Treadmill-to-Overground Mapping of Marker Trajectory for Treadmill-Based Continuous Gait Analysis

Jung, Woo Chang; Lee, Jung Keun

doi:10.3390/s21030786

Cited by 4 publications

(2 citation statements)

References 43 publications

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“…지금까지 바이오 메디컬 분야에서 수집 및 활용하고 있는 병리적 그리고 비병리적 보행 데이터는 주로 적외선 반사 마커를 사용하는 광학식 방식(OMC 방식)을 사용하여 측정하였다. 최근 Zhe Cao [1], 김종욱 [2], Yiqiao Lin [3] 등의 연구를 통해 기존 OMC 방식에 비해 간편하고 단순한 측정과 분석 등 여러 장점을 지니고 있는 관성센서 방식 (IMC 방식)과 다양한 마커리스 방식(MMC 방식)은 Kidziński [4], 정원교 [5], 김하용 [6] 3…”

Section: 서론unclassified

Machine Learning-based Markerless Measurement System for Gait Kinematics : Part 2 - Non-pathologic Gait and Statistics Database

Choi,

Han,

Kim

et al. 2024

APJCRI

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This study established a database on non-pathological gait kinematics (treadmill gait and overground gait) and conducted statistical analysis, using markerless motion capture technology (MMC method) verified through system introduction and verification. Data were collected in three ways, and the collected gait images were analyzed using the MMC method gait analysis system. The mean and standard deviations of gait parameters, such as cadence, shoulder and pelvic horizontal angle, and flexion/extension motions at shoulder, hip, knee, and ankle joints, were evaluated to be utilized as nonpathologic gait norm data of the markerless method. As a result of statistical analysis, the gait speed in non-pathological treadmill walking was found to have a significant effect on some gait kinematics, such as shoulder horizontal angle, shoulder joint angle, hip joint angle, and knee angle. And the gait type (treadmill gait and over ground gait) also affected some of the gait kinematics. As a case study, pathologic over-ground gait was compared with non-pathological gait, and differences were found in the shoulder and pelvis horizontal angle patterns and the timing of maximum flexion/extension of the shoulder joint and lower extremity joint. This database of non-pathologic gait would be valuable for the comparative assessment of pathological gaits, which was one of the significant purposes of general application to the biomedical and healthcare industries.

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Section: 서론unclassified

Machine Learning-based Markerless Measurement System for Gait Kinematics : Part 2 - Non-pathologic Gait and Statistics Database

Choi,

Han,

Kim

et al. 2024

APJCRI

View full text Add to dashboard Cite

show abstract

“…It was possible to walk continuously on a treadmill, but the MoCap data repeated in a limited space during treadmill walking overlap. As a result, most treadmill-based gait data are analyzed using gait cycle percentages [28].…”

Section: Literature Reviewmentioning

confidence: 99%

Analysis of Human Gait Cycle With Body Equilibrium Based on Leg Orientation

et al. 2022

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Gait analysis identifies the posture during movement in order to provide the correct actions for a normal gait. A person's gait may differ from others and can be recognized by specific patterns. Healthy individuals exhibit normal gait patterns, while lower limb amputees exhibit abnormal gait patterns. To better understand the pitfalls of gait, it is imperative to develop systems capable of capturing the gait patterns of healthy individuals. The main objective of this research was to introduce a new concept in gait analysis by computing the static and dynamic equilibrium in a real-world environment. A relationship was also presented among the parameters stated as static & dynamic equilibrium, speed, and body states. A sensing unit was installed on the designed metal-based leg mounting assembly on the lateral side of the leg. An algorithm was proposed based on two variables: the position of the leg in space and the angle of the knee joint measured by an IMU sensor and a rotary encoder. It was acceptable to satisfy the static conditions when the body was in a fixed position and orientation, whether lying down or standing. While walking and running, the orientation is determined by the position and knee angle variables, which fulfill the dynamic condition. High speed reveals a rapid change in orientation, while slow speed reveals a slow change in orientation. The proposed encoder-based feedback system successfully determined the flexion at 47 • , extension at 153 • , and all seven gait cycle phases were recognized within this range of motion. Body equilibrium facilitates individuals when they are at risk of falling or slipping.INDEX TERMS Gait analysis, IMU sensor, rotary encoder, static and dynamic equilibrium, body orientation.

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Sensor Combination Selection Strategy for Kayak Cycle Phase Segmentation Based on Body Sensor Networks

Qiu

Hao

Wang

et al. 2022

IEEE Internet Things J.

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Treadmill-to-Overground Mapping of Marker Trajectory for Treadmill-Based Continuous Gait Analysis

Cited by 4 publications

References 43 publications

Machine Learning-based Markerless Measurement System for Gait Kinematics : Part 2 - Non-pathologic Gait and Statistics Database

Machine Learning-based Markerless Measurement System for Gait Kinematics : Part 2 - Non-pathologic Gait and Statistics Database

Analysis of Human Gait Cycle With Body Equilibrium Based on Leg Orientation

Sensor Combination Selection Strategy for Kayak Cycle Phase Segmentation Based on Body Sensor Networks

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