (SVM) classifier algorithms that determined the feasibility of discriminating between individuals performing the activities. The research allows for future determination of abnormal motion in specific activities via micro-Doppler response and machine learning that can further emphasize the ability of micro-Doppler radar to perform abnormal gait classification.",
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Hall, DL, Ridder, TD & Narayanan, RM 2019, Abnormal gait detection and classification using micro-Doppler radar signatures. in KI Ranney & A Doerry (eds), Radar Sensor Technology XXIII., 110030Q, Proceedings of SPIE - The International Society for Optical Engineering, vol. 11003, SPIE, Radar Sensor Technology XXIII 2019, Baltimore, United States, 4/15/19. https://doi.org/10.1117/12.2519663

Abnormal gait detection and classification using micro-Doppler radar signatures. / Hall, Donald L.; Ridder, Tyler D.; Narayanan, Ram Mohan.

Radar Sensor Technology XXIII. ed. / Kenneth I. Ranney; Armin Doerry. SPIE, 2019. 110030Q (Proceedings of SPIE - The International Society for Optical Engineering; Vol. 11003).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Hall DL, Ridder TD, Narayanan RM. Abnormal gait detection and classification using micro-Doppler radar signatures. In Ranney KI, Doerry A, editors, Radar Sensor Technology XXIII. SPIE. 2019. 110030Q. (Proceedings of SPIE - The International Society for Optical Engineering). https://doi.org/10.1117/12.2519663