Toward Unobtrusive In-Home Gait Analysis Based on Radar Micro-Doppler Signatures

• Published in: IEEE transactions on biomedical engineering Vol. 66; no. 9; pp. 2629 - 2640
• Main Authors: Seifert, Ann-Kathrin, Amin, Moeness G., Zoubir, Abdelhak M.
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.09.2019; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Accuracy; Adult; Assisted living; biomedical monitoring; Classification; Diagnostic software; Diagnostic systems; Doppler Effect; Doppler radar; Feature recognition; Female; Fourier transforms; Gait; Gait - physiology; Gait Analysis - methods; Gait Analysis - standards; Gait Disorders, Neurologic - diagnosis; Gait recognition; Humans; Legged locomotion; Male; Monitoring; Monitoring, Ambulatory; New technology; Pattern Recognition, Automated; Radar; Radar data; radar signal processing; Radar signatures; Rehabilitation; Security; Sensors; Signal Processing, Computer-Assisted; Spectrogram; Time-frequency analysis; Walking; Young Adult
• ISSN: 0018-9294; 1558-2531; 1558-2531
• DOI: 10.1109/TBME.2019.2893528

Objective: In this paper, we demonstrate the applicability of radar for gait classification with application to home security, medical diagnosis, rehabilitation, and assisted living. Aiming at identifying changes in gait patterns based on radar micro-Doppler signatures, this paper is concerned with solving the intra motion category classification problem of gait recognition. Methods: New gait classification approaches utilizing physical features, subspace features, and sum-of-harmonics modeling are presented and their performances are evaluated using experimental K-band radar data of four test subjects. Five different gait classes are considered for each person, including normal, pathological, and assisted walks. Results: The proposed approaches are shown to outperform existing methods for radar-based gait recognition, which utilize physical features from the cadence-velocity data representation domain as in this paper. The analyzed gait classes are correctly identified with an average accuracy of 93.8%, where a classification rate of 98.5% is achieved for a single gait class. When applied to new data of another individual, a classification accuracy on the order of 80% can be expected. Conclusion: Radar micro-Doppler signatures and their Fourier transforms are well suited to capture changes in gait. Five different walking styles are recognized with high accuracy. Significance: Radar-based sensing of gait is an emerging technology with multi-faceted applications in security and health care industries. We show that radar, as a contact-less sensing technology, can supplement existing gait diagnostic tools with respect to long-term monitoring and reproducibility of the examinations.