Clinical trial NCT04462913: Biometric Recognition and Rehabilitation Assessment of Lower Extremity Sports Injury Based on Gait Touch Information
Official title: Biometric Recognition and Rehabilitation Assessment of Lower Extremity Sports Injury Based on Gait Touch Information
Study type: OBSERVATIONAL
Overall status: UNKNOWN
Conditions: Sport Injury
Osteoarthritis, Knee
Keywords: sports injury
gait touch information
machine learning
Lead sponsor: Peking University Third Hospital
Sponsor class: OTHER
First posted: 2020-07-08
Enrollment: 550
Sex: ALL
Standard ages: CHILD
ADULT
OLDER_ADULT
Accepts healthy volunteers: yes
Observational model: CASE_CONTROL
Time perspective: CROSS_SECTIONAL
Sampling method: NON_PROBABILITY_SAMPLE
Study population: Inclusion: one healthy control group: healthy volunteers; and 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: Cognitive impairment or other injuries affecting movement performance.

Brief summary: 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: 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.

Eligibility criteria: 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.

Primary outcomes:
measure: walking speed
timeFrame: On the day of enrollment.
measure: ground reaction force
timeFrame: On the day of enrollment.
measure: knee flexion angle
timeFrame: On the day of enrollment.
measure: the moment of knee extension in the gait cycle
timeFrame: On the day of enrollment.
Secondary outcomes:
measure: The International Knee Documentation Committee (IKDC) score
timeFrame: On the day of enrollment.
Other outcomes:
Locations:
city: Beijing
country: China
facility: Peking University Third Hospital
Study officials:
affiliation: Peking University Third Hospital
name: Hongshi Huang, Doctor
role: STUDY_CHAIR
References: