Degenerative Lumbar Spine Diseases
Conditions
Keywords
degenerative lumbar spine disease, virtual reality, telehealth, real-time detection
Brief summary
This study will combine virtual reality (VR) technology with machine learning to focus on functional movement. Patients' symptoms will be evaluated, and exercise instruction with real-time feedback will be provided. The goals of this research are to: (1) develop a waist digital sensor for real-time monitoring as an evaluation tool, (2) apply a real-time monitoring system in conjunction with virtual reality for telerehabilitation, and (3) develop the standard model.
Detailed description
The remote assessment and training in this study can assist patients obtaining physical therapy care at home. It is suitable for patients who avoid going to the hospital due to the epidemic, and solve the problem of long-term treatment. The outcomes will provide DLSD patients tele-rehabilitation platform and system, help medical personnel to treat patients more effectively in the future, and reduce the burden of medical costs on close ones.
Interventions
OTHERCore muscle training
Combining virtual reality technology and waist wearable sensors, training the pelvis and lumbar spine movements as the main training goal, it is estimated that two 1-hour training sessions per week, a total of 6 weeks of training, to improve the core muscle of patients with lumbar degenerative diseases muscle strength.
Sponsors
National Taiwan University Hospital
Study design
Allocation
NA
Intervention model
SINGLE_GROUP
Primary purpose
TREATMENT
Masking
NONE
Eligibility
Sex/Gender
ALL
Age
20 Years to 80 Years
Healthy volunteers
Yes
Inclusion criteria
1. able to stand and walk for 5 minutes independently 2. aged between 50 and 80 years 3. received a diagnosis of DLSD based on imaging
Exclusion criteria
1. neurological disorder such as stroke or spinal cord injury 2. metabolic disease such as diabetes mellitus 3. vestibular disease such as Meniere's disease.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Kinematic variables: Center of mass | 6 months | A motion capture system is used to measure the displacement of the center of mass. The data is used to calculate the displacement of the center of mass (unit: cm). |
| Kinetic variables | 6 months | A motion capture system is used to measure the joint kinetics. The data is used to calculate joint moments (unit: Nm) |
| Functional assessment: Walking speed | 6 months | Functional assessment is a process that allows for the identification of disability. The data from the functional assessment is used to calculate walking speed (unit: m/s). |
| Functional assessment: Walking distance | 6 months | Functional assessment is a process that allows for the identification of disability. The data from the functional assessment is used to calculate walking distance (unit: m). |
| Functional assessment: Duration | 6 months | Functional assessment is a process that allows for the identification of disability. The data from the functional assessment is used to calculate calculate the duration it took to complete the test (unit: s). |
| Postural balance | 6 months | he postural balance measurements are performed by using a inertial sensor. The data from the inertial sensor is used to calculate the 95% confidence ellipse area (unit: cm\^2) |
| Kinematic variables: Joint angles | 6 months | A motion capture system is used to measure the joint kinematics. The data is used to calculate joint angles (unit: degree). |
| Muscle activities | 6 months | Electromyography is a technique for evaluating and recording muscle activity. The data from electromyography is used to calculate a normalized value (unit: ratio). |
Countries
Taiwan
Outcome results
None listed