Biometric Recognition and Rehabilitation Assessment of Lower Extremity Sports Injury Based on Gait Touch Information

NCT ID: NCT04462913

Last Updated: 2020-07-08

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

UNKNOWN

Total Enrollment

550 participants

Study Classification

OBSERVATIONAL

Study Start Date

2017-07-28

Study Completion Date

2022-12-30

Brief Summary

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The current common clinical methods cannot truly reflect the biomechanical status of the knee joint. Based on the foot-knee coupling mechanism, the simple and practical dynamic gait touch information provided by the 3D force platform are closely related to the knee biomechanics. The purpose of this study is to investigate the disease feature recognition, computer-aided diagnosis and rehabilitation assessment based on the gait touch information related to lower limb injuries.

Detailed Description

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Background:

The current common clinical methods cannot truly reflect the biomechanical status of the knee joint. The three-dimensional gait analysis is the gold standard, but it is difficult to apply clinically. There is an urgent need for a clinically practical method to quantitatively evaluate the biomechanics of the knee joint under dynamic weight bearing.

Methods:

50 healthy volunteers, 450 sports injuries patients (including hip, knee, and ankle joint diseases) and 50 patients with degenerative osteoarthritis were recruited.

55 passive reflective markers were placed bilaterally on the body. Lower extremity kinematics and dynamic plantar pressure during walking, jogging were collected.

Outcome evaluation indicators and statistical methods: The following indicators use repeated measurement two-factor analysis of variance: the left and right sides, different rehabilitation times are used as repeated measurement variables, to analyze the biomechanical changes of the lower limb joint biomechanics and gait touch information. A variety of machine learning methods (such as PCA, SVM, CNN, etc.) are used to analyze, and select the appropriate algorithm and parameters according to the learning effect. Finally, this study will establish a machine learning models for computer-aided diagnosis, treatment, and rehabilitation assessment.

Conditions

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Sport Injury Osteoarthritis, Knee

Study Design

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Observational Model Type

CASE_CONTROL

Study Time Perspective

CROSS_SECTIONAL

Study Groups

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Healthy control

According to the previous clinical diagnosis, volunteers who has never suffered the lower extremity sports injuries.

no intervention

Intervention Type OTHER

This is an observation study, with no intervention

Patients with sports injuries

According to the previous clinical diagnosis, patients who has suffered the sports injuries(including hip, knee, and ankle joint diseases).

no intervention

Intervention Type OTHER

This is an observation study, with no intervention

Patients with degenerative osteoarthritis

According to the previous clinical diagnosis, patients who has suffered the degenerative osteoarthritis.

no intervention

Intervention Type OTHER

This is an observation study, with no intervention

Interventions

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no intervention

This is an observation study, with no intervention

Intervention Type OTHER

Eligibility Criteria

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Inclusion Criteria

* patients with a certain sports injury (soft tissue injury or degenerative osteoarthritis) of a joint of the lower limb (hip or knee or ankle or foot).

Exclusion Criteria

* Cognitive impairment
* other injuries affecting movement performance.
Eligible Sex

ALL

Accepts Healthy Volunteers

Yes

Sponsors

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Peking University Third Hospital

OTHER

Sponsor Role lead

Responsible Party

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Responsibility Role SPONSOR

Principal Investigators

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Hongshi Huang, Doctor

Role: STUDY_CHAIR

Peking University Third Hospital

Locations

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Peking University Third Hospital

Beijing, , China

Site Status RECRUITING

Countries

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China

Central Contacts

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Hongshi Huang, Doctor

Role: CONTACT

+8613910093298

Facility Contacts

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Hongshi Huang, Doctor

Role: primary

+8613910093298

Other Identifiers

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OR001

Identifier Type: -

Identifier Source: org_study_id

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