Faculty of physical therapy, Kafrelsheik university
Cairo, Egypt
NCT Number: NCT05291377
conduct machine learning models to identify different aspects that can give us an impression about the disability in patients with neck pain.
By using 17 different classifier and regressor models. to identify disability from emg, pain, ROM and curve measurements
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Notify Me30 year–55 year
All sexes
Observational
Cairo, Egypt
ninety patients from both sexes suffering from mechanical neck dysfunction were participated in this study.
Patients were participated in the study if they fulfilled the following criteria
Exclusion criteria
Activation pattern of the examined ms was recorded and analyzed using electromyography as follow:
Skin preparation67 After history taking and physical examination, subjects were allowed to rest for 10 minutes for acclimatization. During this period each subject was prepared for the experimental set as follow;
Electrodes positions10:
The electrodes sites were located on each subject's dominant side as follows:
levator scapulae:was centered lateral to the C3-4 spinous process between the posterior margin of the sternocleidomastoid and the anterior margins of the upper trapezius Upper trapezius: 2 cm lateral to the midpoint of a line drawn between C7 spinous process and the posterolateral acromion Reference electrode: was situated over the C7 spinous process. The sites of electrodes placement were determined using a marker and a tape measurement and confirmed through palpation and manual resistance.
Only the study team can determine whether someone qualifies for participation.
Inclusion criteria
Exclusion criteria
Time frame: 2 weeks
using logistic regression, we can get this outcome
Time frame: 2 weeks
using logistic regression, we can get this outcome
Kafrelsheikh University
Other
Machine Learning Models for Identifying Disability in Neck Pain Patients Using Muscle and Joint Parameters
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