NCT04968418

Deep Neural Network for Stroke Patient Gait Analysis and Classification

Status unknown Last updated 9 March 2022

What this trial tests

trial testing APDM OPAL system wearable IMU in Gait Disorders, Neurologic in 100 participants. Status unknown.

Timeline

20 July 2021

Primary endpoint
1 May 2023

31 May 2023

Quick facts

Lead sponsor	Cheng-Hsin General Hospital
Status	Status unknown
Study type	OBSERVATIONAL
Enrollment	100
Start date	20 July 2021
Primary completion	1 May 2023
Estimated completion	31 May 2023
Sites	1 location across Taiwan

Drugs / interventions tested

• APDM OPAL system wearable IMU

Conditions studied

• Gait Disorders, Neurologic — all drugs for Gait Disorders, Neurologic →
• Artificial Intelligence — all drugs for Artificial Intelligence →

Sponsor

Cheng-Hsin General Hospital

Who can join

20 and older, any sex, with Gait Disorders, Neurologic or Artificial Intelligence. Patients with the condition only — healthy volunteers not accepted.

Sponsor's own description

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.

Publications & conference data

1 peer-reviewed publication reference this trial (live from Europe PMC):

1. Performance Evaluation for Clinical Stroke Rehabilitation via an Automatic Mobile Gait Trainer.
   Shih CJ, Li YC, Yuan W, Chen SF, et al · · 2023 · cited 2× · PMID 37571574 · DOI 10.3390/s23156793