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Deep Neural Network for Stroke Patient Gait Analysis and Classification

NCT04968418 · Status: UNKNOWN · Type: OBSERVATIONAL · Enrollment: 100

Last updated 2022-03-09

No results posted yet for this study

Summary

Lower limbs of stroke patients gradually recover through Brunnstrom stages, from initial flaccid status to gradually increased spasticity, and eventually decreased spasticitiy. Throughout this process. after stroke patients can start walking, their gait will show abnormal gait patterns from healthy subjects, including circumduction gait, drop foot, hip hiking and genu recurvatum.

For these abnormal gait patterns, rehabilitation methods include ankle-knee orthosis(AFO) or increasing knee/pelvic joint mobility for assistance. Prior to this study, similar research has been done to differentiate stroke gait patterns from normal gait patterns, with an accuracy of over 96%.

This study recruits subject who has encountered first ever cerebrovascular incident and can currently walk independently on flat surface without assistance, and investigators record gait information via inertial measurement units strapped to their bilateral ankle, wrist and pelvis to detect acceleration and angular velocity as well as other gait parameters. The IMU used in this study consists of a 3-axis accelerometer, 3-axis gyroscope and 3-axis magnetometer, with a highest sampling rate of 128Hz.

Afterwards, investigators use these gait information collected as training data and testing data for a deep neural network (DNN) model and compare clinical observation results by physicians simultaneously, in order to determine whether the DNN model is able to differentiate the types of abnormal gait patterns mentioned above.

If this model is applied in the community, investigators hope it is available to early detect abnormal gait patterns and perform early intervention to decrease possibility of fallen injuries.

This is a non-invasive observational study and doesn't involve medicine use. Participants are only required to perform walking for 6 minutes without assistance on a flat surface. This risk is extremely low and the only possible risk of this study is falling down during walking.

Conditions

  • Gait Disorders, Neurologic
  • Artificial Intelligence

Interventions

DEVICE

APDM OPAL system wearable IMU

The OPAL system contains wearable IMUs with a sampling rate of 128 Hz and a resolution of 17.5 bits. Each IMU has a size of about 44mm 40mm 14mm × × and weighs less than 25 gm.

Sponsors & Collaborators

  • National Taiwan University

    collaborator OTHER

  • Cheng-Hsin General Hospital

    lead OTHER

Principal Investigators

  • Szu-Fu Chen, MD, PHD · Szu-Fu Chen

Eligibility

Min Age
20 Years
Sex
ALL
Healthy Volunteers
Yes

Timeline & Regulatory

Start
2021-07-20
Primary Completion
2023-05-01
Completion
2023-05-31

Countries

  • Taiwan

Study Locations

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT04968418 on ClinicalTrials.gov