A Deep Neural Network for Abnormal Gait Patterns Based on Inertial Sensors Among Post-Stroke Patients
Trial ID: NCT04968418
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2021-07-20
Completion date: 2023-05-31
Sponsor: Cheng-Hsin General Hospital
Collaborators: National Taiwan University
Conditions: Gait Disorders, Neurologic; Artificial Intelligence
Interventions: APDM OPAL system wearable IMU
Locations: Cheng Hsin General Hospital
Countries: Taiwan
Enrollment: 100
Age range: 20 Years - None
Sex: ALL

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.

Eligibility criteria:
Inclusion Criteria:

1. Age over 20 years old with first time stroke
2. And affected lower limb Brunnstrom stage III-V
3. Functional ambulation category VI
4. Participants should be able to walk on flat surface without assistance for 6 minutes
5. Mini-Mental State Examination (MMSE) should be over 25 and can comply to orders and cooperate with our study

Exclusion Criteria:

1. Severe central nervous system(CNS)/peripheral nervous system(PNS)neurological disorders apart from stroke
2. Patients with high risk of falling down during walking
3. Patients who cannot cooperate with testing
4. Patients with severe visual/auditory/cognition deficits
5. Patients with lower limb fracture within recent 6 months

Outcomes:
Deep neural network (DNN) model accuracy of detecting abnormal stroke gait patterns