Doppler Radar System for an Automatic Transfer Phase Detection Using Wavelet Transform Analysis

Soubra, Racha; Alshamaa, Daniel; Chkeir, Aly; Dauriac, Boris; Mourad-Chehade, Farah; Duchêne, Jacques

doi:10.1109/biosmart.2019.8734163

Cited by 2 publications

(3 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…T HS = t Hmin2 (14) In Equation 14, t Hmin2 is the moment when the heel displacement signals rise sharply to first trough.…”

Section: Determination Of Time Of Heel Off (Ho) Eventmentioning

confidence: 99%

“…Gait information can be acquired by collecting kinematics signals, bioelectrical signals, videos and images [7][8][9][10][11][12][13]. In traditional gait analysis methods, a three-dimensional motion capture (3D Mo-Cap) system and force plate pressure signal can accurately describe the 3D motion of the human body, while can accurately detect the gait event with error of 0.13% [14], which is often called the golden standard method in gait analysis [15][16][17][18]. However, the cost of Mo-Cap system equipment and the force measuring board have a high cost, and there are limitations of areas such as a laboratory environment for data acquisition, while it is also unable to synchronously detect human daily activities.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Determination Method for Gait Event Based on Acceleration Sensors

Mei

Gao

Liu

2019

Sensors

View full text Add to dashboard Cite

A gait event is a crucial step towards the effective assessment and rehabilitation of motor dysfunctions. However, for the data acquisition of a three-dimensional motion capture (3D Mo-Cap) system, the high cost of setups, such as the high standard laboratory environment, limits widespread clinical application. Inertial sensors are increasingly being used to recognize and classify physical activities in a variety of applications. Inertial sensors are now sufficiently small in size and light in weight to be part of a body sensor network for the collection of human gait data. The acceleration signal has found important applications in human gait recognition. In this paper, using the experimental data from the heel and toe, first the wavelet method was used to remove noise from the acceleration signal, then, based on the threshold of comprehensive change rate of the acceleration signal, the signal was primarily segmented. Subsequently, the vertical acceleration signals, from heel and toe, were integrated twice, to compute their respective vertical displacement. Four gait events were determined in the segmented signal, based on the characteristics of the vertical displacement of heel and toe. The results indicated that the gait events were consistent with the synchronous record of the motion capture system. The method has achieved gait event subdivision, while it has also ensured the accuracy of the defined gait events. The work acts as a valuable reference, to further study gait recognition.

show abstract

“…T HS = t Hmin2 (14) In Equation 14, t Hmin2 is the moment when the heel displacement signals rise sharply to first trough.…”

Section: Determination Of Time Of Heel Off (Ho) Eventmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Determination Method for Gait Event Based on Acceleration Sensors

Mei

Gao

Liu

2019

Sensors

View full text Add to dashboard Cite

show abstract

“…Applying the DARC algorithm on the variance signal ensured the lowest possible error rate equal to 0.13 seconds. Figure 12(a) shows an example of an extracted gait speed signal, (b) the variance signal extracted from the wavelet coefficient matrix with (c) its cumulative data signal, and the automatic detection of the turning phase [ 65 ].…”

Section: Resultsmentioning

confidence: 99%

Automation of the Timed Up and Go Test Using a Doppler Radar System for Gait and Balance Analysis in Elderly People

Soubra

Mourad-Chehade

Chkeir

2023

Journal of Healthcare Engineering

Self Cite

View full text Add to dashboard Cite

The timed up and go (TUG) test is a simple, valid, and reliable clinical tool that is widely used to assess mobility in elderly people. Several research studies have been conducted to automate the TUG test using wearable sensors or motion-tracking systems. Despite their promising results, the adopted technological systems present inconveniences in terms of acceptability and privacy protection. In this work, we propose to overcome these problems by using a Doppler radar system set into the backrest of a chair in order to automate the TUG test and extract additional information from its phases (i.e., transfer, walk, and turn). We intend to segment its phases and extract spatiotemporal gait parameters automatically. Our methodology is mainly based on a multiresolution analysis of radar signals. We proposed a segmentation technique based on the extraction of limbs oscillations signals through a semisupervised machine learning approach, on the one hand, and the application of the DARC algorithm on the other hand. Once the speed signals of torso and limbs oscillations were detected, we suggested estimating 14 gait parameters. All our approaches were validated by comparing outcomes to those obtained from a reference Vicon system. High correlation coefficients were obtained by comparing the speed signals of the torso ρ = 0.8 , the speed signals of limbs oscillations ρ = 0.91 , the initial and final indices of TUG phases ρ = 0.95 , and the extracted parameters p e r c e n t a g e e r r o r < 4.8 obtained after radar signal processing to those obtained from the Vicon system.

show abstract

Doppler Radar System for an Automatic Transfer Phase Detection Using Wavelet Transform Analysis

Cited by 2 publications

References 12 publications

A Determination Method for Gait Event Based on Acceleration Sensors

A Determination Method for Gait Event Based on Acceleration Sensors

Automation of the Timed Up and Go Test Using a Doppler Radar System for Gait and Balance Analysis in Elderly People

Contact Info

Product

Resources

About