A continuous-wave (CW) radar for gait analysis

Geisheimer, Jonathan L.; Marshall, William; Greneker, Eugene F.

doi:10.1109/acssc.2001.987041

Cited by 108 publications

(69 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The initial approach consisted of using the stationary Doppler spectrum of various movers for classification purposes. More recently, researchers started focusing on understanding the temporal changes in the Doppler spectrum via time-frequency analysis techniques (5)(6)(7)(8). Most of the work published so far in the literature is based on simplistic computer models of a moving human.…”

Section: Introductionmentioning

confidence: 99%

Time-Frequency Analysis of a Moving Human Doppler Signature

Dogaru¹,

Le²,

Kirose³

2009

View full text Add to dashboard Cite

Sensors and Electron Devices Directorate, ARLApproved for public release; distribution unlimited.ii REPORT DOCUMENTATION PAGE Public reporting burden for this collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing the collection information. Send comments regarding this burden estimate or any other aspect of this collection of information, including suggestions for reducing the burden, to Department of Defense, Washington Headquarters Services, Directorate for Information Operations and Reports (0704-0188), 1215 Jefferson Davis Highway, Suite 1204, Arlington, VA 22202-4302. Respondents should be aware that notwithstanding any other provision of law, no person shall be subject to any penalty for failing to comply with a collection of information if it does not display a currently valid OMB control number. PLEASE DO NOT RETURN YOUR FORM TO THE ABOVE ADDRESS. REPORT DATE (DD-MM-YYYY) February 20092. REPORT TYPE AUTHOR(S)Traian Dogaru, Calvin Le, and Getachew Kirose 5f. WORK UNIT NUMBER PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES) U.S. Army Research Laboratory ATTN: AMSRD-ARL-SE-RU 2800 Powder Mill Road Adelphi, MD 20783-1197 PERFORMING ORGANIZATION REPORT NUMBER ARL-TR-4728 SPONSOR/MONITOR'S ACRONYM(S) 9. SPONSORING/MONITORING AGENCY NAME(S) AND ADDRESS(ES) SPONSOR/MONITOR'S REPORT NUMBER(S) DISTRIBUTION/AVAILABILITY STATEMENTApproved for public release; distribution unlimited. SUPPLEMENTARY NOTES ABSTRACTIn this report we analyze the Doppler signature of a moving human with the purpose of finding temporal change patterns that would allow detection and classification of movers. The analysis is based on computer models that simulate the operation of a pulse-Doppler radar. We start with an animated human mesh in walking motion and compute the frame-by-frame radar return using electromagnetic solvers such as the Finite Difference Time Domain (FDTD). The results are processed in the timefrequency domain via Short-Time Fourier Transforms (STFT) and displayed as spectrograms. In the end, we try to distinguish patterns in these spectrograms that are characteristic to a certain human motion type. The analysis emphasizes the phenomenological aspects of the Doppler signature, by looking at the relative contribution of various body parts and the effect of frequency, aspect angle, polarization and other radar parameters.

show abstract

Section: Introductionmentioning

confidence: 99%

Time-Frequency Analysis of a Moving Human Doppler Signature

Dogaru¹,

Le²,

Kirose³

2009

View full text Add to dashboard Cite

show abstract

“…Human gait can used in identification of actions, gender, mood, emotions and intensions of a person. Gait requires no subject contact compared to other biometric technologies like fingerprint, iris detection, face recognition which requires the subject to be in close proximity with the sensor, therefore gait can be far more superior compared to other biometric technologies for example Geisheimer et al [1] used continuous wave radar developed to record human gait signatures and extracted various gait parameters using signal processing techniques like short time Fourier transform (STFT).…”

Section: Introductionmentioning

confidence: 99%

Human Gait Analysis and Recognition Using Support Vector Machines

Das¹,

Saharia²

2014

Computer Science &Amp; Information Technology ( CS &Amp; IT )

View full text Add to dashboard Cite

show abstract

“…CW microwave Doppler radars have been widely used for vital sign detection and life detection [12][13][14][15], and for extracting the Doppler signatures for biometric characterization [16]. Signal processing and Doppler extraction method for CW Doppler radar were also discussed in [4,[17][18][19][20][21][22].…”

Section: Introductionmentioning

confidence: 99%

Analysis of Moving Human Micro-Doppler Signature in Forest Environments

Garcia-Rubia¹,

Kıłıc²,

Dang³

et al. 2014

PIER

View full text Add to dashboard Cite

Abstract-Automatic detection of human motion is important for security and surveillance applications. Compared to other sensors, radar sensors present advantages for human motion detection and identification because of their all-weather and day-and-night capabilities, as well as the fact that they detect targets at a long range. This is particularly advantageous in the case of remote and highly cluttered radar scenes. The objective of this paper is to investigate human motion in highly cluttered forest medium to observe the characteristics of the received Doppler signature from the scene. For this purpose we attempt to develop an accurate model accounting for the key contributions to the Doppler signature for the human motion in a forest environment. Analytical techniques are combined with full wave numerical methods such as Method of Moments (MoM) enhanced with Fast Multipole Method (FMM) to achieve a realistic representation of the signature from the scene. Mutual interactions between the forest and the human as well as the attenuation due to the vegetation are accounted for. Due to the large problem size, parallel programming techniques that utilize a Graphics Processing Unit (GPU) based cluster are used.

show abstract

A continuous-wave (CW) radar for gait analysis

Cited by 108 publications

References 6 publications

Time-Frequency Analysis of a Moving Human Doppler Signature

Time-Frequency Analysis of a Moving Human Doppler Signature

Human Gait Analysis and Recognition Using Support Vector Machines

Analysis of Moving Human Micro-Doppler Signature in Forest Environments

Contact Info

Product

Resources

About