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Wearable Device-Based Gait Recognition
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Wearable Device-Based Gait Recognition

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Biomedical Sensors".

Deadline for manuscript submissions: closed (31 December 2022) | Viewed by 10366

Special Issue Editors

Prof. Dr. Jongshill Lee

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Guest Editor
Department of Biomedical Engineering, Hanyang University, Seoul 04763, Korea
Interests: wearable devices; rehabilitation (gait measurement and analysis); healthcare; biosignal monitoring; biometric authentication; intelligent soldier survivability; cardiovascular system; wearable sensors (IMU, PPG, ECG)
Prof. Park Jae Hyeon

E-Mail Website
Guest Editor
Department of Rehabilitation Medicine, Hanyang University Guri Hospital, Gyeonggi-do 11923, Korea
Interests: rehabilitation; wearable sensors; musculoskeletal disorders
Dr. Shin Joon-Ho

E-Mail Website
Guest Editor
Department of neurorehabilitation, National rehabilitation center, Ministry of Health and Welfare, Seoul, Korea
Interests: stroke rehabilitation; robotic rehabilitation; gait; kinematic data analysis
Prof. Dr. In Young Kim

E-Mail Website
Guest Editor
Department of Biomedical Engineering, Hanyang University, Seoul 04763, Republic of Korea
Interests: biomedical diagnostics; wearable electronics; biosensors; medical instrumentation

Special Issue Information

Dear Colleagues,

Recently, in gait recognition using wearable sensors, various motion sensors are being worn or attached to various parts of the human body. These sensors include accelerometers, gyroscopes, magnetoresistive sensors, force sensors, strain gauges, inclinometers, goniometers, sensing fabrics and so on. They can measure various characteristics of human gait. Based on these sensors, a single type of sensor system or a combination of several types of sensors can also be used in a variety of applications. Wearable devices using these sensors allow researchers to easily collect gait biomechanical data, whether indoors or outdoors, due to many advantageous factors such as small size, ease of installation, lightweight, portability, effectiveness and low cost. By analyzing biomechanical data obtained from wearable sensors, kinematic and kinematic parameters of human movement can be determined, and musculoskeletal function can be quantitatively evaluated. With the development of sensor technology and gait data analysis technology, gait recognition using wearable sensors has become a widespread and useful tool for both clinical practice and biomechanical research. Using effective wearable sensors, gait analysis can be conveniently used for applications such as sports, rehabilitation and clinical diagnosis. This Special Issue entitled “Wearable device-based gait recognition” aims to highlight the most recent research regarding sensors and their applications in gait recognition and analysis.

Prof. Dr. Jongshill Lee
Prof. Park Jae Hyeon
Dr. Shin Joon-Ho
Prof. Dr. In Young Kim
Guest Editors

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Keywords

  • Wearable sensors
  • Wearable gait measurement
  • Gait analysis and recognition
  • Biomechanical movement
  • Gait authentication and identification
  • Gait rehabilitation

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Published Papers (3 papers)

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Research

11 pages, 2689 KiB  
Article
Customized Textile Capacitive Insole Sensor for Center of Pressure Analysis
by Jong-Gab Ho, Young Kim and Se-Dong Min
Sensors 2022, 22(23), 9390; https://doi.org/10.3390/s22239390 - 1 Dec 2022
Cited by 9 | Viewed by 2681
Abstract
Center of pressure refers to the centroid of the ground reaction force vector detected underneath the walking foot, which is a summary measure representing body segment movements during human locomotion. In this study, we developed a cost-effective, lightweight insole-type textile capacitive sensor (I-TCPs) [...] Read more.
Center of pressure refers to the centroid of the ground reaction force vector detected underneath the walking foot, which is a summary measure representing body segment movements during human locomotion. In this study, we developed a cost-effective, lightweight insole-type textile capacitive sensor (I-TCPs) to analyze plantar pressure (PP) distribution and center of pressure (COP) trajectory. To test the accuracy of I-TCPs, the measured pressure data was compared with that of F-scan. The sensor performance test was divided into a static baseline test and a dynamic gait experiment, both at two different gait speeds self-selected by the subjects. Static gait results showed that I-TCPs were capable of recognizing PP segments at different gait speeds. Dynamic gait results showed an average RMSE of 1.29 ± 0.47 mm in COPx (mediolateral shift) and 12.55 ± 5.08 mm in COPy (anteroposterior shift) at a comfortable gait speed. The COP correlation between I-TCPs and F-scan was 0.54 ± 0.09 in COPx and 0.92 ± 0.04 in COPy in comfortable gait speed conditions, in which COPy values presented a stronger correlation. RMSE and correlation in fast gait speed conditions also showed similar results. The findings of this study can be the basis for future research, including rehabilitation engineering, developing ICT devices, and creating smart wearable sensors to improve quality of life for patients and healthy individuals. Full article
(This article belongs to the Special Issue Wearable Device-Based Gait Recognition)
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11 pages, 6735 KiB  
Article
Analysis of Gait Characteristics Using Hip-Knee Cyclograms in Patients with Hemiplegic Stroke
by Ho Seok Lee, Hokyoung Ryu, Shi-Uk Lee, Jae-sung Cho, Sungmin You, Jae Hyeon Park and Seong-Ho Jang
Sensors 2021, 21(22), 7685; https://doi.org/10.3390/s21227685 - 19 Nov 2021
Cited by 17 | Viewed by 4438
Abstract
Gait disturbance is a common sequela of stroke. Conventional gait analysis has limitations in simultaneously assessing multiple joints. Therefore, we investigated the gait characteristics in stroke patients using hip-knee cyclograms, which have the advantage of simultaneously visualizing the gait kinematics of multiple joints. [...] Read more.
Gait disturbance is a common sequela of stroke. Conventional gait analysis has limitations in simultaneously assessing multiple joints. Therefore, we investigated the gait characteristics in stroke patients using hip-knee cyclograms, which have the advantage of simultaneously visualizing the gait kinematics of multiple joints. Stroke patients (n = 47) were categorized into two groups according to stroke severity, and healthy controls (n = 32) were recruited. An inertial measurement unit sensor-based gait analysis system, which requires placing seven sensors on the dorsum of both feet, the shafts of both tibias, the middle of both femurs, and the lower abdomen, was used for the gait analysis. Then, the hip-knee cyclogram parameters (range of motion, perimeter, and area) were obtained from the collected data. The coefficient of variance of the cyclogram parameters was obtained to evaluate gait variability. The cyclogram parameters differed between the stroke patients and healthy controls, and differences according to stroke severity were also observed. The gait variability parameters mainly differed in patients with more severe stroke, and specific visualized gait patterns of stroke patients were obtained through cyclograms. In conclusion, the hip-knee cyclograms, which show inter-joint coordination and visualized gait cycle in stroke patients, are clinically significant. Full article
(This article belongs to the Special Issue Wearable Device-Based Gait Recognition)
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13 pages, 1789 KiB  
Article
Gait Disorder Detection and Classification Method Using Inertia Measurement Unit for Augmented Feedback Training in Wearable Devices
by Hyeonjong Kim, Ji-Won Kim and Junghyuk Ko
Sensors 2021, 21(22), 7676; https://doi.org/10.3390/s21227676 - 18 Nov 2021
Cited by 3 | Viewed by 2329
Abstract
Parkinson’s disease (PD) is a common neurodegenerative disease, one of the symptoms of which is a gait disorder, which decreases gait speed and cadence. Recently, augmented feedback training has been considered to achieve effective physical rehabilitation. Therefore, we have devised a numerical modeling [...] Read more.
Parkinson’s disease (PD) is a common neurodegenerative disease, one of the symptoms of which is a gait disorder, which decreases gait speed and cadence. Recently, augmented feedback training has been considered to achieve effective physical rehabilitation. Therefore, we have devised a numerical modeling process and algorithm for gait detection and classification (GDC) that actively utilizes augmented feedback training. The numerical model converted each joint angle into a magnitude of acceleration (MoA) and a Z-axis angular velocity (ZAV) parameter. Subsequently, we confirmed the validity of both the GDC numerical modeling and algorithm. As a result, a higher gait detection and classification rate (GDCR) could be observed at a higher gait speed and lower acceleration threshold (AT) and gyroscopic threshold (GT). However, the pattern of the GDCR was ambiguous if the patient was affected by a gait disorder compared to a normal user. To utilize the relationships between the GDCR, AT, GT, and gait speed, we controlled the GDCR by using AT and GT as inputs, which we found to be a reasonable methodology. Moreover, the GDC algorithm could distinguish between normal people and people who suffered from gait disorders. Consequently, the GDC method could be used for rehabilitation and gait evaluation. Full article
(This article belongs to the Special Issue Wearable Device-Based Gait Recognition)
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